From 0b754ffa91b9496bdd2f892cf3ca2bd887028dea Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Tue, 27 Jul 2021 18:22:01 +0200 Subject: Fix dimethenamid vignette problems and update docs --- docs/dev/404.html | 2 +- docs/dev/articles/index.html | 4 +- docs/dev/articles/web_only/dimethenamid_2018.html | 420 +++++++++++ .../accessible-code-block-0.0.1/empty-anchor.js | 15 + .../figure-html/f_parent_mkin_dfop_const-1.png | Bin 0 -> 145534 bytes .../f_parent_mkin_dfop_const_test-1.png | Bin 0 -> 146181 bytes .../figure-html/f_parent_mkin_dfop_tc_test-1.png | Bin 0 -> 150274 bytes .../figure-html/f_parent_mkin_sfo_const-1.png | Bin 0 -> 141499 bytes .../f_parent_nlmixr_saem_dfop_const-1.png | Bin 0 -> 180061 bytes .../figure-html/f_parent_nlmixr_saem_dfop_tc-1.png | Bin 0 -> 151832 bytes .../f_parent_nlmixr_saem_sfo_const-1.png | Bin 0 -> 124946 bytes .../figure-html/f_parent_nlmixr_saem_sfo_tc-1.png | Bin 0 -> 140491 bytes .../figure-html/f_parent_saemix_dfop_const-1.png | Bin 0 -> 58759 bytes .../f_parent_saemix_dfop_tc_moreiter-1.png | Bin 0 -> 45420 bytes .../figure-html/f_parent_saemix_sfo_const-1.png | Bin 0 -> 59913 bytes .../figure-html/f_parent_saemix_sfo_tc-1.png | Bin 0 -> 53550 bytes .../figure-html/plot_parent_nlme-1.png | Bin 0 -> 147409 bytes .../header-attrs-2.9/header-attrs.js | 12 + docs/dev/authors.html | 2 +- docs/dev/index.html | 2 +- docs/dev/news/index.html | 2 +- docs/dev/pkgdown.yml | 3 +- docs/dev/reference/Rplot005.png | Bin 59049 -> 59600 bytes docs/dev/reference/dimethenamid_2018-2.png | Bin 0 -> 245108 bytes docs/dev/reference/dimethenamid_2018.html | 217 +++++- docs/dev/reference/endpoints.html | 2 +- docs/dev/reference/index.html | 2 +- docs/dev/reference/mean_degparms.html | 2 +- docs/dev/reference/mkinmod.html | 12 +- docs/dev/reference/nlme-1.png | Bin 68943 -> 69667 bytes docs/dev/reference/nlme-2.png | Bin 94409 -> 93394 bytes docs/dev/reference/nlme.html | 18 +- docs/dev/reference/nlme.mmkin.html | 9 +- docs/dev/reference/nlmixr.mmkin.html | 28 +- docs/dev/reference/plot.mixed.mmkin-3.png | Bin 173260 -> 173794 bytes docs/dev/reference/plot.mixed.mmkin-4.png | Bin 176346 -> 176972 bytes docs/dev/reference/plot.mixed.mmkin.html | 6 +- docs/dev/reference/reexports.html | 2 +- docs/dev/reference/saem-5.png | Bin 174406 -> 174405 bytes docs/dev/reference/saem.html | 480 ++++++------- docs/dev/reference/summary.nlmixr.mmkin.html | 792 ++------------------- docs/dev/reference/tffm0.html | 2 +- docs/dev/sitemap.xml | 3 + vignettes/web_only/dimethenamid_2018.html | 109 ++- vignettes/web_only/dimethenamid_2018.rmd | 57 +- 45 files changed, 1131 insertions(+), 1072 deletions(-) create mode 100644 docs/dev/articles/web_only/dimethenamid_2018.html create mode 100644 docs/dev/articles/web_only/dimethenamid_2018_files/accessible-code-block-0.0.1/empty-anchor.js create mode 100644 docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const-1.png create mode 100644 docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const_test-1.png create mode 100644 docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_tc_test-1.png create mode 100644 docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_sfo_const-1.png create mode 100644 docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_const-1.png create mode 100644 docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_tc-1.png create mode 100644 docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_sfo_const-1.png create mode 100644 docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_sfo_tc-1.png create mode 100644 docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_const-1.png create mode 100644 docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc_moreiter-1.png create mode 100644 docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_sfo_const-1.png create mode 100644 docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_sfo_tc-1.png create mode 100644 docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png create mode 100644 docs/dev/articles/web_only/dimethenamid_2018_files/header-attrs-2.9/header-attrs.js create mode 100644 docs/dev/reference/dimethenamid_2018-2.png diff --git a/docs/dev/404.html b/docs/dev/404.html index 98c0b1e0..38898979 100644 --- a/docs/dev/404.html +++ b/docs/dev/404.html @@ -71,7 +71,7 @@ mkin - 1.0.5 + 1.1.0 diff --git a/docs/dev/articles/index.html b/docs/dev/articles/index.html index 3896120a..c0338df8 100644 --- a/docs/dev/articles/index.html +++ b/docs/dev/articles/index.html @@ -71,7 +71,7 @@ mkin - 1.0.5 + 1.1.0 @@ -161,6 +161,8 @@
Performance benefit by using compiled model definitions in mkin
+
Example evaluations of the dimethenamid data from 2018
+
diff --git a/docs/dev/articles/web_only/dimethenamid_2018.html b/docs/dev/articles/web_only/dimethenamid_2018.html new file mode 100644 index 00000000..7648f75a --- /dev/null +++ b/docs/dev/articles/web_only/dimethenamid_2018.html @@ -0,0 +1,420 @@ + + + + + + + +Example evaluations of the dimethenamid data from 2018 • mkin + + + + + + + + + + + +
+
+ + + + +
+
+
+

Example evaluations of the dimethenamid data from 2018

+

Johannes Ranke

+ +

Last change 27 July 2021, built on 27 Jul 2021

+ + Source: vignettes/web_only/dimethenamid_2018.rmd + + +
+ + + +

Wissenschaftlicher Berater, Kronacher Str. 12, 79639 Grenzach-Wyhlen, Germany
Privatdozent at the University of Bremen

+
+

+Introduction

+

During the preparation of the journal article on nonlinear mixed-effects models in degradation kinetics (submitted) and the analysis of the dimethenamid degradation data analysed therein, a need for a more detailed analysis using not only nlme and saemix, but also nlmixr for fitting the mixed-effects models was identified.

+

This vignette is an attempt to satisfy this need.

+
+
+

+Data

+

Residue data forming the basis for the endpoints derived in the conclusion on the peer review of the pesticide risk assessment of dimethenamid-P published by the European Food Safety Authority (EFSA) in 2018 (EFSA 2018) were transcribed from the risk assessment report (Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria 2018) which can be downloaded from the EFSA register of questions.

+

The data are available in the mkin package. The following code (hidden by default, please use the button to the right to show it) treats the data available for the racemic mixture dimethenamid (DMTA) and its enantiomer dimethenamid-P (DMTAP) in the same way, as no difference between their degradation behaviour was identified in the EU risk assessment. The observation times of each dataset are multiplied with the corresponding normalisation factor also available in the dataset, in order to make it possible to describe all datasets with a single set of parameters.

+

Also, datasets observed in the same soil are merged, resulting in dimethenamid (DMTA) data from six soils.

+
+library(mkin)
+dmta_ds <- lapply(1:8, function(i) {
+  ds_i <- dimethenamid_2018$ds[[i]]$data
+  ds_i[ds_i$name == "DMTAP", "name"] <-  "DMTA"
+  ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i]
+  ds_i
+})
+names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title)
+dmta_ds[["Borstel"]] <- rbind(dmta_ds[["Borstel 1"]], dmta_ds[["Borstel 2"]])
+dmta_ds[["Borstel 1"]] <- NULL
+dmta_ds[["Borstel 2"]] <- NULL
+dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]])
+dmta_ds[["Elliot 1"]] <- NULL
+dmta_ds[["Elliot 2"]] <- NULL
+
+
+

+Parent degradation

+

We evaluate the observed degradation of the parent compound using simple exponential decline (SFO) and biexponential decline (DFOP), using constant variance (const) and a two-component variance (tc) as error models.

+
+

+Separate evaluations

+

As a first step, to get a visual impression of the fit of the different models, we do separate evaluations for each soil using the mmkin function from the mkin package:

+
+f_parent_mkin_const <- mmkin(c("SFO", "DFOP"), dmta_ds,
+  error_model = "const", quiet = TRUE)
+f_parent_mkin_tc <- mmkin(c("SFO", "DFOP"), dmta_ds,
+  error_model = "tc", quiet = TRUE)
+

The plot of the individual SFO fits shown below suggests that at least in some datasets the degradation slows down towards later time points, and that the scatter of the residuals error is smaller for smaller values (panel to the right):

+
+plot(mixed(f_parent_mkin_const["SFO", ]))
+

+

Using biexponential decline (DFOP) results in a slightly more random scatter of the residuals:

+
+plot(mixed(f_parent_mkin_const["DFOP", ]))
+

+

The population curve (bold line) in the above plot results from taking the mean of the individual transformed parameters, i.e. of log k1 and log k2, as well as of the logit of the g parameter of the DFOP model). Here, this procedure does not result in parameters that represent the degradation well, because in some datasets the fitted value for k2 is extremely close to zero, leading to a log k2 value that dominates the average. This is alleviated if only rate constants that pass the t-test for significant difference from zero (on the untransformed scale) are considered in the averaging:

+
+plot(mixed(f_parent_mkin_const["DFOP", ]), test_log_parms = TRUE)
+

+

While this is visually much more satisfactory, such an average procedure could introduce a bias, as not all results from the individual fits enter the population curve with the same weight. This is where nonlinear mixed-effects models can help out by treating all datasets with equally by fitting a parameter distribution model together with the degradation model and the error model (see below).

+

The remaining trend of the residuals to be higher for higher predicted residues is reduced by using the two-component error model:

+
+plot(mixed(f_parent_mkin_tc["DFOP", ]), test_log_parms = TRUE)
+

+
+
+

+Nonlinear mixed-effects models

+

Instead of taking a model selection decision for each of the individual fits, we fit nonlinear mixed-effects models (using different fitting algorithms as implemented in different packages) and do model selection using all available data at the same time. In order to make sure that these decisions are not unduly influenced by the type of algorithm used, by implementation details or by the use of wrong control parameters, we compare the model selection results obtained with different R packages, with different algorithms and checking control parameters.

+
+

+nlme

+

The nlme package was the first R extension providing facilities to fit nonlinear mixed-effects models. We use would like to do model selection from all four combinations of degradation models and error models based on the AIC. However, fitting the DFOP model with constant variance and using default control parameters results in an error, signalling that the maximum number of 50 iterations was reached, potentially indicating overparameterisation. However, the algorithm converges when the two-component error model is used in combination with the DFOP model. This can be explained by the fact that the smaller residues observed at later sampling times get more weight when using the two-component error model which will counteract the tendency of the algorithm to try parameter combinations unsuitable for fitting these data.

+
+library(nlme)
+f_parent_nlme_sfo_const <- nlme(f_parent_mkin_const["SFO", ])
+#f_parent_nlme_dfop_const <- nlme(f_parent_mkin_const["DFOP", ])
+# maxIter = 50 reached
+f_parent_nlme_sfo_tc <- nlme(f_parent_mkin_tc["SFO", ])
+f_parent_nlme_dfop_tc <- nlme(f_parent_mkin_tc["DFOP", ])
+

Note that overparameterisation is also indicated by warnings obtained when fitting SFO or DFOP with the two-component error model (‘false convergence’ in the ‘LME step’ in some iterations). In addition to these fits, attempts were also made to include correlations between random effects by using the log Cholesky parameterisation of the matrix specifying them. The code used for these attempts can be made visible below.

+
+f_parent_nlme_sfo_const_logchol <- nlme(f_parent_mkin_const["SFO", ],
+  random = pdLogChol(list(DMTA_0 ~ 1, log_k_DMTA ~ 1)))
+anova(f_parent_nlme_sfo_const, f_parent_nlme_sfo_const_logchol) # not better
+#f_parent_nlme_dfop_tc_logchol <- update(f_parent_nlme_dfop_tc,
+#  random = pdLogChol(list(DMTA_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)))
+# using log Cholesky parameterisation for random effects (nlme default) does
+# not converge here and gives lots of warnings about the LME step not converging
+

The model comparison function of the nlme package can directly be applied to these fits showing a similar goodness-of-fit of the SFO model, but a much lower AIC for the DFOP model fitted with the two-component error model. Also, the likelihood ratio test indicates that this difference is significant. as the p-value is below 0.0001.

+
+anova(
+  f_parent_nlme_sfo_const, f_parent_nlme_sfo_tc, f_parent_nlme_dfop_tc
+)
+
                        Model df    AIC    BIC  logLik   Test L.Ratio p-value
+f_parent_nlme_sfo_const     1  5 818.63 834.00 -404.31                       
+f_parent_nlme_sfo_tc        2  6 820.61 839.06 -404.31 1 vs 2   0.014  0.9049
+f_parent_nlme_dfop_tc       3 10 687.84 718.59 -333.92 2 vs 3 140.771  <.0001
+

The selected model (DFOP with two-component error) fitted to the data assuming no correlations between random effects is shown below.

+
+plot(f_parent_nlme_dfop_tc)
+

+
+
+

+saemix

+

The saemix package provided the first Open Source implementation of the Stochastic Approximation to the Expectation Maximisation (SAEM) algorithm. SAEM fits of degradation models can be performed using an interface to the saemix package available in current development versions of the mkin package.

+

The corresponding SAEM fits of the four combinations of degradation and error models are fitted below. As there is no convergence criterion implemented in the saemix package, the convergence plots need to be manually checked for every fit.

+

The convergence plot for the SFO model using constant variance is shown below.

+
+library(saemix)
+f_parent_saemix_sfo_const <- mkin::saem(f_parent_mkin_const["SFO", ], quiet = TRUE,
+  transformations = "saemix")
+plot(f_parent_saemix_sfo_const$so, plot.type = "convergence")
+

+

Obviously the default number of iterations is sufficient to reach convergence. This can also be said for the SFO fit using the two-component error model.

+
+f_parent_saemix_sfo_tc <- mkin::saem(f_parent_mkin_tc["SFO", ], quiet = TRUE,
+  transformations = "saemix")
+plot(f_parent_saemix_sfo_tc$so, plot.type = "convergence")
+

+

When fitting the DFOP model with constant variance, parameter convergence is not as unambiguous (see the failure of nlme with the default number of iterations above). Therefore, the number of iterations in the first phase of the algorithm was increased, leading to visually satisfying convergence.

+
+f_parent_saemix_dfop_const <- mkin::saem(f_parent_mkin_const["DFOP", ], quiet = TRUE,
+  control = saemixControl(nbiter.saemix = c(800, 200), print = FALSE,
+    save = FALSE, save.graphs = FALSE, displayProgress = FALSE),
+  transformations = "saemix")
+plot(f_parent_saemix_dfop_const$so, plot.type = "convergence")
+

+

The same applies to the case where the DFOP model is fitted with the two-component error model. Convergence of the variance of k2 is enhanced by using the two-component error, it remains more or less stable already after 200 iterations of the first phase.

+
+f_parent_saemix_dfop_tc_moreiter <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE,
+  control = saemixControl(nbiter.saemix = c(800, 200), print = FALSE,
+    save = FALSE, save.graphs = FALSE, displayProgress = FALSE),
+  transformations = "saemix")
+plot(f_parent_saemix_dfop_tc_moreiter$so, plot.type = "convergence")
+

+

The four combinations can be compared using the model comparison function from the saemix package:

+
+compare.saemix(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so,
+  f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc_moreiter$so)
+
Likelihoods calculated by importance sampling
+
     AIC    BIC
+1 818.37 817.33
+2 820.38 819.14
+3 725.91 724.04
+4 683.64 681.55
+

As in the case of nlme fits, the DFOP model fitted with two-component error (number 4) gives the lowest AIC. The numeric values are reasonably close to the ones obtained using nlme, considering that the algorithms for fitting the model and for the likelihood calculation are quite different.

+

In order to check the influence of the likelihood calculation algorithms implemented in saemix, the likelihood from Gaussian quadrature is added to the best fit, and the AIC values obtained from the three methods are compared.

+
+f_parent_saemix_dfop_tc_moreiter$so <-
+  llgq.saemix(f_parent_saemix_dfop_tc_moreiter$so)
+AIC(f_parent_saemix_dfop_tc_moreiter$so)
+
[1] 683.64
+
+AIC(f_parent_saemix_dfop_tc_moreiter$so, method = "gq")
+
[1] 683.7
+
+AIC(f_parent_saemix_dfop_tc_moreiter$so, method = "lin")
+
[1] 683.17
+

The AIC values based on importance sampling and Gaussian quadrature are quite similar. Using linearisation is less accurate, but still gives a similar value.

+
+
+

+nlmixr

+

In the last years, a lot of effort has been put into the nlmixr package which is designed for pharmacokinetics, where nonlinear mixed-effects models are routinely used, but which can also be used for related data like chemical degradation data. A current development branch of the mkin package provides an interface between mkin and nlmixr. Here, we check if we get equivalent results when using a refined version of the First Order Conditional Estimation (FOCE) algorithm used in nlme, namely First Order Conditional Estimation with Interaction (FOCEI), and the SAEM algorithm as implemented in nlmixr.

+

First, the focei algorithm is used for the four model combinations and the goodness of fit of the results is compared.

+
+library(nlmixr)
+f_parent_nlmixr_focei_sfo_const <- nlmixr(f_parent_mkin_const["SFO", ], est = "focei")
+f_parent_nlmixr_focei_sfo_tc <- nlmixr(f_parent_mkin_tc["SFO", ], est = "focei")
+f_parent_nlmixr_focei_dfop_const <- nlmixr(f_parent_mkin_const["DFOP", ], est = "focei")
+f_parent_nlmixr_focei_dfop_tc<- nlmixr(f_parent_mkin_tc["DFOP", ], est = "focei")
+
+AIC(f_parent_nlmixr_focei_sfo_const$nm, f_parent_nlmixr_focei_sfo_tc$nm,
+  f_parent_nlmixr_focei_dfop_const$nm, f_parent_nlmixr_focei_dfop_tc$nm)
+
                                    df    AIC
+f_parent_nlmixr_focei_sfo_const$nm   5 818.63
+f_parent_nlmixr_focei_sfo_tc$nm      6 820.61
+f_parent_nlmixr_focei_dfop_const$nm  9 728.11
+f_parent_nlmixr_focei_dfop_tc$nm    10 687.82
+

The AIC values are very close to the ones obtained with nlme which are repeated below for convenience.

+
+AIC(
+  f_parent_nlme_sfo_const, f_parent_nlme_sfo_tc, f_parent_nlme_dfop_tc
+)
+
                        df    AIC
+f_parent_nlme_sfo_const  5 818.63
+f_parent_nlme_sfo_tc     6 820.61
+f_parent_nlme_dfop_tc   10 687.84
+

Secondly, we use the SAEM estimation routine and check the convergence plots for SFO with constant variance

+
+f_parent_nlmixr_saem_sfo_const <- nlmixr(f_parent_mkin_const["SFO", ], est = "saem",
+  control = nlmixr::saemControl(logLik = TRUE))
+traceplot(f_parent_nlmixr_saem_sfo_const$nm)
+

+

for SFO with two-component error

+
+f_parent_nlmixr_saem_sfo_tc <- nlmixr(f_parent_mkin_tc["SFO", ], est = "saem",
+  control = nlmixr::saemControl(logLik = TRUE))
+traceplot(f_parent_nlmixr_saem_sfo_tc$nm)
+

+

For DFOP with constant variance, the convergence plots show considerable instability of the fit, which can be alleviated by increasing the number of iterations and the number of parallel chains for the first phase of algorithm.

+
+f_parent_nlmixr_saem_dfop_const <- nlmixr(f_parent_mkin_const["DFOP", ], est = "saem",
+  control = nlmixr::saemControl(logLik = TRUE, nBurn = 1000), nmc = 15)
+traceplot(f_parent_nlmixr_saem_dfop_const$nm)
+

+

For DFOP with two-component error, the same increase in iterations and parallel chains was used, but using the two-component error appears to lead to a less erratic convergence, so this may not be necessary to this degree.

+
+f_parent_nlmixr_saem_dfop_tc <- nlmixr(f_parent_mkin_tc["DFOP", ], est = "saem",
+  control = nlmixr::saemControl(logLik = TRUE, nBurn = 1000, nmc = 15))
+traceplot(f_parent_nlmixr_saem_dfop_tc$nm)
+

+

The AIC values are internally calculated using Gaussian quadrature. For an unknown reason, the AIC value obtained for the DFOP fit using the two-component error model is given as Infinity.

+
+AIC(f_parent_nlmixr_saem_sfo_const$nm, f_parent_nlmixr_saem_sfo_tc$nm,
+  f_parent_nlmixr_saem_dfop_const$nm, f_parent_nlmixr_saem_dfop_tc$nm)
+
                                   df    AIC
+f_parent_nlmixr_saem_sfo_const$nm   5 820.54
+f_parent_nlmixr_saem_sfo_tc$nm      6 835.26
+f_parent_nlmixr_saem_dfop_const$nm  9 842.84
+f_parent_nlmixr_saem_dfop_tc$nm    10 684.51
+

The following table gives the AIC values obtained with the three packages.

+
+AIC_all <- data.frame(
+  nlme = c(AIC(f_parent_nlme_sfo_const), AIC(f_parent_nlme_sfo_tc), NA, AIC(f_parent_nlme_dfop_tc)),
+  nlmixr_focei = sapply(list(f_parent_nlmixr_focei_sfo_const$nm, f_parent_nlmixr_focei_sfo_tc$nm,
+  f_parent_nlmixr_focei_dfop_const$nm, f_parent_nlmixr_focei_dfop_tc$nm), AIC),
+  saemix = sapply(list(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so,
+    f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc_moreiter$so), AIC),
+  nlmixr_saem = sapply(list(f_parent_nlmixr_saem_sfo_const$nm, f_parent_nlmixr_saem_sfo_tc$nm,
+  f_parent_nlmixr_saem_dfop_const$nm, f_parent_nlmixr_saem_dfop_tc$nm), AIC)
+)
+kable(AIC_all)
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
nlmenlmixr_foceisaemixnlmixr_saem
818.63818.63818.37820.54
820.61820.61820.38835.26
NA728.11725.91842.84
687.84687.82683.64684.51
+
+
+
+
+

+References

+ +
+
+

EFSA. 2018. “Peer Review of the Pesticide Risk Assessment of the Active Substance Dimethenamid-P.” EFSA Journal 16 (4): 5211.

+
+
+

Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria. 2018. “Renewal Assessment Report Dimethenamid-P Volume 3 - B.8 Environmental fate and behaviour, Rev. 2 - November 2017.” https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716.

+
+
+
+
+ + + +
+ + + +
+

Developed by Johannes Ranke.

+
+ +
+

Site built with pkgdown 1.6.1.

+
+ +
+
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a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png new file mode 100644 index 00000000..6edeb794 Binary files /dev/null and b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png differ diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/header-attrs-2.9/header-attrs.js b/docs/dev/articles/web_only/dimethenamid_2018_files/header-attrs-2.9/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/dev/articles/web_only/dimethenamid_2018_files/header-attrs-2.9/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/dev/authors.html b/docs/dev/authors.html index 4208dc24..943cba1b 100644 --- a/docs/dev/authors.html +++ b/docs/dev/authors.html @@ -71,7 +71,7 @@ mkin - 1.0.5 + 1.1.0 diff --git a/docs/dev/index.html b/docs/dev/index.html index 6e3fa6e1..8049b3a1 100644 --- a/docs/dev/index.html +++ b/docs/dev/index.html @@ -38,7 +38,7 @@ mkin - 1.0.5 + 1.1.0 diff --git a/docs/dev/news/index.html b/docs/dev/news/index.html index 234ba02f..cfe577cf 100644 --- a/docs/dev/news/index.html +++ b/docs/dev/news/index.html @@ -71,7 +71,7 @@ mkin - 1.0.5 + 1.1.0 diff --git a/docs/dev/pkgdown.yml b/docs/dev/pkgdown.yml index b2c50e79..16f7f0d6 100644 --- a/docs/dev/pkgdown.yml +++ b/docs/dev/pkgdown.yml @@ -10,7 +10,8 @@ articles: web_only/NAFTA_examples: NAFTA_examples.html web_only/benchmarks: benchmarks.html web_only/compiled_models: compiled_models.html -last_built: 2021-06-17T12:41Z + web_only/dimethenamid_2018: dimethenamid_2018.html +last_built: 2021-07-27T15:54Z urls: reference: https://pkgdown.jrwb.de/mkin/reference article: https://pkgdown.jrwb.de/mkin/articles diff --git a/docs/dev/reference/Rplot005.png b/docs/dev/reference/Rplot005.png index 55aa7eec..92c7cc2d 100644 Binary files a/docs/dev/reference/Rplot005.png and b/docs/dev/reference/Rplot005.png differ diff --git a/docs/dev/reference/dimethenamid_2018-2.png b/docs/dev/reference/dimethenamid_2018-2.png new file mode 100644 index 00000000..a81b2aaf Binary files /dev/null and b/docs/dev/reference/dimethenamid_2018-2.png differ diff --git a/docs/dev/reference/dimethenamid_2018.html b/docs/dev/reference/dimethenamid_2018.html index e255765e..160dcaa3 100644 --- a/docs/dev/reference/dimethenamid_2018.html +++ b/docs/dev/reference/dimethenamid_2018.html @@ -77,7 +77,7 @@ constrained by data protection regulations." /> mkin - 1.0.5 + 1.1.0 @@ -168,7 +168,7 @@ constrained by data protection regulations.

Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria (2018) Renewal Assessment Report Dimethenamid-P Volume 3 - B.8 Environmental fate and behaviour Rev. 2 - November 2017 -http://registerofquestions.efsa.europa.eu/roqFrontend/outputLoader?output=ON-5211

+https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716

Details

The R code used to create this data object is installed with this package @@ -295,8 +295,11 @@ specific pieces of information in the comments.

#> M31 ~ add(sigma_low_M31) + prop(rsd_high_M31) #> }) #> } -#> <environment: 0x555559c2bd78>
f_dmta_nlmixr_focei <- nlmixr(f_dmta_mkin_tc, est = "focei", - control = nlmixr::foceiControl(print = 500)) +#> <environment: 0x555559c00ce8>
# The focei fit takes about four minutes on my system +system.time( + f_dmta_nlmixr_focei <- nlmixr(f_dmta_mkin_tc, est = "focei", + control = nlmixr::foceiControl(print = 500)) +)
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#> → creating full model...
#> → pruning branches (`if`/`else`)...
#> ✔ done
#> → loading into symengine environment...
#> ✔ done
#> → creating full model...
#> → pruning branches (`if`/`else`)...
#> ✔ done
#> → loading into symengine environment...
#> ✔ done
#> → calculate jacobian
#> [====|====|====|====|====|====|====|====|====|====] 0:00:02 #>
#> → calculate sensitivities
#> [====|====|====|====|====|====|====|====|====|====] 0:00:04 #>
#> → calculate ∂(f)/∂(η)
#> [====|====|====|====|====|====|====|====|====|====] 0:00:01 @@ -320,12 +323,13 @@ specific pieces of information in the comments.

#> |.....................| o5 | o6 | o7 | o8 | #> |.....................| o9 | o10 |...........|...........| #> calculating covariance matrix -#> done
#> Calculating residuals/tables
#> done
#> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
#> Warning: last objective function was not at minimum, possible problems in optimization
#> Warning: S matrix non-positive definite
#> Warning: using R matrix to calculate covariance
#> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
summary(f_dmta_nlmixr_focei) +#> done
#> Calculating residuals/tables
#> done
#> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
#> Warning: last objective function was not at minimum, possible problems in optimization
#> Warning: S matrix non-positive definite
#> Warning: using R matrix to calculate covariance
#> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
#> user system elapsed +#> 227.879 9.742 237.728
summary(f_dmta_nlmixr_focei)
#> nlmixr version used for fitting: 2.0.4 -#> mkin version used for pre-fitting: 1.0.5 +#> mkin version used for pre-fitting: 1.1.0 #> R version used for fitting: 4.1.0 -#> Date of fit: Thu Jun 17 14:04:58 2021 -#> Date of summary: Thu Jun 17 14:04:58 2021 +#> Date of fit: Tue Jul 27 16:02:33 2021 +#> Date of summary: Tue Jul 27 16:02:34 2021 #> #> Equations: #> d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -346,7 +350,7 @@ specific pieces of information in the comments.

#> #> Degradation model predictions using RxODE #> -#> Fitted in 242.937 s +#> Fitted in 237.547 s #> #> Variance model: Two-component variance function #> @@ -480,13 +484,194 @@ specific pieces of information in the comments.

#> M23 34.99 116.24 NA NA NA #> M27 53.05 176.23 NA NA NA #> M31 48.48 161.05 NA NA NA
plot(f_dmta_nlmixr_focei) -
# saem has a problem with this model/data combination, maybe because of the -# overparameterised error model, to be investigated -#f_dmta_nlmixr_saem <- nlmixr(f_dmta_mkin_tc, est = "saem", -# control = saemControl(print = 500)) -#summary(f_dmta_nlmixr_saem) -#plot(f_dmta_nlmixr_saem) -# } +
# Using saemix takes about 18 minutes +system.time( + f_dmta_saemix <- saem(f_dmta_mkin_tc, test_log_parms = TRUE) +) +
#> Running main SAEM algorithm +#> [1] "Tue Jul 27 16:02:34 2021" +#> .... +#> Minimisation finished +#> [1] "Tue Jul 27 16:21:39 2021"
#> user system elapsed +#> 1213.394 0.087 1213.578
+# nlmixr with est = "saem" is pretty fast with default iteration numbers, most +# of the time (about 2.5 minutes) is spent for calculating the log likelihood at the end +# The likelihood calculated for the nlmixr fit is much lower than that found by saemix +# Also, the trace plot and the plot of the individual predictions is not +# convincing for the parent. It seems we are fitting an overparameterised +# model, so the result we get strongly depends on starting parameters and control settings. +system.time( + f_dmta_nlmixr_saem <- nlmixr(f_dmta_mkin_tc, est = "saem", + control = nlmixr::saemControl(print = 500, logLik = TRUE, nmc = 9)) +) +
#> With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#>
#> → generate SAEM model
#> ✔ done
#> 1: 98.3427 -3.5148 -3.3187 -3.7728 -2.1163 -2.8457 0.9482 -2.8064 -2.7412 -2.8745 2.7912 0.6805 0.8213 0.8055 0.8578 1.4980 2.9309 0.2850 0.2854 0.2850 4.0990 0.3821 3.5349 0.6537 5.4143 0.0002 4.5093 0.1905 +#> 500: 97.8277 -4.3506 -4.0318 -4.1520 -3.0553 -3.5843 1.1326 -2.0873 -2.0421 -2.0751 0.2960 1.2515 0.2531 0.3807 0.7928 0.8863 6.5211 0.1433 0.1082 0.3353 0.8960 0.0470 0.7501 0.0475 0.9527 0.0281 0.7321 0.0594
#> Calculating covariance matrix
#>
#> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)
#>
#> → creating full model...
#> → pruning branches (`if`/`else`)...
#> ✔ done
#> → loading into symengine environment...
#> ✔ done
#> → compiling EBE model...
#>
#> ✔ done
#> Needed Covariates:
#> [1] "CMT"
#> Calculating residuals/tables
#> done
#> user system elapsed +#> 818.782 3.808 154.926
traceplot(f_dmta_nlmixr_saem$nm) +
#> Error in traceplot(f_dmta_nlmixr_saem$nm): could not find function "traceplot"
summary(f_dmta_nlmixr_saem) +
#> nlmixr version used for fitting: 2.0.4 +#> mkin version used for pre-fitting: 1.1.0 +#> R version used for fitting: 4.1.0 +#> Date of fit: Tue Jul 27 16:25:23 2021 +#> Date of summary: Tue Jul 27 16:25:23 2021 +#> +#> Equations: +#> d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * +#> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) +#> * DMTA +#> d_M23/dt = + f_DMTA_to_M23 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) +#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * +#> exp(-k2 * time))) * DMTA - k_M23 * M23 +#> d_M27/dt = + f_DMTA_to_M27 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) +#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * +#> exp(-k2 * time))) * DMTA - k_M27 * M27 + k_M31 * M31 +#> d_M31/dt = + f_DMTA_to_M31 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) +#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * +#> exp(-k2 * time))) * DMTA - k_M31 * M31 +#> +#> Data: +#> 568 observations of 4 variable(s) grouped in 6 datasets +#> +#> Degradation model predictions using RxODE +#> +#> Fitted in 154.632 s +#> +#> Variance model: Two-component variance function +#> +#> Mean of starting values for individual parameters: +#> DMTA_0 log_k_M23 log_k_M27 log_k_M31 f_DMTA_ilr_1 f_DMTA_ilr_2 +#> 98.7698 -3.9216 -4.3377 -4.2477 0.1380 0.1393 +#> f_DMTA_ilr_3 log_k1 log_k2 g_qlogis +#> -1.7571 -2.2341 -3.7763 0.4502 +#> +#> Mean of starting values for error model parameters: +#> sigma_low_DMTA rsd_high_DMTA sigma_low_M23 rsd_high_M23 sigma_low_M27 +#> 0.69793 0.02577 0.69793 0.02577 0.69793 +#> rsd_high_M27 sigma_low_M31 rsd_high_M31 +#> 0.02577 0.69793 0.02577 +#> +#> Fixed degradation parameter values: +#> None +#> +#> Results: +#> +#> Likelihood calculated by focei +#> AIC BIC logLik +#> 2036 2157 -989.8 +#> +#> Optimised parameters: +#> est. lower upper +#> DMTA_0 97.828 96.121 99.535 +#> log_k_M23 -4.351 -5.300 -3.401 +#> log_k_M27 -4.032 -4.470 -3.594 +#> log_k_M31 -4.152 -4.689 -3.615 +#> log_k1 -3.055 -3.785 -2.325 +#> log_k2 -3.584 -4.517 -2.651 +#> g_qlogis 1.133 -2.165 4.430 +#> f_DMTA_tffm0_1_qlogis -2.087 -2.407 -1.768 +#> f_DMTA_tffm0_2_qlogis -2.042 -2.336 -1.748 +#> f_DMTA_tffm0_3_qlogis -2.075 -2.557 -1.593 +#> +#> Correlation: +#> DMTA_0 l__M23 l__M27 l__M31 log_k1 log_k2 g_qlgs +#> log_k_M23 -0.031 +#> log_k_M27 -0.050 0.004 +#> log_k_M31 -0.032 0.003 0.078 +#> log_k1 0.014 -0.002 -0.002 -0.001 +#> log_k2 0.059 0.006 -0.001 0.002 -0.037 +#> g_qlogis -0.077 0.005 0.009 0.004 0.035 -0.201 +#> f_DMTA_tffm0_1_qlogis -0.104 0.066 0.009 0.006 0.000 -0.011 0.014 +#> f_DMTA_tffm0_2_qlogis -0.120 0.013 0.081 -0.033 -0.002 -0.013 0.017 +#> f_DMTA_tffm0_3_qlogis -0.086 0.010 0.060 0.078 -0.002 -0.005 0.010 +#> f_DMTA_0_1 f_DMTA_0_2 +#> log_k_M23 +#> log_k_M27 +#> log_k_M31 +#> log_k1 +#> log_k2 +#> g_qlogis +#> f_DMTA_tffm0_1_qlogis +#> f_DMTA_tffm0_2_qlogis 0.026 +#> f_DMTA_tffm0_3_qlogis 0.019 0.002 +#> +#> Random effects (omega): +#> eta.DMTA_0 eta.log_k_M23 eta.log_k_M27 eta.log_k_M31 +#> eta.DMTA_0 0.296 0.000 0.0000 0.0000 +#> eta.log_k_M23 0.000 1.252 0.0000 0.0000 +#> eta.log_k_M27 0.000 0.000 0.2531 0.0000 +#> eta.log_k_M31 0.000 0.000 0.0000 0.3807 +#> eta.log_k1 0.000 0.000 0.0000 0.0000 +#> eta.log_k2 0.000 0.000 0.0000 0.0000 +#> eta.g_qlogis 0.000 0.000 0.0000 0.0000 +#> eta.f_DMTA_tffm0_1_qlogis 0.000 0.000 0.0000 0.0000 +#> eta.f_DMTA_tffm0_2_qlogis 0.000 0.000 0.0000 0.0000 +#> eta.f_DMTA_tffm0_3_qlogis 0.000 0.000 0.0000 0.0000 +#> eta.log_k1 eta.log_k2 eta.g_qlogis +#> eta.DMTA_0 0.0000 0.0000 0.000 +#> eta.log_k_M23 0.0000 0.0000 0.000 +#> eta.log_k_M27 0.0000 0.0000 0.000 +#> eta.log_k_M31 0.0000 0.0000 0.000 +#> eta.log_k1 0.7928 0.0000 0.000 +#> eta.log_k2 0.0000 0.8863 0.000 +#> eta.g_qlogis 0.0000 0.0000 6.521 +#> eta.f_DMTA_tffm0_1_qlogis 0.0000 0.0000 0.000 +#> eta.f_DMTA_tffm0_2_qlogis 0.0000 0.0000 0.000 +#> eta.f_DMTA_tffm0_3_qlogis 0.0000 0.0000 0.000 +#> eta.f_DMTA_tffm0_1_qlogis eta.f_DMTA_tffm0_2_qlogis +#> eta.DMTA_0 0.0000 0.0000 +#> eta.log_k_M23 0.0000 0.0000 +#> eta.log_k_M27 0.0000 0.0000 +#> eta.log_k_M31 0.0000 0.0000 +#> eta.log_k1 0.0000 0.0000 +#> eta.log_k2 0.0000 0.0000 +#> eta.g_qlogis 0.0000 0.0000 +#> eta.f_DMTA_tffm0_1_qlogis 0.1433 0.0000 +#> eta.f_DMTA_tffm0_2_qlogis 0.0000 0.1082 +#> eta.f_DMTA_tffm0_3_qlogis 0.0000 0.0000 +#> eta.f_DMTA_tffm0_3_qlogis +#> eta.DMTA_0 0.0000 +#> eta.log_k_M23 0.0000 +#> eta.log_k_M27 0.0000 +#> eta.log_k_M31 0.0000 +#> eta.log_k1 0.0000 +#> eta.log_k2 0.0000 +#> eta.g_qlogis 0.0000 +#> eta.f_DMTA_tffm0_1_qlogis 0.0000 +#> eta.f_DMTA_tffm0_2_qlogis 0.0000 +#> eta.f_DMTA_tffm0_3_qlogis 0.3353 +#> +#> Variance model: +#> sigma_low_DMTA rsd_high_DMTA sigma_low_M23 rsd_high_M23 sigma_low_M27 +#> 0.89603 0.04704 0.75015 0.04753 0.95265 +#> rsd_high_M27 sigma_low_M31 rsd_high_M31 +#> 0.02810 0.73212 0.05942 +#> +#> Backtransformed parameters: +#> est. lower upper +#> DMTA_0 97.82774 96.120503 99.53498 +#> k_M23 0.01290 0.004991 0.03334 +#> k_M27 0.01774 0.011451 0.02749 +#> k_M31 0.01573 0.009195 0.02692 +#> f_DMTA_to_M23 0.11033 NA NA +#> f_DMTA_to_M27 0.10218 NA NA +#> f_DMTA_to_M31 0.08784 NA NA +#> k1 0.04711 0.022707 0.09773 +#> k2 0.02775 0.010918 0.07056 +#> g 0.75632 0.102960 0.98823 +#> +#> Resulting formation fractions: +#> ff +#> DMTA_M23 0.11033 +#> DMTA_M27 0.10218 +#> DMTA_M31 0.08784 +#> DMTA_sink 0.69965 +#> +#> Estimated disappearance times: +#> DT50 DT90 DT50back DT50_k1 DT50_k2 +#> DMTA 16.59 57.44 17.29 14.71 24.97 +#> M23 53.74 178.51 NA NA NA +#> M27 39.07 129.78 NA NA NA +#> M31 44.06 146.36 NA NA NA
plot(f_dmta_nlmixr_saem) +
# }
diff --git a/docs/dev/reference/index.html b/docs/dev/reference/index.html index bb030605..d5ec387a 100644 --- a/docs/dev/reference/index.html +++ b/docs/dev/reference/index.html @@ -71,7 +71,7 @@ mkin - 1.0.5 + 1.1.0 diff --git a/docs/dev/reference/mean_degparms.html b/docs/dev/reference/mean_degparms.html index f63dbc31..5981c625 100644 --- a/docs/dev/reference/mean_degparms.html +++ b/docs/dev/reference/mean_degparms.html @@ -72,7 +72,7 @@ mkin - 1.0.5 + 1.1.0 diff --git a/docs/dev/reference/mkinmod.html b/docs/dev/reference/mkinmod.html index 413f0ae1..e57e7062 100644 --- a/docs/dev/reference/mkinmod.html +++ b/docs/dev/reference/mkinmod.html @@ -44,9 +44,7 @@ variable, specifying the corresponding submodel as well as outgoing pathways (see examples). Print mkinmod objects in a way that the user finds his way to get to its -components. -This is a convenience function to set up the lists used as arguments for -mkinmod." /> +components." /> @@ -78,7 +76,7 @@ mkinmod." /> mkin - 1.0.3.9000 + 1.1.0 @@ -155,8 +153,6 @@ variable, specifying the corresponding submodel as well as outgoing pathways (see examples).

Print mkinmod objects in a way that the user finds his way to get to its components.

-

This is a convenience function to set up the lists used as arguments for -mkinmod.

mkinmod(
@@ -348,7 +344,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media

    parent = mkinsub("SFO", "m1", full_name = "Test compound"),
    m1 = mkinsub("SFO", full_name = "Metabolite M1"),
    name = "SFO_SFO", dll_dir = DLL_dir, unload = TRUE, overwrite = TRUE)
-
#> Copied DLL from /tmp/Rtmp92fCb2/file133ad522561845.so to /home/jranke/.local/share/mkin/SFO_SFO.so
# Now we can save the model and restore it in a new session
+
#> Copied DLL from /tmp/RtmpPWWdbW/fileccff46a6d9773.so to /home/jranke/.local/share/mkin/SFO_SFO.so
# Now we can save the model and restore it in a new session
 saveRDS(SFO_SFO.2, file = "~/SFO_SFO.rds")
 # Terminate the R session here if you would like to check, and then do
 library(mkin)
@@ -397,7 +393,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media

 #>     })
 #>     return(predicted)
 #> }
-#> <environment: 0x5555572517f8>

+#> <environment: 0x5555645abab8>

 # If we have several parallel metabolites
 # (compare tests/testthat/test_synthetic_data_for_UBA_2014.R)
 m_synth_DFOP_par <- mkinmod(
diff --git a/docs/dev/reference/nlme-1.png b/docs/dev/reference/nlme-1.png
index 365aaef0..f739089a 100644
Binary files a/docs/dev/reference/nlme-1.png and b/docs/dev/reference/nlme-1.png differ
diff --git a/docs/dev/reference/nlme-2.png b/docs/dev/reference/nlme-2.png
index 40841404..d3b29bb0 100644
Binary files a/docs/dev/reference/nlme-2.png and b/docs/dev/reference/nlme-2.png differ
diff --git a/docs/dev/reference/nlme.html b/docs/dev/reference/nlme.html
index 55a94443..184585df 100644
--- a/docs/dev/reference/nlme.html
+++ b/docs/dev/reference/nlme.html
@@ -75,7 +75,7 @@ datasets. They are used internally by the nlme.mmkin() method." />
       
       
         mkin
-        1.0.5
+        1.1.0
       
     

 
@@ -216,28 +216,28 @@ datasets. They are used internally by the nlme.m
 #>   Model: value ~ nlme_f(name, time, parent_0, log_k_parent_sink) 
 #>   Data: grouped_data 
 #>        AIC      BIC    logLik
-#>   300.6824 310.2426 -145.3412
+#>   278.1355 287.7946 -134.0677
 #> 
 #> Random effects:
 #>  Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1)
 #>  Level: ds
 #>  Structure: Diagonal
 #>         parent_0 log_k_parent_sink Residual
-#> StdDev: 1.697361         0.6801209 3.666073
+#> StdDev: 3.406042         0.6437579 2.620833
 #> 
 #> Fixed effects:  parent_0 + log_k_parent_sink ~ 1 
 #>                       Value Std.Error DF  t-value p-value
-#> parent_0          100.99378 1.3890416 46 72.70753       0
-#> log_k_parent_sink  -3.07521 0.4018589 46 -7.65246       0
+#> parent_0          101.50173  2.123709 47 47.79457       0
+#> log_k_parent_sink  -3.07597  0.379775 47 -8.09945       0
 #>  Correlation: 
 #>                   prnt_0
-#> log_k_parent_sink 0.027 
+#> log_k_parent_sink 0.01  
 #> 
 #> Standardized Within-Group Residuals:
-#>        Min         Q1        Med         Q3        Max 
-#> -1.9942823 -0.5622565  0.1791579  0.7165038  2.0704781 
+#>         Min          Q1         Med          Q3         Max 
+#> -2.06889303 -0.50100169 -0.06268253  0.62557544  2.19922001 
 #> 
-#> Number of Observations: 50
+#> Number of Observations: 51
 #> Number of Groups: 3 
plot(augPred(m_nlme, level = 0:1), layout = c(3, 1))
 
# augPred does not work on fits with more than one state
 # variable
diff --git a/docs/dev/reference/nlme.mmkin.html b/docs/dev/reference/nlme.mmkin.html
index 2bbf4f80..866091ca 100644
--- a/docs/dev/reference/nlme.mmkin.html
+++ b/docs/dev/reference/nlme.mmkin.html
@@ -74,7 +74,7 @@ have been obtained by fitting the same model to a list of datasets." />
       
       
         mkin
-        1.0.5
+        1.1.0
       
     

 
@@ -194,10 +194,9 @@ mmkin model are used as fixed parameters

     
     
       random
-      
If not specified, correlated random effects are set up
-for all optimised degradation model parameters using the log-Cholesky
-parameterization nlme::pdLogChol that is also the default of
-the generic nlme method.

+      
If not specified, no correlations between random effects are
+set up for the optimised degradation model parameters. This is
+achieved by using the nlme::pdDiag method.

     
     
       groups
diff --git a/docs/dev/reference/nlmixr.mmkin.html b/docs/dev/reference/nlmixr.mmkin.html
index d09f2ad4..328bad43 100644
--- a/docs/dev/reference/nlmixr.mmkin.html
+++ b/docs/dev/reference/nlmixr.mmkin.html
@@ -74,7 +74,7 @@ Expectation Maximisation algorithm (SAEM)." />
       
       
         mkin
-        1.0.5
+        1.1.0
       
     
 
@@ -4501,7 +4501,7 @@ obtained by fitting the same model to a list of datasets using k_A1=rx_expr_11;
 #> f_parent=1/(1+exp(-(ETA[4]+THETA[4])));
 #> tad=tad();
-#> dosenum=dosenum();
#> Needed Covariates:
#> [1] "f_parent_to_A1" "CMT"           
#> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
#> Timing stopped at: 5.607 0.474 6.078
f_nlmixr_fomc_sfo_focei_const <- nlmixr(f_mmkin_const["FOMC-SFO", ], est = "focei")
+#> dosenum=dosenum();
#> Needed Covariates:
#> [1] "f_parent_to_A1" "CMT"           
#> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
#> Timing stopped at: 5.548 0.415 5.961
f_nlmixr_fomc_sfo_focei_const <- nlmixr(f_mmkin_const["FOMC-SFO", ], est = "focei")
 
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#> → creating full model...
#> → pruning branches (`if`/`else`)...
#> ✔ done
#> → loading into symengine environment...
#> ✔ done
#> → creating full model...
#> → pruning branches (`if`/`else`)...
#> ✔ done
#> → loading into symengine environment...
#> ✔ done
#> → calculate jacobian
#> 
#> → calculate sensitivities
#> 
#> → calculate ∂(f)/∂(η)
#> 
#> → calculate ∂(R²)/∂(η)
#> 
#> → finding duplicate expressions in inner model...
#> 
#> → optimizing duplicate expressions in inner model...
#> 
#> → finding duplicate expressions in EBE model...
#> 
#> → optimizing duplicate expressions in EBE model...
#> 
#> → compiling inner model...
#>  
#> ✔ done
#> → finding duplicate expressions in FD model...
#> 
#> → optimizing duplicate expressions in FD model...
#> 
#> → compiling EBE model...
#>  
#> ✔ done
#> → compiling events FD model...
#>  
#> ✔ done
#> Model:
#> cmt(parent);
 #> cmt(A1);
 #> rx_expr_6~ETA[1]+THETA[1];
@@ -4550,7 +4550,7 @@ obtained by fitting the same model to a list of datasets using beta=exp(rx_expr_8);
 #> f_parent=1/(1+exp(-(ETA[3]+THETA[3])));
 #> tad=tad();
-#> dosenum=dosenum();
#> Needed Covariates:
#> [1] "f_parent_to_A1" "CMT"           
#> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
#> Timing stopped at: 6.853 0.393 7.242
f_nlmixr_dfop_sfo_focei_const <- nlmixr(f_mmkin_const["DFOP-SFO", ], est = "focei")
+#> dosenum=dosenum();
#> Needed Covariates:
#> [1] "f_parent_to_A1" "CMT"           
#> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
#> Timing stopped at: 6.895 0.416 7.309
f_nlmixr_dfop_sfo_focei_const <- nlmixr(f_mmkin_const["DFOP-SFO", ], est = "focei")
 
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#> → creating full model...
#> → pruning branches (`if`/`else`)...
#> ✔ done
#> → loading into symengine environment...
#> ✔ done
#> → creating full model...
#> → pruning branches (`if`/`else`)...
#> ✔ done
#> → loading into symengine environment...
#> ✔ done
#> → calculate jacobian
#> 
#> → calculate sensitivities
#> 
#> → calculate ∂(f)/∂(η)
#> 
#> → calculate ∂(R²)/∂(η)
#> 
#> → finding duplicate expressions in inner model...
#> 
#> → optimizing duplicate expressions in inner model...
#> 
#> → finding duplicate expressions in EBE model...
#> 
#> → optimizing duplicate expressions in EBE model...
#> 
#> → compiling inner model...
#>  
#> ✔ done
#> → finding duplicate expressions in FD model...
#> 
#> → optimizing duplicate expressions in FD model...
#> 
#> → compiling EBE model...
#>  
#> ✔ done
#> → compiling events FD model...
#>  
#> ✔ done
#> Model:
#> cmt(parent);
 #> cmt(A1);
 #> rx_expr_6~ETA[1]+THETA[1];
@@ -4607,10 +4607,10 @@ obtained by fitting the same model to a list of datasets using f_parent=1/(1+exp(-(ETA[3]+THETA[3])));
 #> g=1/(rx_expr_20);
 #> tad=tad();
-#> dosenum=dosenum();
#> Needed Covariates:
#> [1] "f_parent_to_A1" "CMT"           
#> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
#> Timing stopped at: 15.18 0.414 15.6

+#> dosenum=dosenum();
#> Needed Covariates:
#> [1] "f_parent_to_A1" "CMT"           
#> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
#> Timing stopped at: 15.03 0.478 15.51

 # Variance by variable is supported by 'saem' and 'focei'
 f_nlmixr_fomc_sfo_saem_obs <- nlmixr(f_mmkin_obs["FOMC-SFO", ], est = "saem")
-
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#>  
#> → generate SAEM model
#> ✔ done
#> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc,     ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG,     addProp = .addProp, tol = .tol, itmax = .itmax, type = .type,     powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1
#> Timing stopped at: 1.22 0.089 1.31
f_nlmixr_fomc_sfo_focei_obs <- nlmixr(f_mmkin_obs["FOMC-SFO", ], est = "focei")
+
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#>  
#> → generate SAEM model
#> ✔ done
#> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc,     ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG,     addProp = .addProp, tol = .tol, itmax = .itmax, type = .type,     powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1
#> Timing stopped at: 1.294 0.134 1.427
f_nlmixr_fomc_sfo_focei_obs <- nlmixr(f_mmkin_obs["FOMC-SFO", ], est = "focei")
 
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#> → creating full model...
#> → pruning branches (`if`/`else`)...
#> ✔ done
#> → loading into symengine environment...
#> ✔ done
#> → creating full model...
#> → pruning branches (`if`/`else`)...
#> ✔ done
#> → loading into symengine environment...
#> ✔ done
#> → calculate jacobian
#> 
#> → calculate sensitivities
#> 
#> → calculate ∂(f)/∂(η)
#> 
#> → calculate ∂(R²)/∂(η)
#> 
#> → finding duplicate expressions in inner model...
#> 
#> → optimizing duplicate expressions in inner model...
#> 
#> → finding duplicate expressions in EBE model...
#> 
#> → optimizing duplicate expressions in EBE model...
#> 
#> → compiling inner model...
#>  
#> ✔ done
#> → finding duplicate expressions in FD model...
#> 
#> → optimizing duplicate expressions in FD model...
#> 
#> → compiling EBE model...
#>  
#> ✔ done
#> → compiling events FD model...
#>  
#> ✔ done
#> Model:
#> cmt(parent);
 #> cmt(A1);
 #> rx_expr_6~ETA[1]+THETA[1];
@@ -4659,8 +4659,8 @@ obtained by fitting the same model to a list of datasets using beta=exp(rx_expr_8);
 #> f_parent=1/(1+exp(-(ETA[3]+THETA[3])));
 #> tad=tad();
-#> dosenum=dosenum();
#> Needed Covariates:
#> [1] "f_parent_to_A1" "CMT"           
#> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
#> Timing stopped at: 6.784 0.418 7.2
f_nlmixr_dfop_sfo_saem_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "saem")
-
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#>  
#> → generate SAEM model
#> ✔ done
#> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc,     ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG,     addProp = .addProp, tol = .tol, itmax = .itmax, type = .type,     powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1
#> Timing stopped at: 1.357 0.096 1.452
f_nlmixr_dfop_sfo_focei_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "focei")
+#> dosenum=dosenum();
#> Needed Covariates:
#> [1] "f_parent_to_A1" "CMT"           
#> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
#> Timing stopped at: 6.584 0.393 6.976
f_nlmixr_dfop_sfo_saem_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "saem")
+
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#>  
#> → generate SAEM model
#> ✔ done
#> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc,     ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG,     addProp = .addProp, tol = .tol, itmax = .itmax, type = .type,     powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1
#> Timing stopped at: 1.302 0.142 1.443
f_nlmixr_dfop_sfo_focei_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "focei")
 
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#> → creating full model...
#> → pruning branches (`if`/`else`)...
#> ✔ done
#> → loading into symengine environment...
#> ✔ done
#> → creating full model...
#> → pruning branches (`if`/`else`)...
#> ✔ done
#> → loading into symengine environment...
#> ✔ done
#> → calculate jacobian
#> 
#> → calculate sensitivities
#> 
#> → calculate ∂(f)/∂(η)
#> 
#> → calculate ∂(R²)/∂(η)
#> 
#> → finding duplicate expressions in inner model...
#> 
#> → optimizing duplicate expressions in inner model...
#> 
#> → finding duplicate expressions in EBE model...
#> 
#> → optimizing duplicate expressions in EBE model...
#> 
#> → compiling inner model...
#>  
#> ✔ done
#> → finding duplicate expressions in FD model...
#> 
#> → optimizing duplicate expressions in FD model...
#> 
#> → compiling EBE model...
#>  
#> ✔ done
#> → compiling events FD model...
#>  
#> ✔ done
#> Model:
#> cmt(parent);
 #> cmt(A1);
 #> rx_expr_6~ETA[1]+THETA[1];
@@ -4717,7 +4717,7 @@ obtained by fitting the same model to a list of datasets using f_parent=1/(1+exp(-(ETA[3]+THETA[3])));
 #> g=1/(rx_expr_19);
 #> tad=tad();
-#> dosenum=dosenum();
#> Needed Covariates:
#> [1] "f_parent_to_A1" "CMT"           
#> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
#> Timing stopped at: 15.17 0.353 15.52

+#> dosenum=dosenum();
#> Needed Covariates:
#> [1] "f_parent_to_A1" "CMT"           
#> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
#> Timing stopped at: 14.58 0.482 15.06

 # Identical two-component error for all variables is only possible with
 # est = 'focei' in nlmixr
 f_nlmixr_fomc_sfo_focei_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "focei")
@@ -4771,7 +4771,7 @@ obtained by fitting the same model to a list of datasets using beta=exp(rx_expr_8);
 #> f_parent=1/(1+exp(-(ETA[3]+THETA[3])));
 #> tad=tad();
-#> dosenum=dosenum();
#> Needed Covariates:
#> [1] "f_parent_to_A1" "CMT"           
#> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
#> Timing stopped at: 8.708 0.429 9.135
f_nlmixr_dfop_sfo_focei_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei")
+#> dosenum=dosenum();
#> Needed Covariates:
#> [1] "f_parent_to_A1" "CMT"           
#> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
#> Timing stopped at: 8.484 0.401 8.883
f_nlmixr_dfop_sfo_focei_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei")
 
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#> → creating full model...
#> → pruning branches (`if`/`else`)...
#> ✔ done
#> → loading into symengine environment...
#> ✔ done
#> → creating full model...
#> → pruning branches (`if`/`else`)...
#> ✔ done
#> → loading into symengine environment...
#> ✔ done
#> → calculate jacobian
#> 
#> → calculate sensitivities
#> 
#> → calculate ∂(f)/∂(η)
#> 
#> → calculate ∂(R²)/∂(η)
#> 
#> → finding duplicate expressions in inner model...
#> 
#> → optimizing duplicate expressions in inner model...
#> 
#> → finding duplicate expressions in EBE model...
#> 
#> → optimizing duplicate expressions in EBE model...
#> 
#> → compiling inner model...
#>  
#> ✔ done
#> → finding duplicate expressions in FD model...
#> 
#> → optimizing duplicate expressions in FD model...
#> 
#> → compiling EBE model...
#>  
#> ✔ done
#> → compiling events FD model...
#>  
#> ✔ done
#> Model:
#> cmt(parent);
 #> cmt(A1);
 #> rx_expr_6~ETA[1]+THETA[1];
@@ -4830,12 +4830,12 @@ obtained by fitting the same model to a list of datasets using f_parent=1/(1+exp(-(ETA[3]+THETA[3])));
 #> g=1/(rx_expr_21);
 #> tad=tad();
-#> dosenum=dosenum();
#> Needed Covariates:
#> [1] "f_parent_to_A1" "CMT"           
#> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
#> Timing stopped at: 18.05 0.446 18.5

+#> dosenum=dosenum();
#> Needed Covariates:
#> [1] "f_parent_to_A1" "CMT"           
#> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
#> Timing stopped at: 18.44 0.438 18.87

 # Two-component error by variable is possible with both estimation methods
 # Variance by variable is supported by 'saem' and 'focei'
 f_nlmixr_fomc_sfo_saem_obs_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "saem",
   error_model = "obs_tc")
-
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#>  
#> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc,     ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG,     addProp = .addProp, tol = .tol, itmax = .itmax, type = .type,     powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1
#> Timing stopped at: 0.763 0.036 0.799
f_nlmixr_fomc_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "focei",
+
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#>  
#> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc,     ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG,     addProp = .addProp, tol = .tol, itmax = .itmax, type = .type,     powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1
#> Timing stopped at: 0.784 0.028 0.812
f_nlmixr_fomc_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "focei",
   error_model = "obs_tc")
 
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#> → creating full model...
#> → pruning branches (`if`/`else`)...
#> ✔ done
#> → loading into symengine environment...
#> ✔ done
#> → creating full model...
#> → pruning branches (`if`/`else`)...
#> ✔ done
#> → loading into symengine environment...
#> ✔ done
#> → calculate jacobian
#> 
#> → calculate sensitivities
#> 
#> → calculate ∂(f)/∂(η)
#> 
#> → calculate ∂(R²)/∂(η)
#> 
#> → finding duplicate expressions in inner model...
#> 
#> → optimizing duplicate expressions in inner model...
#> 
#> → finding duplicate expressions in EBE model...
#> 
#> → optimizing duplicate expressions in EBE model...
#> 
#> → compiling inner model...
#>  
#> ✔ done
#> → finding duplicate expressions in FD model...
#> 
#> → optimizing duplicate expressions in FD model...
#> 
#> → compiling EBE model...
#>  
#> ✔ done
#> → compiling events FD model...
#>  
#> ✔ done
#> Model:
#> cmt(parent);
 #> cmt(A1);
@@ -4887,9 +4887,9 @@ obtained by fitting the same model to a list of datasets using beta=exp(rx_expr_8);
 #> f_parent=1/(1+exp(-(ETA[3]+THETA[3])));
 #> tad=tad();
-#> dosenum=dosenum();
#> Needed Covariates:
#> [1] "f_parent_to_A1" "CMT"           
#> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
#> Timing stopped at: 8.196 0.388 8.584
f_nlmixr_dfop_sfo_saem_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "saem",
+#> dosenum=dosenum();
#> Needed Covariates:
#> [1] "f_parent_to_A1" "CMT"           
#> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
#> Timing stopped at: 8.157 0.51 8.664
f_nlmixr_dfop_sfo_saem_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "saem",
   error_model = "obs_tc")
-
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#>  
#> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc,     ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG,     addProp = .addProp, tol = .tol, itmax = .itmax, type = .type,     powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1
#> Timing stopped at: 0.843 0.028 0.871
f_nlmixr_dfop_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei",
+
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#>  
#> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc,     ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG,     addProp = .addProp, tol = .tol, itmax = .itmax, type = .type,     powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1
#> Timing stopped at: 0.81 0.045 0.854
f_nlmixr_dfop_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei",
   error_model = "obs_tc")
 
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#> → creating full model...
#> → pruning branches (`if`/`else`)...
#> ✔ done
#> → loading into symengine environment...
#> ✔ done
#> → creating full model...
#> → pruning branches (`if`/`else`)...
#> ✔ done
#> → loading into symengine environment...
#> ✔ done
#> → calculate jacobian
#> 
#> → calculate sensitivities
#> 
#> → calculate ∂(f)/∂(η)
#> 
#> → calculate ∂(R²)/∂(η)
#> 
#> → finding duplicate expressions in inner model...
#> 
#> → optimizing duplicate expressions in inner model...
#> 
#> → finding duplicate expressions in EBE model...
#> 
#> → optimizing duplicate expressions in EBE model...
#> 
#> → compiling inner model...
#>  
#> ✔ done
#> → finding duplicate expressions in FD model...
#> 
#> → optimizing duplicate expressions in FD model...
#> 
#> → compiling EBE model...
#>  
#> ✔ done
#> → compiling events FD model...
#>  
#> ✔ done
#> Model:
#> cmt(parent);
 #> cmt(A1);
@@ -4949,7 +4949,7 @@ obtained by fitting the same model to a list of datasets using f_parent=1/(1+exp(-(ETA[3]+THETA[3])));
 #> g=1/(rx_expr_19);
 #> tad=tad();
-#> dosenum=dosenum();
#> Needed Covariates:
#> [1] "f_parent_to_A1" "CMT"           
#> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
#> Timing stopped at: 17.73 0.411 18.14

+#> dosenum=dosenum();
#> Needed Covariates:
#> [1] "f_parent_to_A1" "CMT"           
#> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
#> Timing stopped at: 17.34 0.397 17.73

 AIC(
   f_nlmixr_sfo_sfo_focei_const$nm,
   f_nlmixr_fomc_sfo_focei_const$nm,
diff --git a/docs/dev/reference/plot.mixed.mmkin-3.png b/docs/dev/reference/plot.mixed.mmkin-3.png
index a9b96726..7e2876b3 100644
Binary files a/docs/dev/reference/plot.mixed.mmkin-3.png and b/docs/dev/reference/plot.mixed.mmkin-3.png differ
diff --git a/docs/dev/reference/plot.mixed.mmkin-4.png b/docs/dev/reference/plot.mixed.mmkin-4.png
index 22219e5e..945c4d41 100644
Binary files a/docs/dev/reference/plot.mixed.mmkin-4.png and b/docs/dev/reference/plot.mixed.mmkin-4.png differ
diff --git a/docs/dev/reference/plot.mixed.mmkin.html b/docs/dev/reference/plot.mixed.mmkin.html
index a4222991..7f3faa90 100644
--- a/docs/dev/reference/plot.mixed.mmkin.html
+++ b/docs/dev/reference/plot.mixed.mmkin.html
@@ -72,7 +72,7 @@
       
       
         mkin
-        1.0.5
+        1.1.0
       
     

 
@@ -296,10 +296,10 @@ corresponding model prediction lines for the different datasets.

 

 f_saem <- saem(f, transformations = "saemix")
 
#> Running main SAEM algorithm
-#> [1] "Fri Jun 11 10:56:37 2021"
+#> [1] "Tue Jul 27 16:30:50 2021"
 #> ....
 #>     Minimisation finished
-#> [1] "Fri Jun 11 10:56:44 2021"
plot(f_saem)
+#> [1] "Tue Jul 27 16:30:58 2021"
plot(f_saem)
 

 f_obs <- mmkin(list("DFOP-SFO" = dfop_sfo), ds, quiet = TRUE, error_model = "obs")
 f_nlmix <- nlmix(f_obs)
diff --git a/docs/dev/reference/reexports.html b/docs/dev/reference/reexports.html
index f5ace044..ac4fa4d9 100644
--- a/docs/dev/reference/reexports.html
+++ b/docs/dev/reference/reexports.html
@@ -81,7 +81,7 @@ below to see their documentation.
       
       
         mkin
-        1.0.5
+        1.1.0
       
     

 
diff --git a/docs/dev/reference/saem-5.png b/docs/dev/reference/saem-5.png
index 8212ec67..d22e7285 100644
Binary files a/docs/dev/reference/saem-5.png and b/docs/dev/reference/saem-5.png differ
diff --git a/docs/dev/reference/saem.html b/docs/dev/reference/saem.html
index 98faad6f..15271c8a 100644
--- a/docs/dev/reference/saem.html
+++ b/docs/dev/reference/saem.html
@@ -74,7 +74,7 @@ Expectation Maximisation algorithm (SAEM)." />
       
       
         mkin
-        1.0.5
+        1.1.0
       
     
 
@@ -158,7 +158,7 @@ Expectation Maximisation algorithm (SAEM).

   object,
   transformations = c("mkin", "saemix"),
   degparms_start = numeric(),
-  test_log_parms = FALSE,
+  test_log_parms = TRUE,
   conf.level = 0.6,
   solution_type = "auto",
   nbiter.saemix = c(300, 100),
@@ -288,27 +288,27 @@ using mmkin.

   state.ini = c(parent = 100), fixed_initials = "parent", quiet = TRUE)
 f_saem_p0_fixed <- saem(f_mmkin_parent_p0_fixed)
 
#> Running main SAEM algorithm
-#> [1] "Fri Jun 11 10:56:49 2021"
+#> [1] "Tue Jul 27 16:31:02 2021"
 #> ....
 #>     Minimisation finished
-#> [1] "Fri Jun 11 10:56:51 2021"

+#> [1] "Tue Jul 27 16:31:04 2021"

 f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE)
 f_saem_sfo <- saem(f_mmkin_parent["SFO", ])
 
#> Running main SAEM algorithm
-#> [1] "Fri Jun 11 10:56:53 2021"
+#> [1] "Tue Jul 27 16:31:06 2021"
 #> ....
 #>     Minimisation finished
-#> [1] "Fri Jun 11 10:56:54 2021"
f_saem_fomc <- saem(f_mmkin_parent["FOMC", ])
+#> [1] "Tue Jul 27 16:31:07 2021"
f_saem_fomc <- saem(f_mmkin_parent["FOMC", ])
 
#> Running main SAEM algorithm
-#> [1] "Fri Jun 11 10:56:54 2021"
+#> [1] "Tue Jul 27 16:31:07 2021"
 #> ....
 #>     Minimisation finished
-#> [1] "Fri Jun 11 10:56:57 2021"
f_saem_dfop <- saem(f_mmkin_parent["DFOP", ])
+#> [1] "Tue Jul 27 16:31:09 2021"
f_saem_dfop <- saem(f_mmkin_parent["DFOP", ])
 
#> Running main SAEM algorithm
-#> [1] "Fri Jun 11 10:56:57 2021"
+#> [1] "Tue Jul 27 16:31:10 2021"
 #> ....
 #>     Minimisation finished
-#> [1] "Fri Jun 11 10:57:00 2021"

+#> [1] "Tue Jul 27 16:31:12 2021"

 # The returned saem.mmkin object contains an SaemixObject, therefore we can use
 # functions from saemix
 library(saemix)
@@ -357,10 +357,10 @@ using mmkin.

 f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = "tc")
 f_saem_fomc_tc <- saem(f_mmkin_parent_tc["FOMC", ])
 
#> Running main SAEM algorithm
-#> [1] "Fri Jun 11 10:57:03 2021"
+#> [1] "Tue Jul 27 16:31:16 2021"
 #> ....
 #>     Minimisation finished
-#> [1] "Fri Jun 11 10:57:09 2021"
compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so)
+#> [1] "Tue Jul 27 16:31:20 2021"
compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so)
 
#> Likelihoods calculated by importance sampling
#>        AIC      BIC
 #> 1 467.7096 464.9757
 #> 2 469.6831 466.5586

@@ -381,15 +381,15 @@ using mmkin.

 # four minutes
 f_saem_sfo_sfo <- saem(f_mmkin["SFO-SFO", ])
 
#> Running main SAEM algorithm
-#> [1] "Fri Jun 11 10:57:12 2021"
+#> [1] "Tue Jul 27 16:31:24 2021"
 #> ....
 #>     Minimisation finished
-#> [1] "Fri Jun 11 10:57:17 2021"
f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ])
+#> [1] "Tue Jul 27 16:31:29 2021"
f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ])
 
#> Running main SAEM algorithm
-#> [1] "Fri Jun 11 10:57:17 2021"
+#> [1] "Tue Jul 27 16:31:30 2021"
 #> ....
 #>     Minimisation finished
-#> [1] "Fri Jun 11 10:57:26 2021"
# We can use print, plot and summary methods to check the results
+#> [1] "Tue Jul 27 16:31:38 2021"
# We can use print, plot and summary methods to check the results
 print(f_saem_dfop_sfo)
 
#> Kinetic nonlinear mixed-effects model fit by SAEM
 #> Structural model:
@@ -405,35 +405,35 @@ using mmkin.

 #> 
 #> Likelihood computed by importance sampling
 #>     AIC   BIC logLik
-#>   841.6 836.5 -407.8
+#>   839.6 834.6 -406.8
 #> 
 #> Fitted parameters:
 #>                     estimate    lower   upper
-#> parent_0            93.76647 91.15312 96.3798
-#> log_k_A1            -6.13235 -8.45788 -3.8068
-#> f_parent_qlogis     -0.97364 -1.36940 -0.5779
-#> log_k1              -2.53176 -3.80372 -1.2598
-#> log_k2              -3.58667 -5.29524 -1.8781
-#> g_qlogis             0.01238 -1.07968  1.1044
-#> Var.parent_0         7.61106 -3.34955 18.5717
-#> Var.log_k_A1         4.64679 -2.73133 12.0249
-#> Var.f_parent_qlogis  0.19693 -0.05498  0.4488
-#> Var.log_k1           2.01717 -0.51980  4.5542
-#> Var.log_k2           3.63412 -0.92964  8.1979
-#> Var.g_qlogis         0.20045 -0.97425  1.3751
-#> a.1                  1.88335  1.66636  2.1004
-#> SD.parent_0          2.75881  0.77234  4.7453
-#> SD.log_k_A1          2.15564  0.44429  3.8670
-#> SD.f_parent_qlogis   0.44377  0.15994  0.7276
-#> SD.log_k1            1.42027  0.52714  2.3134
-#> SD.log_k2            1.90634  0.70934  3.1033
-#> SD.g_qlogis          0.44771 -0.86417  1.7596
plot(f_saem_dfop_sfo)
+#> parent_0            93.80521 91.22487 96.3856
+#> log_k_A1            -6.06244 -8.26517 -3.8597
+#> f_parent_qlogis     -0.97319 -1.37024 -0.5761
+#> log_k1              -2.55394 -4.00815 -1.0997
+#> log_k2              -3.47160 -5.18763 -1.7556
+#> g_qlogis            -0.09324 -1.42737  1.2409
+#> Var.parent_0         7.42157 -3.25683 18.1000
+#> Var.log_k_A1         4.22850 -2.46339 10.9204
+#> Var.f_parent_qlogis  0.19803 -0.05541  0.4515
+#> Var.log_k1           2.28644 -0.86079  5.4337
+#> Var.log_k2           3.35626 -1.14639  7.8589
+#> Var.g_qlogis         0.20084 -1.32516  1.7268
+#> a.1                  1.88399  1.66794  2.1000
+#> SD.parent_0          2.72425  0.76438  4.6841
+#> SD.log_k_A1          2.05633  0.42919  3.6835
+#> SD.f_parent_qlogis   0.44501  0.16025  0.7298
+#> SD.log_k1            1.51210  0.47142  2.5528
+#> SD.log_k2            1.83201  0.60313  3.0609
+#> SD.g_qlogis          0.44816 -1.25437  2.1507
plot(f_saem_dfop_sfo)
 
summary(f_saem_dfop_sfo, data = TRUE)
 
#> saemix version used for fitting:      3.1.9000 
-#> mkin version used for pre-fitting:  1.0.5 
+#> mkin version used for pre-fitting:  1.1.0 
 #> R version used for fitting:         4.1.0 
-#> Date of fit:     Fri Jun 11 10:57:27 2021 
-#> Date of summary: Fri Jun 11 10:57:27 2021 
+#> Date of fit:     Tue Jul 27 16:31:39 2021 
+#> Date of summary: Tue Jul 27 16:31:39 2021 
 #> 
 #> Equations:
 #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -448,13 +448,13 @@ using mmkin.

 #> 
 #> Model predictions using solution type analytical 
 #> 
-#> Fitted in 9.712 s using 300, 100 iterations
+#> Fitted in 9.479 s using 300, 100 iterations
 #> 
 #> Variance model: Constant variance 
 #> 
 #> Mean of starting values for individual parameters:
 #>        parent_0        log_k_A1 f_parent_qlogis          log_k1          log_k2 
-#>         93.8102         -9.7647         -0.9711         -1.8799         -4.2708 
+#>         93.8102         -5.3734         -0.9711         -1.8799         -4.2708 
 #>        g_qlogis 
 #>          0.1356 
 #> 
@@ -465,46 +465,46 @@ using mmkin.

 #> 
 #> Likelihood computed by importance sampling
 #>     AIC   BIC logLik
-#>   841.6 836.5 -407.8
+#>   839.6 834.6 -406.8
 #> 
 #> Optimised parameters:
 #>                     est.  lower   upper
-#> parent_0        93.76647 91.153 96.3798
-#> log_k_A1        -6.13235 -8.458 -3.8068
-#> f_parent_qlogis -0.97364 -1.369 -0.5779
-#> log_k1          -2.53176 -3.804 -1.2598
-#> log_k2          -3.58667 -5.295 -1.8781
-#> g_qlogis         0.01238 -1.080  1.1044
+#> parent_0        93.80521 91.225 96.3856
+#> log_k_A1        -6.06244 -8.265 -3.8597
+#> f_parent_qlogis -0.97319 -1.370 -0.5761
+#> log_k1          -2.55394 -4.008 -1.0997
+#> log_k2          -3.47160 -5.188 -1.7556
+#> g_qlogis        -0.09324 -1.427  1.2409
 #> 
 #> Correlation: 
 #>                 prnt_0 lg__A1 f_prn_ log_k1 log_k2
-#> log_k_A1        -0.013                            
-#> f_parent_qlogis -0.025  0.050                     
-#> log_k1           0.030  0.000 -0.005              
-#> log_k2           0.010  0.005 -0.003  0.032       
-#> g_qlogis        -0.063 -0.015  0.010 -0.167 -0.177
+#> log_k_A1        -0.014                            
+#> f_parent_qlogis -0.025  0.054                     
+#> log_k1           0.027 -0.003 -0.005              
+#> log_k2           0.011  0.005 -0.002 -0.070       
+#> g_qlogis        -0.067 -0.009  0.011 -0.189 -0.171
 #> 
 #> Random effects:
 #>                      est.   lower  upper
-#> SD.parent_0        2.7588  0.7723 4.7453
-#> SD.log_k_A1        2.1556  0.4443 3.8670
-#> SD.f_parent_qlogis 0.4438  0.1599 0.7276
-#> SD.log_k1          1.4203  0.5271 2.3134
-#> SD.log_k2          1.9063  0.7093 3.1033
-#> SD.g_qlogis        0.4477 -0.8642 1.7596
+#> SD.parent_0        2.7243  0.7644 4.6841
+#> SD.log_k_A1        2.0563  0.4292 3.6835
+#> SD.f_parent_qlogis 0.4450  0.1602 0.7298
+#> SD.log_k1          1.5121  0.4714 2.5528
+#> SD.log_k2          1.8320  0.6031 3.0609
+#> SD.g_qlogis        0.4482 -1.2544 2.1507
 #> 
 #> Variance model:
 #>      est. lower upper
-#> a.1 1.883 1.666   2.1
+#> a.1 1.884 1.668   2.1
 #> 
 #> Backtransformed parameters:
 #>                     est.     lower    upper
-#> parent_0       93.766473 9.115e+01 96.37983
-#> k_A1            0.002171 2.122e-04  0.02222
-#> f_parent_to_A1  0.274156 2.027e-01  0.35942
-#> k1              0.079519 2.229e-02  0.28371
-#> k2              0.027691 5.015e-03  0.15288
-#> g               0.503095 2.536e-01  0.75109
+#> parent_0       93.805214 9.122e+01 96.38556
+#> k_A1            0.002329 2.573e-04  0.02107
+#> f_parent_to_A1  0.274245 2.026e-01  0.35982
+#> k1              0.077775 1.817e-02  0.33296
+#> k2              0.031067 5.585e-03  0.17281
+#> g               0.476707 1.935e-01  0.77572
 #> 
 #> Resulting formation fractions:
 #>                 ff
@@ -512,182 +512,182 @@ using mmkin.

 #> parent_sink 0.7258
 #> 
 #> Estimated disappearance times:
-#>          DT50    DT90 DT50back DT50_k1 DT50_k2
-#> parent  14.11   59.53    17.92   8.717   25.03
-#> A1     319.21 1060.38       NA      NA      NA
+#>          DT50  DT90 DT50back DT50_k1 DT50_k2
+#> parent  13.96  55.4    16.68   8.912   22.31
+#> A1     297.65 988.8       NA      NA      NA
 #> 
 #> Data:
-#>          ds   name time observed predicted residual   std standardized
-#>   Dataset 6 parent    0     97.2  95.79523  1.40477 1.883     0.745888
-#>   Dataset 6 parent    0     96.4  95.79523  0.60477 1.883     0.321114
-#>   Dataset 6 parent    3     71.1  71.32042 -0.22042 1.883    -0.117035
-#>   Dataset 6 parent    3     69.2  71.32042 -2.12042 1.883    -1.125873
-#>   Dataset 6 parent    6     58.1  56.45256  1.64744 1.883     0.874739
-#>   Dataset 6 parent    6     56.6  56.45256  0.14744 1.883     0.078288
-#>   Dataset 6 parent   10     44.4  44.48523 -0.08523 1.883    -0.045257
-#>   Dataset 6 parent   10     43.4  44.48523 -1.08523 1.883    -0.576224
-#>   Dataset 6 parent   20     33.3  29.75774  3.54226 1.883     1.880826
-#>   Dataset 6 parent   20     29.2  29.75774 -0.55774 1.883    -0.296141
-#>   Dataset 6 parent   34     17.6  19.35710 -1.75710 1.883    -0.932966
-#>   Dataset 6 parent   34     18.0  19.35710 -1.35710 1.883    -0.720579
-#>   Dataset 6 parent   55     10.5  10.48443  0.01557 1.883     0.008266
-#>   Dataset 6 parent   55      9.3  10.48443 -1.18443 1.883    -0.628895
-#>   Dataset 6 parent   90      4.5   3.78622  0.71378 1.883     0.378995
-#>   Dataset 6 parent   90      4.7   3.78622  0.91378 1.883     0.485188
-#>   Dataset 6 parent  112      3.0   1.99608  1.00392 1.883     0.533048
-#>   Dataset 6 parent  112      3.4   1.99608  1.40392 1.883     0.745435
-#>   Dataset 6 parent  132      2.3   1.11539  1.18461 1.883     0.628990
-#>   Dataset 6 parent  132      2.7   1.11539  1.58461 1.883     0.841377
-#>   Dataset 6     A1    3      4.3   4.66132 -0.36132 1.883    -0.191849
-#>   Dataset 6     A1    3      4.6   4.66132 -0.06132 1.883    -0.032559
-#>   Dataset 6     A1    6      7.0   7.41087 -0.41087 1.883    -0.218157
-#>   Dataset 6     A1    6      7.2   7.41087 -0.21087 1.883    -0.111964
-#>   Dataset 6     A1   10      8.2   9.50878 -1.30878 1.883    -0.694921
-#>   Dataset 6     A1   10      8.0   9.50878 -1.50878 1.883    -0.801114
-#>   Dataset 6     A1   20     11.0  11.69902 -0.69902 1.883    -0.371157
-#>   Dataset 6     A1   20     13.7  11.69902  2.00098 1.883     1.062455
-#>   Dataset 6     A1   34     11.5  12.67784 -1.17784 1.883    -0.625396
-#>   Dataset 6     A1   34     12.7  12.67784  0.02216 1.883     0.011765
-#>   Dataset 6     A1   55     14.9  12.78556  2.11444 1.883     1.122701
-#>   Dataset 6     A1   55     14.5  12.78556  1.71444 1.883     0.910314
-#>   Dataset 6     A1   90     12.1  11.52954  0.57046 1.883     0.302898
-#>   Dataset 6     A1   90     12.3  11.52954  0.77046 1.883     0.409092
-#>   Dataset 6     A1  112      9.9  10.43825 -0.53825 1.883    -0.285793
-#>   Dataset 6     A1  112     10.2  10.43825 -0.23825 1.883    -0.126503
-#>   Dataset 6     A1  132      8.8   9.42830 -0.62830 1.883    -0.333609
-#>   Dataset 6     A1  132      7.8   9.42830 -1.62830 1.883    -0.864577
-#>   Dataset 7 parent    0     93.6  90.91477  2.68523 1.883     1.425772
-#>   Dataset 7 parent    0     92.3  90.91477  1.38523 1.883     0.735514
-#>   Dataset 7 parent    3     87.0  84.76874  2.23126 1.883     1.184726
-#>   Dataset 7 parent    3     82.2  84.76874 -2.56874 1.883    -1.363919
-#>   Dataset 7 parent    7     74.0  77.62735 -3.62735 1.883    -1.926003
-#>   Dataset 7 parent    7     73.9  77.62735 -3.72735 1.883    -1.979100
-#>   Dataset 7 parent   14     64.2  67.52266 -3.32266 1.883    -1.764224
-#>   Dataset 7 parent   14     69.5  67.52266  1.97734 1.883     1.049904
-#>   Dataset 7 parent   30     54.0  52.41949  1.58051 1.883     0.839202
-#>   Dataset 7 parent   30     54.6  52.41949  2.18051 1.883     1.157783
-#>   Dataset 7 parent   60     41.1  39.36582  1.73418 1.883     0.920794
-#>   Dataset 7 parent   60     38.4  39.36582 -0.96582 1.883    -0.512818
-#>   Dataset 7 parent   90     32.5  33.75388 -1.25388 1.883    -0.665771
-#>   Dataset 7 parent   90     35.5  33.75388  1.74612 1.883     0.927132
-#>   Dataset 7 parent  120     28.1  30.41716 -2.31716 1.883    -1.230335
-#>   Dataset 7 parent  120     29.0  30.41716 -1.41716 1.883    -0.752464
-#>   Dataset 7 parent  180     26.5  25.66046  0.83954 1.883     0.445767
-#>   Dataset 7 parent  180     27.6  25.66046  1.93954 1.883     1.029832
-#>   Dataset 7     A1    3      3.9   2.69355  1.20645 1.883     0.640585
-#>   Dataset 7     A1    3      3.1   2.69355  0.40645 1.883     0.215811
-#>   Dataset 7     A1    7      6.9   5.81807  1.08193 1.883     0.574470
-#>   Dataset 7     A1    7      6.6   5.81807  0.78193 1.883     0.415180
-#>   Dataset 7     A1   14     10.4  10.22529  0.17471 1.883     0.092767
-#>   Dataset 7     A1   14      8.3  10.22529 -1.92529 1.883    -1.022265
-#>   Dataset 7     A1   30     14.4  16.75484 -2.35484 1.883    -1.250345
-#>   Dataset 7     A1   30     13.7  16.75484 -3.05484 1.883    -1.622022
-#>   Dataset 7     A1   60     22.1  22.22540 -0.12540 1.883    -0.066583
-#>   Dataset 7     A1   60     22.3  22.22540  0.07460 1.883     0.039610
-#>   Dataset 7     A1   90     27.5  24.38799  3.11201 1.883     1.652376
-#>   Dataset 7     A1   90     25.4  24.38799  1.01201 1.883     0.537344
-#>   Dataset 7     A1  120     28.0  25.53294  2.46706 1.883     1.309927
-#>   Dataset 7     A1  120     26.6  25.53294  1.06706 1.883     0.566572
-#>   Dataset 7     A1  180     25.8  26.94943 -1.14943 1.883    -0.610309
-#>   Dataset 7     A1  180     25.3  26.94943 -1.64943 1.883    -0.875793
-#>   Dataset 8 parent    0     91.9  91.53246  0.36754 1.883     0.195151
-#>   Dataset 8 parent    0     90.8  91.53246 -0.73246 1.883    -0.388914
-#>   Dataset 8 parent    1     64.9  67.73197 -2.83197 1.883    -1.503686
-#>   Dataset 8 parent    1     66.2  67.73197 -1.53197 1.883    -0.813428
-#>   Dataset 8 parent    3     43.5  41.58448  1.91552 1.883     1.017081
-#>   Dataset 8 parent    3     44.1  41.58448  2.51552 1.883     1.335662
-#>   Dataset 8 parent    8     18.3  19.62286 -1.32286 1.883    -0.702395
-#>   Dataset 8 parent    8     18.1  19.62286 -1.52286 1.883    -0.808588
-#>   Dataset 8 parent   14     10.2  10.77819 -0.57819 1.883    -0.306999
-#>   Dataset 8 parent   14     10.8  10.77819  0.02181 1.883     0.011582
-#>   Dataset 8 parent   27      4.9   3.26977  1.63023 1.883     0.865599
-#>   Dataset 8 parent   27      3.3   3.26977  0.03023 1.883     0.016051
-#>   Dataset 8 parent   48      1.6   0.48024  1.11976 1.883     0.594557
-#>   Dataset 8 parent   48      1.5   0.48024  1.01976 1.883     0.541460
-#>   Dataset 8 parent   70      1.1   0.06438  1.03562 1.883     0.549881
-#>   Dataset 8 parent   70      0.9   0.06438  0.83562 1.883     0.443688
-#>   Dataset 8     A1    1      9.6   7.61539  1.98461 1.883     1.053761
-#>   Dataset 8     A1    1      7.7   7.61539  0.08461 1.883     0.044923
-#>   Dataset 8     A1    3     15.0  15.47954 -0.47954 1.883    -0.254622
-#>   Dataset 8     A1    3     15.1  15.47954 -0.37954 1.883    -0.201525
-#>   Dataset 8     A1    8     21.2  20.22616  0.97384 1.883     0.517075
-#>   Dataset 8     A1    8     21.1  20.22616  0.87384 1.883     0.463979
-#>   Dataset 8     A1   14     19.7  20.00067 -0.30067 1.883    -0.159645
-#>   Dataset 8     A1   14     18.9  20.00067 -1.10067 1.883    -0.584419
-#>   Dataset 8     A1   27     17.5  16.38142  1.11858 1.883     0.593928
-#>   Dataset 8     A1   27     15.9  16.38142 -0.48142 1.883    -0.255620
-#>   Dataset 8     A1   48      9.5  10.25357 -0.75357 1.883    -0.400124
-#>   Dataset 8     A1   48      9.8  10.25357 -0.45357 1.883    -0.240833
-#>   Dataset 8     A1   70      6.2   5.95728  0.24272 1.883     0.128878
-#>   Dataset 8     A1   70      6.1   5.95728  0.14272 1.883     0.075781
-#>   Dataset 9 parent    0     99.8  97.47274  2.32726 1.883     1.235697
-#>   Dataset 9 parent    0     98.3  97.47274  0.82726 1.883     0.439246
-#>   Dataset 9 parent    1     77.1  79.72257 -2.62257 1.883    -1.392500
-#>   Dataset 9 parent    1     77.2  79.72257 -2.52257 1.883    -1.339404
-#>   Dataset 9 parent    3     59.0  56.26497  2.73503 1.883     1.452212
-#>   Dataset 9 parent    3     58.1  56.26497  1.83503 1.883     0.974342
-#>   Dataset 9 parent    8     27.4  31.66985 -4.26985 1.883    -2.267151
-#>   Dataset 9 parent    8     29.2  31.66985 -2.46985 1.883    -1.311410
-#>   Dataset 9 parent   14     19.1  22.39789 -3.29789 1.883    -1.751071
-#>   Dataset 9 parent   14     29.6  22.39789  7.20211 1.883     3.824090
-#>   Dataset 9 parent   27     10.1  14.21758 -4.11758 1.883    -2.186301
-#>   Dataset 9 parent   27     18.2  14.21758  3.98242 1.883     2.114537
-#>   Dataset 9 parent   48      4.5   7.27921 -2.77921 1.883    -1.475671
-#>   Dataset 9 parent   48      9.1   7.27921  1.82079 1.883     0.966780
-#>   Dataset 9 parent   70      2.3   3.61470 -1.31470 1.883    -0.698065
-#>   Dataset 9 parent   70      2.9   3.61470 -0.71470 1.883    -0.379485
-#>   Dataset 9 parent   91      2.0   1.85303  0.14697 1.883     0.078038
-#>   Dataset 9 parent   91      1.8   1.85303 -0.05303 1.883    -0.028155
-#>   Dataset 9 parent  120      2.0   0.73645  1.26355 1.883     0.670906
-#>   Dataset 9 parent  120      2.2   0.73645  1.46355 1.883     0.777099
-#>   Dataset 9     A1    1      4.2   3.87843  0.32157 1.883     0.170743
-#>   Dataset 9     A1    1      3.9   3.87843  0.02157 1.883     0.011453
-#>   Dataset 9     A1    3      7.4   8.90535 -1.50535 1.883    -0.799291
-#>   Dataset 9     A1    3      7.9   8.90535 -1.00535 1.883    -0.533807
-#>   Dataset 9     A1    8     14.5  13.75172  0.74828 1.883     0.397312
-#>   Dataset 9     A1    8     13.7  13.75172 -0.05172 1.883    -0.027462
-#>   Dataset 9     A1   14     14.2  14.97541 -0.77541 1.883    -0.411715
-#>   Dataset 9     A1   14     12.2  14.97541 -2.77541 1.883    -1.473650
-#>   Dataset 9     A1   27     13.7  14.94728 -1.24728 1.883    -0.662266
-#>   Dataset 9     A1   27     13.2  14.94728 -1.74728 1.883    -0.927750
-#>   Dataset 9     A1   48     13.6  13.66078 -0.06078 1.883    -0.032272
-#>   Dataset 9     A1   48     15.4  13.66078  1.73922 1.883     0.923470
-#>   Dataset 9     A1   70     10.4  11.84899 -1.44899 1.883    -0.769365
-#>   Dataset 9     A1   70     11.6  11.84899 -0.24899 1.883    -0.132204
-#>   Dataset 9     A1   91     10.0  10.09177 -0.09177 1.883    -0.048727
-#>   Dataset 9     A1   91      9.5  10.09177 -0.59177 1.883    -0.314211
-#>   Dataset 9     A1  120      9.1   7.91379  1.18621 1.883     0.629841
-#>   Dataset 9     A1  120      9.0   7.91379  1.08621 1.883     0.576744
-#>  Dataset 10 parent    0     96.1  93.65257  2.44743 1.883     1.299505
-#>  Dataset 10 parent    0     94.3  93.65257  0.64743 1.883     0.343763
-#>  Dataset 10 parent    8     73.9  77.85906 -3.95906 1.883    -2.102132
-#>  Dataset 10 parent    8     73.9  77.85906 -3.95906 1.883    -2.102132
-#>  Dataset 10 parent   14     69.4  70.17143 -0.77143 1.883    -0.409606
-#>  Dataset 10 parent   14     73.1  70.17143  2.92857 1.883     1.554974
-#>  Dataset 10 parent   21     65.6  63.99188  1.60812 1.883     0.853862
-#>  Dataset 10 parent   21     65.3  63.99188  1.30812 1.883     0.694572
-#>  Dataset 10 parent   41     55.9  54.64292  1.25708 1.883     0.667467
-#>  Dataset 10 parent   41     54.4  54.64292 -0.24292 1.883    -0.128985
-#>  Dataset 10 parent   63     47.0  49.61303 -2.61303 1.883    -1.387433
-#>  Dataset 10 parent   63     49.3  49.61303 -0.31303 1.883    -0.166207
-#>  Dataset 10 parent   91     44.7  45.17807 -0.47807 1.883    -0.253839
-#>  Dataset 10 parent   91     46.7  45.17807  1.52193 1.883     0.808096
-#>  Dataset 10 parent  120     42.1  41.27970  0.82030 1.883     0.435552
-#>  Dataset 10 parent  120     41.3  41.27970  0.02030 1.883     0.010778
-#>  Dataset 10     A1    8      3.3   3.99294 -0.69294 1.883    -0.367929
-#>  Dataset 10     A1    8      3.4   3.99294 -0.59294 1.883    -0.314832
-#>  Dataset 10     A1   14      3.9   5.92756 -2.02756 1.883    -1.076570
-#>  Dataset 10     A1   14      2.9   5.92756 -3.02756 1.883    -1.607538
-#>  Dataset 10     A1   21      6.4   7.47313 -1.07313 1.883    -0.569799
-#>  Dataset 10     A1   21      7.2   7.47313 -0.27313 1.883    -0.145025
-#>  Dataset 10     A1   41      9.1   9.76819 -0.66819 1.883    -0.354786
-#>  Dataset 10     A1   41      8.5   9.76819 -1.26819 1.883    -0.673367
-#>  Dataset 10     A1   63     11.7  10.94733  0.75267 1.883     0.399643
-#>  Dataset 10     A1   63     12.0  10.94733  1.05267 1.883     0.558933
-#>  Dataset 10     A1   91     13.3  11.93773  1.36227 1.883     0.723321
-#>  Dataset 10     A1   91     13.2  11.93773  1.26227 1.883     0.670224
-#>  Dataset 10     A1  120     14.3  12.77666  1.52334 1.883     0.808847
-#>  Dataset 10     A1  120     12.1  12.77666 -0.67666 1.883    -0.359282

+#>          ds   name time observed predicted  residual   std standardized
+#>   Dataset 6 parent    0     97.2  95.75408  1.445920 1.884     0.767479
+#>   Dataset 6 parent    0     96.4  95.75408  0.645920 1.884     0.342847
+#>   Dataset 6 parent    3     71.1  71.22466 -0.124662 1.884    -0.066169
+#>   Dataset 6 parent    3     69.2  71.22466 -2.024662 1.884    -1.074669
+#>   Dataset 6 parent    6     58.1  56.42290  1.677100 1.884     0.890187
+#>   Dataset 6 parent    6     56.6  56.42290  0.177100 1.884     0.094003
+#>   Dataset 6 parent   10     44.4  44.55255 -0.152554 1.884    -0.080974
+#>   Dataset 6 parent   10     43.4  44.55255 -1.152554 1.884    -0.611763
+#>   Dataset 6 parent   20     33.3  29.88846  3.411537 1.884     1.810807
+#>   Dataset 6 parent   20     29.2  29.88846 -0.688463 1.884    -0.365429
+#>   Dataset 6 parent   34     17.6  19.40826 -1.808260 1.884    -0.959805
+#>   Dataset 6 parent   34     18.0  19.40826 -1.408260 1.884    -0.747489
+#>   Dataset 6 parent   55     10.5  10.45560  0.044398 1.884     0.023566
+#>   Dataset 6 parent   55      9.3  10.45560 -1.155602 1.884    -0.613381
+#>   Dataset 6 parent   90      4.5   3.74026  0.759744 1.884     0.403264
+#>   Dataset 6 parent   90      4.7   3.74026  0.959744 1.884     0.509421
+#>   Dataset 6 parent  112      3.0   1.96015  1.039853 1.884     0.551943
+#>   Dataset 6 parent  112      3.4   1.96015  1.439853 1.884     0.764258
+#>   Dataset 6 parent  132      2.3   1.08940  1.210603 1.884     0.642575
+#>   Dataset 6 parent  132      2.7   1.08940  1.610603 1.884     0.854890
+#>   Dataset 6     A1    3      4.3   4.75601 -0.456009 1.884    -0.242045
+#>   Dataset 6     A1    3      4.6   4.75601 -0.156009 1.884    -0.082808
+#>   Dataset 6     A1    6      7.0   7.53839 -0.538391 1.884    -0.285772
+#>   Dataset 6     A1    6      7.2   7.53839 -0.338391 1.884    -0.179614
+#>   Dataset 6     A1   10      8.2   9.64728 -1.447276 1.884    -0.768198
+#>   Dataset 6     A1   10      8.0   9.64728 -1.647276 1.884    -0.874356
+#>   Dataset 6     A1   20     11.0  11.83954 -0.839545 1.884    -0.445621
+#>   Dataset 6     A1   20     13.7  11.83954  1.860455 1.884     0.987509
+#>   Dataset 6     A1   34     11.5  12.81233 -1.312327 1.884    -0.696569
+#>   Dataset 6     A1   34     12.7  12.81233 -0.112327 1.884    -0.059622
+#>   Dataset 6     A1   55     14.9  12.87919  2.020809 1.884     1.072624
+#>   Dataset 6     A1   55     14.5  12.87919  1.620809 1.884     0.860308
+#>   Dataset 6     A1   90     12.1  11.52464  0.575364 1.884     0.305397
+#>   Dataset 6     A1   90     12.3  11.52464  0.775364 1.884     0.411555
+#>   Dataset 6     A1  112      9.9  10.37694 -0.476938 1.884    -0.253153
+#>   Dataset 6     A1  112     10.2  10.37694 -0.176938 1.884    -0.093917
+#>   Dataset 6     A1  132      8.8   9.32474 -0.524742 1.884    -0.278528
+#>   Dataset 6     A1  132      7.8   9.32474 -1.524742 1.884    -0.809317
+#>   Dataset 7 parent    0     93.6  90.16918  3.430816 1.884     1.821040
+#>   Dataset 7 parent    0     92.3  90.16918  2.130816 1.884     1.131014
+#>   Dataset 7 parent    3     87.0  84.05442  2.945583 1.884     1.563483
+#>   Dataset 7 parent    3     82.2  84.05442 -1.854417 1.884    -0.984304
+#>   Dataset 7 parent    7     74.0  77.00960 -3.009596 1.884    -1.597461
+#>   Dataset 7 parent    7     73.9  77.00960 -3.109596 1.884    -1.650540
+#>   Dataset 7 parent   14     64.2  67.15684 -2.956840 1.884    -1.569459
+#>   Dataset 7 parent   14     69.5  67.15684  2.343160 1.884     1.243724
+#>   Dataset 7 parent   30     54.0  52.66290  1.337101 1.884     0.709719
+#>   Dataset 7 parent   30     54.6  52.66290  1.937101 1.884     1.028192
+#>   Dataset 7 parent   60     41.1  40.04995  1.050050 1.884     0.557355
+#>   Dataset 7 parent   60     38.4  40.04995 -1.649950 1.884    -0.875775
+#>   Dataset 7 parent   90     32.5  34.09675 -1.596746 1.884    -0.847535
+#>   Dataset 7 parent   90     35.5  34.09675  1.403254 1.884     0.744832
+#>   Dataset 7 parent  120     28.1  30.12281 -2.022814 1.884    -1.073688
+#>   Dataset 7 parent  120     29.0  30.12281 -1.122814 1.884    -0.595977
+#>   Dataset 7 parent  180     26.5  24.10888  2.391123 1.884     1.269182
+#>   Dataset 7 parent  180     27.6  24.10888  3.491123 1.884     1.853050
+#>   Dataset 7     A1    3      3.9   2.77684  1.123161 1.884     0.596161
+#>   Dataset 7     A1    3      3.1   2.77684  0.323161 1.884     0.171530
+#>   Dataset 7     A1    7      6.9   5.96705  0.932950 1.884     0.495200
+#>   Dataset 7     A1    7      6.6   5.96705  0.632950 1.884     0.335963
+#>   Dataset 7     A1   14     10.4  10.40535 -0.005348 1.884    -0.002839
+#>   Dataset 7     A1   14      8.3  10.40535 -2.105348 1.884    -1.117496
+#>   Dataset 7     A1   30     14.4  16.83722 -2.437216 1.884    -1.293648
+#>   Dataset 7     A1   30     13.7  16.83722 -3.137216 1.884    -1.665200
+#>   Dataset 7     A1   60     22.1  22.15018 -0.050179 1.884    -0.026635
+#>   Dataset 7     A1   60     22.3  22.15018  0.149821 1.884     0.079523
+#>   Dataset 7     A1   90     27.5  24.36286  3.137143 1.884     1.665161
+#>   Dataset 7     A1   90     25.4  24.36286  1.037143 1.884     0.550504
+#>   Dataset 7     A1  120     28.0  25.64064  2.359361 1.884     1.252323
+#>   Dataset 7     A1  120     26.6  25.64064  0.959361 1.884     0.509218
+#>   Dataset 7     A1  180     25.8  27.25486 -1.454858 1.884    -0.772223
+#>   Dataset 7     A1  180     25.3  27.25486 -1.954858 1.884    -1.037617
+#>   Dataset 8 parent    0     91.9  91.72652  0.173479 1.884     0.092081
+#>   Dataset 8 parent    0     90.8  91.72652 -0.926521 1.884    -0.491787
+#>   Dataset 8 parent    1     64.9  67.22810 -2.328104 1.884    -1.235732
+#>   Dataset 8 parent    1     66.2  67.22810 -1.028104 1.884    -0.545706
+#>   Dataset 8 parent    3     43.5  41.46375  2.036251 1.884     1.080820
+#>   Dataset 8 parent    3     44.1  41.46375  2.636251 1.884     1.399293
+#>   Dataset 8 parent    8     18.3  19.83597 -1.535968 1.884    -0.815275
+#>   Dataset 8 parent    8     18.1  19.83597 -1.735968 1.884    -0.921433
+#>   Dataset 8 parent   14     10.2  10.34793 -0.147927 1.884    -0.078518
+#>   Dataset 8 parent   14     10.8  10.34793  0.452073 1.884     0.239956
+#>   Dataset 8 parent   27      4.9   2.67641  2.223595 1.884     1.180260
+#>   Dataset 8 parent   27      3.3   2.67641  0.623595 1.884     0.330997
+#>   Dataset 8 parent   48      1.6   0.30218  1.297822 1.884     0.688870
+#>   Dataset 8 parent   48      1.5   0.30218  1.197822 1.884     0.635791
+#>   Dataset 8 parent   70      1.1   0.03075  1.069248 1.884     0.567545
+#>   Dataset 8 parent   70      0.9   0.03075  0.869248 1.884     0.461388
+#>   Dataset 8     A1    1      9.6   7.74066  1.859342 1.884     0.986918
+#>   Dataset 8     A1    1      7.7   7.74066 -0.040658 1.884    -0.021581
+#>   Dataset 8     A1    3     15.0  15.37549 -0.375495 1.884    -0.199309
+#>   Dataset 8     A1    3     15.1  15.37549 -0.275495 1.884    -0.146230
+#>   Dataset 8     A1    8     21.2  19.95900  1.241003 1.884     0.658711
+#>   Dataset 8     A1    8     21.1  19.95900  1.141003 1.884     0.605632
+#>   Dataset 8     A1   14     19.7  19.92898 -0.228978 1.884    -0.121539
+#>   Dataset 8     A1   14     18.9  19.92898 -1.028978 1.884    -0.546170
+#>   Dataset 8     A1   27     17.5  16.34046  1.159536 1.884     0.615469
+#>   Dataset 8     A1   27     15.9  16.34046 -0.440464 1.884    -0.233793
+#>   Dataset 8     A1   48      9.5  10.12131 -0.621313 1.884    -0.329786
+#>   Dataset 8     A1   48      9.8  10.12131 -0.321313 1.884    -0.170550
+#>   Dataset 8     A1   70      6.2   5.84753  0.352469 1.884     0.187087
+#>   Dataset 8     A1   70      6.1   5.84753  0.252469 1.884     0.134008
+#>   Dataset 9 parent    0     99.8  98.23600  1.564002 1.884     0.830155
+#>   Dataset 9 parent    0     98.3  98.23600  0.064002 1.884     0.033972
+#>   Dataset 9 parent    1     77.1  79.68007 -2.580074 1.884    -1.369475
+#>   Dataset 9 parent    1     77.2  79.68007 -2.480074 1.884    -1.316396
+#>   Dataset 9 parent    3     59.0  55.81142  3.188584 1.884     1.692465
+#>   Dataset 9 parent    3     58.1  55.81142  2.288584 1.884     1.214755
+#>   Dataset 9 parent    8     27.4  31.81995 -4.419948 1.884    -2.346060
+#>   Dataset 9 parent    8     29.2  31.81995 -2.619948 1.884    -1.390640
+#>   Dataset 9 parent   14     19.1  22.78328 -3.683282 1.884    -1.955046
+#>   Dataset 9 parent   14     29.6  22.78328  6.816718 1.884     3.618240
+#>   Dataset 9 parent   27     10.1  14.15172 -4.051720 1.884    -2.150609
+#>   Dataset 9 parent   27     18.2  14.15172  4.048280 1.884     2.148783
+#>   Dataset 9 parent   48      4.5   6.86094 -2.360941 1.884    -1.253162
+#>   Dataset 9 parent   48      9.1   6.86094  2.239059 1.884     1.188468
+#>   Dataset 9 parent   70      2.3   3.21580 -0.915798 1.884    -0.486096
+#>   Dataset 9 parent   70      2.9   3.21580 -0.315798 1.884    -0.167622
+#>   Dataset 9 parent   91      2.0   1.56010  0.439897 1.884     0.233492
+#>   Dataset 9 parent   91      1.8   1.56010  0.239897 1.884     0.127335
+#>   Dataset 9 parent  120      2.0   0.57458  1.425424 1.884     0.756600
+#>   Dataset 9 parent  120      2.2   0.57458  1.625424 1.884     0.862757
+#>   Dataset 9     A1    1      4.2   4.01796  0.182037 1.884     0.096623
+#>   Dataset 9     A1    1      3.9   4.01796 -0.117963 1.884    -0.062613
+#>   Dataset 9     A1    3      7.4   9.08527 -1.685270 1.884    -0.894523
+#>   Dataset 9     A1    3      7.9   9.08527 -1.185270 1.884    -0.629129
+#>   Dataset 9     A1    8     14.5  13.75054  0.749457 1.884     0.397804
+#>   Dataset 9     A1    8     13.7  13.75054 -0.050543 1.884    -0.026827
+#>   Dataset 9     A1   14     14.2  14.91180 -0.711804 1.884    -0.377818
+#>   Dataset 9     A1   14     12.2  14.91180 -2.711804 1.884    -1.439396
+#>   Dataset 9     A1   27     13.7  14.97813 -1.278129 1.884    -0.678417
+#>   Dataset 9     A1   27     13.2  14.97813 -1.778129 1.884    -0.943812
+#>   Dataset 9     A1   48     13.6  13.75574 -0.155745 1.884    -0.082668
+#>   Dataset 9     A1   48     15.4  13.75574  1.644255 1.884     0.872753
+#>   Dataset 9     A1   70     10.4  11.92861 -1.528608 1.884    -0.811369
+#>   Dataset 9     A1   70     11.6  11.92861 -0.328608 1.884    -0.174422
+#>   Dataset 9     A1   91     10.0  10.14395 -0.143947 1.884    -0.076405
+#>   Dataset 9     A1   91      9.5  10.14395 -0.643947 1.884    -0.341800
+#>   Dataset 9     A1  120      9.1   7.93869  1.161307 1.884     0.616409
+#>   Dataset 9     A1  120      9.0   7.93869  1.061307 1.884     0.563330
+#>  Dataset 10 parent    0     96.1  93.65914  2.440862 1.884     1.295583
+#>  Dataset 10 parent    0     94.3  93.65914  0.640862 1.884     0.340163
+#>  Dataset 10 parent    8     73.9  77.83065 -3.930647 1.884    -2.086344
+#>  Dataset 10 parent    8     73.9  77.83065 -3.930647 1.884    -2.086344
+#>  Dataset 10 parent   14     69.4  70.15862 -0.758619 1.884    -0.402667
+#>  Dataset 10 parent   14     73.1  70.15862  2.941381 1.884     1.561253
+#>  Dataset 10 parent   21     65.6  64.00840  1.591600 1.884     0.844804
+#>  Dataset 10 parent   21     65.3  64.00840  1.291600 1.884     0.685567
+#>  Dataset 10 parent   41     55.9  54.71192  1.188076 1.884     0.630618
+#>  Dataset 10 parent   41     54.4  54.71192 -0.311924 1.884    -0.165566
+#>  Dataset 10 parent   63     47.0  49.66775 -2.667747 1.884    -1.416011
+#>  Dataset 10 parent   63     49.3  49.66775 -0.367747 1.884    -0.195196
+#>  Dataset 10 parent   91     44.7  45.17119 -0.471186 1.884    -0.250101
+#>  Dataset 10 parent   91     46.7  45.17119  1.528814 1.884     0.811478
+#>  Dataset 10 parent  120     42.1  41.20430  0.895699 1.884     0.475427
+#>  Dataset 10 parent  120     41.3  41.20430  0.095699 1.884     0.050796
+#>  Dataset 10     A1    8      3.3   4.00920 -0.709204 1.884    -0.376438
+#>  Dataset 10     A1    8      3.4   4.00920 -0.609204 1.884    -0.323359
+#>  Dataset 10     A1   14      3.9   5.94267 -2.042668 1.884    -1.084226
+#>  Dataset 10     A1   14      2.9   5.94267 -3.042668 1.884    -1.615015
+#>  Dataset 10     A1   21      6.4   7.48222 -1.082219 1.884    -0.574430
+#>  Dataset 10     A1   21      7.2   7.48222 -0.282219 1.884    -0.149799
+#>  Dataset 10     A1   41      9.1   9.76246 -0.662460 1.884    -0.351626
+#>  Dataset 10     A1   41      8.5   9.76246 -1.262460 1.884    -0.670100
+#>  Dataset 10     A1   63     11.7  10.93972  0.760278 1.884     0.403547
+#>  Dataset 10     A1   63     12.0  10.93972  1.060278 1.884     0.562784
+#>  Dataset 10     A1   91     13.3  11.93666  1.363337 1.884     0.723645
+#>  Dataset 10     A1   91     13.2  11.93666  1.263337 1.884     0.670566
+#>  Dataset 10     A1  120     14.3  12.78218  1.517817 1.884     0.805641
+#>  Dataset 10     A1  120     12.1  12.78218 -0.682183 1.884    -0.362095

 # The following takes about 6 minutes
 #f_saem_dfop_sfo_deSolve <- saem(f_mmkin["DFOP-SFO", ], solution_type = "deSolve",
 #  control = list(nbiter.saemix = c(200, 80), nbdisplay = 10))
diff --git a/docs/dev/reference/summary.nlmixr.mmkin.html b/docs/dev/reference/summary.nlmixr.mmkin.html
index 0fead0df..373ce75f 100644
--- a/docs/dev/reference/summary.nlmixr.mmkin.html
+++ b/docs/dev/reference/summary.nlmixr.mmkin.html
@@ -76,7 +76,7 @@ endpoints such as formation fractions and DT50 values. Optionally
       
       
         mkin
-        1.0.5
+        1.1.0
       
     

 
@@ -258,737 +258,73 @@ nlmixr authors for the parts inherited from nlmixr.

   quiet = TRUE, error_model = "tc", cores = 5)
 f_saemix_dfop_sfo <- mkin::saem(f_mmkin_dfop_sfo)
 
#> Running main SAEM algorithm
-#> [1] "Fri Jun 11 10:57:31 2021"
+#> [1] "Tue Jul 27 16:31:43 2021"
 #> ....
 #>     Minimisation finished
-#> [1] "Fri Jun 11 10:57:43 2021"
f_nlme_dfop_sfo <- mkin::nlme(f_mmkin_dfop_sfo)
+#> [1] "Tue Jul 27 16:31:55 2021"
f_nlme_dfop_sfo <- mkin::nlme(f_mmkin_dfop_sfo)
 
#> Warning: Iteration 4, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)
#> Warning: Iteration 6, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)
f_nlmixr_dfop_sfo_saem <- nlmixr(f_mmkin_dfop_sfo, est = "saem")
-
#> With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#>  
#> → generate SAEM model
#> ✔ done
#> 1:    1.0127e+02  -3.8515e+00  -2.0719e+00  -3.7271e+00  -1.9335e+00   4.0311e-01   6.9594e+00   1.5021e-01   5.3947e-01   1.9686e-01   3.7429e-01   5.4209e-01   8.4121e+00   7.3391e-02   7.1185e+00   2.5869e-01
-#> 2:    1.0136e+02  -3.8005e+00  -2.3424e+00  -4.0759e+00  -1.6475e+00   1.1598e-01   6.6115e+00   1.4406e-01   5.1249e-01   1.8701e-01   3.5786e-01   5.1499e-01   4.9102e+00   6.2829e-02   4.7230e+00   7.8901e-02
-#> 3:    1.0126e+02  -4.0285e+00  -2.3629e+00  -4.1271e+00  -1.1733e+00   1.7634e-02   6.2809e+00   1.6892e-01   4.8687e-01   1.7766e-01   3.3997e-01   4.8924e-01   3.2256e+00   6.6693e-02   3.3261e+00   8.7190e-02
-#> 4:    1.0105e+02  -4.0894e+00  -2.5516e+00  -4.1037e+00  -1.0816e+00   4.5377e-02   5.9668e+00   1.6048e-01   4.6252e-01   1.6878e-01   3.2297e-01   4.6478e-01   2.4343e+00   7.0557e-02   2.2610e+00   9.2498e-02
-#> 5:    1.0101e+02  -4.1364e+00  -2.4605e+00  -4.0737e+00  -1.0920e+00  -4.7953e-03   5.9593e+00   1.5245e-01   4.3940e-01   1.8078e-01   3.0682e-01   5.4688e-01   1.7424e+00   7.4776e-02   1.5144e+00   1.0787e-01
-#> 6:    1.0042e+02  -4.0933e+00  -2.4472e+00  -4.1090e+00  -9.7996e-01  -9.0472e-02   6.0175e+00   1.4483e-01   4.1743e-01   1.8824e-01   2.9148e-01   5.3033e-01   1.5545e+00   6.8588e-02   1.3401e+00   9.8865e-02
-#> 7:    1.0078e+02  -4.0911e+00  -2.4335e+00  -4.0758e+00  -9.9422e-01  -7.8849e-02   6.6318e+00   1.3759e-01   3.9656e-01   1.7882e-01   2.7691e-01   5.0381e-01   1.3780e+00   6.9978e-02   1.1346e+00   9.6162e-02
-#> 8:    1.0077e+02  -4.0196e+00  -2.4345e+00  -4.0444e+00  -9.3483e-01  -1.1032e-01   6.3002e+00   1.3071e-01   3.7673e-01   1.6988e-01   2.6306e-01   4.8191e-01   1.1774e+00   7.4232e-02   1.0270e+00   9.5616e-02
-#> 9:    1.0118e+02  -4.0436e+00  -2.4649e+00  -4.0207e+00  -8.9829e-01  -1.7784e-01   5.9852e+00   1.2417e-01   3.5789e-01   1.6139e-01   2.4991e-01   5.5466e-01   1.1040e+00   7.1515e-02   1.0342e+00   9.3972e-02
-#> 10:    1.0143e+02  -4.0523e+00  -2.3737e+00  -4.0184e+00  -9.1167e-01  -2.3828e-01   5.8520e+00   1.1797e-01   3.4196e-01   1.5332e-01   2.3741e-01   5.2849e-01   1.0510e+00   7.5719e-02   1.0638e+00   9.3973e-02
-#> 11:    1.0119e+02  -4.0699e+00  -2.3680e+00  -4.0191e+00  -9.4858e-01  -1.7310e-01   6.9958e+00   1.1207e-01   3.6891e-01   1.4565e-01   2.2554e-01   5.0206e-01   1.0247e+00   7.5497e-02   1.0292e+00   9.3707e-02
-#> 12:    1.0121e+02  -4.0189e+00  -2.4198e+00  -4.0139e+00  -9.1693e-01  -2.0613e-01   6.6460e+00   1.0646e-01   3.5046e-01   1.3837e-01   2.1427e-01   5.7696e-01   1.1046e+00   7.6090e-02   9.3689e-01   9.4115e-02
-#> 13:    1.0083e+02  -4.0451e+00  -2.4395e+00  -4.0235e+00  -9.4535e-01  -1.4723e-01   6.3137e+00   1.0114e-01   3.3294e-01   1.3145e-01   2.0355e-01   5.4811e-01   1.0360e+00   7.3381e-02   9.7078e-01   9.1659e-02
-#> 14:    1.0056e+02  -4.0401e+00  -2.4045e+00  -4.0054e+00  -9.4191e-01  -1.3928e-01   5.9980e+00   9.6084e-02   3.4934e-01   1.2488e-01   1.9338e-01   5.2071e-01   1.0303e+00   7.7118e-02   8.8372e-01   9.0469e-02
-#> 15:    1.0070e+02  -4.0388e+00  -2.4210e+00  -4.0113e+00  -9.1136e-01  -1.2702e-01   5.6981e+00   9.1279e-02   3.3187e-01   1.1864e-01   1.8371e-01   4.9467e-01   1.0486e+00   7.2427e-02   7.8179e-01   9.1572e-02
-#> 16:    1.0078e+02  -4.0175e+00  -2.4766e+00  -4.0191e+00  -9.0733e-01  -1.1952e-01   5.4132e+00   8.6716e-02   3.1528e-01   1.1270e-01   1.7452e-01   4.8928e-01   9.7799e-01   8.1464e-02   8.2935e-01   8.6520e-02
-#> 17:    1.0069e+02  -4.0533e+00  -2.5110e+00  -4.0294e+00  -9.1841e-01  -6.8363e-03   5.1426e+00   8.2380e-02   2.9952e-01   1.0707e-01   1.6580e-01   4.6482e-01   9.1609e-01   8.1008e-02   8.1783e-01   8.8818e-02
-#> 18:    99.9647   -4.0672   -2.5327   -4.0416   -0.9273    0.0097    4.8854    0.0783    0.2970    0.1280    0.1941    0.5053    0.9306    0.0764    0.8097    0.0881
-#> 19:    1.0027e+02  -4.0667e+00  -2.4653e+00  -4.0579e+00  -9.2776e-01   3.0417e-02   4.6412e+00   7.4348e-02   3.3694e-01   1.2164e-01   1.8435e-01   5.1797e-01   9.7386e-01   7.4954e-02   7.9297e-01   8.9915e-02
-#> 20:    1.0006e+02  -4.0935e+00  -2.4804e+00  -4.0721e+00  -9.3737e-01   1.9496e-02   4.4091e+00   7.0630e-02   3.3728e-01   1.2544e-01   1.7513e-01   6.0925e-01   1.0232e+00   7.4618e-02   7.9988e-01   8.9642e-02
-#> 21:    1.0043e+02  -4.0542e+00  -2.5168e+00  -4.0623e+00  -9.1553e-01   3.9474e-02   4.1887e+00   6.7099e-02   3.4553e-01   1.1917e-01   1.6638e-01   6.0827e-01   1.0155e+00   8.0771e-02   7.8424e-01   8.6213e-02
-#> 22:    1.0049e+02  -4.0449e+00  -2.5082e+00  -4.0849e+00  -9.2553e-01   4.5424e-02   3.9792e+00   6.3744e-02   3.2825e-01   1.2365e-01   1.5806e-01   5.8922e-01   8.2860e-01   8.3384e-02   8.2525e-01   8.9218e-02
-#> 23:    1.0067e+02  -4.0411e+00  -2.5460e+00  -4.0736e+00  -9.2578e-01   5.2422e-02   3.7803e+00   6.0557e-02   3.1661e-01   1.2306e-01   1.5016e-01   5.8274e-01   9.3412e-01   8.0508e-02   8.1829e-01   8.6377e-02
-#> 24:    1.0091e+02  -4.0314e+00  -2.5298e+00  -4.0566e+00  -8.9743e-01   3.7634e-02   3.5913e+00   5.7529e-02   3.5267e-01   1.2194e-01   1.4265e-01   5.5360e-01   9.6271e-01   7.6960e-02   8.8466e-01   8.5693e-02
-#> 25:    1.0100e+02  -4.0442e+00  -2.5399e+00  -4.0568e+00  -8.9494e-01   1.7415e-02   3.4117e+00   5.4652e-02   3.3504e-01   1.2781e-01   1.3552e-01   5.2592e-01   9.6040e-01   7.7299e-02   8.9561e-01   8.6893e-02
-#> 26:    1.0111e+02  -4.0354e+00  -2.5182e+00  -4.0899e+00  -9.0799e-01   7.6464e-02   4.8614e+00   5.1920e-02   3.1829e-01   1.2142e-01   1.3110e-01   4.9963e-01   9.6997e-01   7.4932e-02   8.2521e-01   9.3659e-02
-#> 27:    1.0159e+02  -4.0653e+00  -2.4934e+00  -4.0803e+00  -9.5632e-01   2.8659e-03   4.6184e+00   4.9324e-02   3.0237e-01   1.1535e-01   1.4743e-01   4.7465e-01   9.4314e-01   7.7860e-02   8.9820e-01   8.8210e-02
-#> 28:    1.0154e+02  -4.0487e+00  -2.4844e+00  -4.0511e+00  -9.6473e-01  -4.7382e-02   4.3874e+00   4.6858e-02   3.2049e-01   1.0958e-01   1.5243e-01   4.5091e-01   9.8808e-01   7.4786e-02   8.6833e-01   8.8720e-02
-#> 29:    1.0144e+02  -4.0414e+00  -2.4105e+00  -4.0504e+00  -9.4039e-01  -3.6753e-02   4.1681e+00   4.4515e-02   3.2754e-01   1.0410e-01   1.4940e-01   4.2837e-01   9.5520e-01   7.8507e-02   8.2408e-01   8.5998e-02
-#> 30:    1.0137e+02  -4.0292e+00  -2.4174e+00  -4.0382e+00  -9.3180e-01  -7.1482e-02   5.4636e+00   4.2289e-02   3.2074e-01   9.8896e-02   1.6877e-01   4.0695e-01   8.8153e-01   7.5106e-02   8.5239e-01   8.8266e-02
-#> 31:    1.0105e+02  -4.0387e+00  -2.4368e+00  -4.0346e+00  -9.1098e-01  -5.4730e-02   5.1904e+00   4.0175e-02   3.0470e-01   9.3951e-02   1.6034e-01   3.8660e-01   8.7853e-01   8.0278e-02   8.7981e-01   8.6404e-02
-#> 32:    1.0147e+02  -4.0435e+00  -2.4530e+00  -4.0365e+00  -9.1241e-01  -7.1281e-02   4.9309e+00   3.8166e-02   2.8947e-01   9.4694e-02   1.7475e-01   3.6727e-01   8.7005e-01   8.1398e-02   8.7784e-01   8.8976e-02
-#> 33:    1.0144e+02  -4.0092e+00  -2.4279e+00  -4.0090e+00  -8.8656e-01  -1.4017e-01   5.2945e+00   3.6258e-02   2.9770e-01   1.0169e-01   1.6601e-01   3.4891e-01   9.2202e-01   7.8841e-02   8.7551e-01   8.4011e-02
-#> 34:    1.0157e+02  -3.9839e+00  -2.4469e+00  -4.0180e+00  -8.3877e-01  -1.4664e-01   6.3506e+00   3.4445e-02   2.8282e-01   1.0831e-01   1.6850e-01   3.3146e-01   8.4403e-01   7.9056e-02   8.4620e-01   8.6363e-02
-#> 35:    1.0149e+02  -3.9928e+00  -2.4771e+00  -4.0106e+00  -8.6974e-01  -1.4219e-01   6.2039e+00   3.2722e-02   2.8123e-01   1.1283e-01   1.6008e-01   3.1489e-01   9.1308e-01   7.8685e-02   7.8939e-01   8.7289e-02
-#> 36:    1.0162e+02  -4.0099e+00  -2.4822e+00  -3.9880e+00  -8.7959e-01  -1.3237e-01   5.8937e+00   3.1086e-02   3.2200e-01   1.0719e-01   1.6077e-01   2.9914e-01   9.0821e-01   8.4066e-02   7.5559e-01   8.4838e-02
-#> 37:    1.0102e+02  -3.9962e+00  -2.4852e+00  -3.9954e+00  -8.8307e-01  -9.2070e-02   5.5991e+00   2.9532e-02   3.3713e-01   1.0183e-01   1.5333e-01   2.8419e-01   8.3918e-01   8.5231e-02   7.6007e-01   8.9541e-02
-#> 38:    1.0102e+02  -3.9987e+00  -2.5129e+00  -3.9833e+00  -8.7454e-01  -1.6469e-01   5.3191e+00   2.8055e-02   3.2027e-01   1.0792e-01   1.4707e-01   2.6998e-01   9.1490e-01   8.4715e-02   7.6778e-01   8.9241e-02
-#> 39:    1.0054e+02  -3.9875e+00  -2.4301e+00  -3.9797e+00  -8.7222e-01  -1.9597e-01   7.3800e+00   2.6653e-02   3.0426e-01   1.0801e-01   1.4393e-01   2.5648e-01   9.5901e-01   7.8320e-02   8.1559e-01   9.2429e-02
-#> 40:    1.0077e+02  -4.0057e+00  -2.4630e+00  -3.9849e+00  -8.6788e-01  -1.9606e-01   7.0110e+00   2.5320e-02   3.0385e-01   1.3164e-01   1.4567e-01   3.0284e-01   9.7123e-01   7.6328e-02   8.3681e-01   8.9349e-02
-#> 41:    1.0069e+02  -4.0143e+00  -2.3805e+00  -3.9962e+00  -8.7503e-01  -1.8532e-01   6.6604e+00   2.4054e-02   3.0707e-01   1.4668e-01   1.5021e-01   3.0404e-01   1.0072e+00   7.3629e-02   9.4494e-01   8.4745e-02
-#> 42:    1.0073e+02  -3.9861e+00  -2.4464e+00  -3.9919e+00  -8.7912e-01  -1.8435e-01   6.3274e+00   2.2851e-02   2.9171e-01   1.3935e-01   1.5080e-01   2.8883e-01   9.6502e-01   7.7470e-02   9.4221e-01   8.2459e-02
-#> 43:    1.0104e+02  -3.9881e+00  -2.4156e+00  -3.9688e+00  -8.9448e-01  -2.3739e-01   6.0110e+00   2.1709e-02   2.7713e-01   1.3238e-01   1.5603e-01   2.7439e-01   9.7714e-01   7.1720e-02   8.5890e-01   8.6635e-02
-#> 44:    1.0084e+02  -4.0117e+00  -2.4455e+00  -3.9753e+00  -8.8716e-01  -2.0112e-01   5.7105e+00   2.0623e-02   2.6327e-01   1.2741e-01   1.5200e-01   2.6067e-01   9.3289e-01   8.0543e-02   8.5055e-01   8.2921e-02
-#> 45:    1.0071e+02  -3.9996e+00  -2.4359e+00  -3.9764e+00  -9.1082e-01  -2.4578e-01   5.4250e+00   1.9592e-02   2.5011e-01   1.3254e-01   1.6132e-01   2.8273e-01   9.5805e-01   7.7734e-02   7.8171e-01   8.4571e-02
-#> 46:    1.0018e+02  -4.0077e+00  -2.4835e+00  -3.9739e+00  -8.6079e-01  -1.6592e-01   5.1537e+00   1.8613e-02   2.3760e-01   1.3830e-01   1.5392e-01   3.0295e-01   1.0931e+00   7.3274e-02   8.9544e-01   8.8388e-02
-#> 47:    99.9834   -3.9991   -2.5292   -3.9863   -0.8820   -0.0796    4.8960    0.0177    0.2348    0.1376    0.1639    0.2878    0.9864    0.0837    0.9094    0.0832
-#> 48:    99.9155   -4.0224   -2.5422   -3.9854   -0.8719   -0.0750    4.6512    0.0184    0.2251    0.1307    0.1596    0.2734    0.9841    0.0835    0.8696    0.0843
-#> 49:    99.6136   -4.0397   -2.5172   -4.0115   -0.8774   -0.0922    5.2402    0.0175    0.2558    0.1242    0.1551    0.2597    0.9060    0.0816    0.8365    0.0869
-#> 50:    99.4747   -4.0542   -2.4192   -3.9834   -0.9041   -0.1798    4.9782    0.0219    0.2695    0.1234    0.1474    0.2468    0.9269    0.0783    0.8593    0.0854
-#> 51:    99.3401   -4.0386   -2.3951   -3.9661   -0.9181   -0.1887    4.7574    0.0213    0.2746    0.1522    0.1400    0.2344    0.9901    0.0781    0.8863    0.0928
-#> 52:    99.7109   -4.0509   -2.4227   -3.9770   -0.9247   -0.1431    4.9004    0.0203    0.2688    0.1446    0.1330    0.2227    0.8999    0.0791    1.0265    0.0890
-#> 53:    99.6496   -4.0397   -2.4398   -3.9752   -0.9193   -0.2119    5.1106    0.0193    0.2795    0.1527    0.1325    0.2116    0.8949    0.0788    0.9447    0.0872
-#> 54:    99.9071   -4.0211   -2.3887   -3.9812   -0.9233   -0.1946    5.0887    0.0183    0.2763    0.1450    0.1365    0.2010    0.8793    0.0875    0.8643    0.0903
-#> 55:    1.0012e+02  -4.0401e+00  -2.4203e+00  -3.9511e+00  -9.0712e-01  -2.5566e-01   5.7301e+00   1.7375e-02   2.7324e-01   1.3780e-01   1.6204e-01   1.9094e-01   9.7803e-01   7.6146e-02   9.0756e-01   8.7636e-02
-#> 56:    1.0032e+02  -4.0207e+00  -2.4263e+00  -3.9533e+00  -8.7574e-01  -2.3076e-01   6.5321e+00   1.6507e-02   3.0821e-01   1.3091e-01   1.5394e-01   1.8139e-01   8.8520e-01   7.6350e-02   9.2796e-01   8.5283e-02
-#> 57:    1.0028e+02  -4.0037e+00  -2.4301e+00  -3.9655e+00  -8.8472e-01  -1.8969e-01   9.8969e+00   1.5681e-02   2.9280e-01   1.2436e-01   1.4624e-01   1.7232e-01   9.2902e-01   7.4974e-02   8.9204e-01   8.4563e-02
-#> 58:    1.0048e+02  -3.9928e+00  -2.4961e+00  -3.9709e+00  -9.0263e-01  -1.4516e-01   9.4021e+00   1.6151e-02   2.7816e-01   1.1814e-01   1.4165e-01   1.6370e-01   9.5145e-01   8.0233e-02   8.2896e-01   8.3498e-02
-#> 59:    1.0060e+02  -4.0181e+00  -2.4963e+00  -3.9751e+00  -9.0684e-01  -1.1186e-01   8.9320e+00   1.9914e-02   3.0097e-01   1.1224e-01   1.4109e-01   1.5552e-01   9.9121e-01   7.3120e-02   8.6454e-01   8.2239e-02
-#> 60:    1.0047e+02  -3.9976e+00  -2.4797e+00  -3.9780e+00  -8.9328e-01  -1.0814e-01   8.4854e+00   1.8918e-02   3.2275e-01   1.1591e-01   1.3404e-01   1.4774e-01   9.6968e-01   7.4984e-02   8.9831e-01   8.1655e-02
-#> 61:    1.0040e+02  -4.0068e+00  -2.5217e+00  -3.9844e+00  -8.6447e-01  -1.0567e-01   8.0611e+00   1.7972e-02   3.1372e-01   1.1011e-01   1.2973e-01   1.4036e-01   9.1698e-01   7.8118e-02   9.1811e-01   8.4420e-02
-#> 62:    1.0076e+02  -4.0080e+00  -2.4931e+00  -3.9623e+00  -8.9789e-01  -8.3896e-02   7.6580e+00   1.7073e-02   3.0460e-01   1.1254e-01   1.2324e-01   1.3334e-01   9.9032e-01   7.7618e-02   8.3808e-01   8.5031e-02
-#> 63:    1.0064e+02  -4.0129e+00  -2.4731e+00  -3.9561e+00  -8.9103e-01  -8.8987e-02   7.2751e+00   1.6220e-02   2.8944e-01   1.1647e-01   1.4845e-01   1.2667e-01   1.0745e+00   7.6375e-02   8.4316e-01   8.6681e-02
-#> 64:    1.0098e+02  -4.0094e+00  -2.4541e+00  -3.9604e+00  -9.1524e-01  -9.3413e-02   6.9114e+00   1.5409e-02   2.7497e-01   1.2065e-01   1.7095e-01   1.2034e-01   1.0963e+00   7.8304e-02   8.7104e-01   8.5727e-02
-#> 65:    1.0070e+02  -4.0433e+00  -2.4793e+00  -3.9722e+00  -9.3012e-01  -6.5917e-02   6.5658e+00   1.4638e-02   2.7040e-01   1.1462e-01   1.9067e-01   1.1432e-01   9.7444e-01   8.4510e-02   8.7028e-01   8.6292e-02
-#> 66:    1.0049e+02  -4.0656e+00  -2.4659e+00  -3.9898e+00  -9.4278e-01  -7.5929e-02   6.2375e+00   1.3906e-02   2.9347e-01   1.1997e-01   1.8114e-01   1.0860e-01   9.9830e-01   8.0902e-02   9.3551e-01   8.5261e-02
-#> 67:    1.0046e+02  -4.0477e+00  -2.4685e+00  -3.9907e+00  -9.1503e-01  -9.8019e-02   5.9256e+00   1.3211e-02   3.2166e-01   1.1506e-01   1.7208e-01   1.0317e-01   8.6453e-01   9.0533e-02   8.3598e-01   8.6343e-02
-#> 68:    1.0077e+02  -4.0575e+00  -2.4709e+00  -3.9523e+00  -9.2903e-01  -8.1099e-02   5.6294e+00   1.2818e-02   3.1005e-01   1.3665e-01   1.6347e-01   9.8015e-02   9.0181e-01   8.7058e-02   8.4937e-01   8.3248e-02
-#> 69:    1.0086e+02  -4.0626e+00  -2.3922e+00  -3.9557e+00  -9.6741e-01  -3.5986e-02   5.3479e+00   1.2844e-02   3.3024e-01   1.2982e-01   1.5530e-01   9.3115e-02   9.8180e-01   8.3132e-02   8.6549e-01   8.8939e-02
-#> 70:    1.0082e+02  -4.0640e+00  -2.4449e+00  -3.9787e+00  -9.5159e-01  -3.2904e-02   5.0805e+00   1.4346e-02   3.1373e-01   1.2333e-01   1.4754e-01   8.8459e-02   1.0129e+00   7.4856e-02   8.6688e-01   8.4769e-02
-#> 71:    1.0072e+02  -4.0642e+00  -2.5069e+00  -3.9493e+00  -9.3453e-01  -4.4116e-02   4.8265e+00   1.3628e-02   3.0428e-01   1.2122e-01   1.4091e-01   8.4036e-02   1.0454e+00   7.7023e-02   8.9566e-01   8.1639e-02
-#> 72:    1.0049e+02  -4.0609e+00  -2.4472e+00  -3.9669e+00  -9.3972e-01  -7.7498e-02   4.5852e+00   1.4441e-02   3.2552e-01   1.3911e-01   1.4144e-01   8.1899e-02   1.0114e+00   7.7019e-02   8.2312e-01   8.2494e-02
-#> 73:    1.0022e+02  -4.0598e+00  -2.4410e+00  -3.9952e+00  -9.2810e-01  -1.1309e-01   4.3559e+00   1.3719e-02   3.3556e-01   1.3303e-01   1.4990e-01   1.1303e-01   9.6726e-01   7.6776e-02   8.6331e-01   8.3048e-02
-#> 74:    1.0024e+02  -4.0628e+00  -2.4358e+00  -3.9977e+00  -9.1347e-01  -9.1966e-02   4.1381e+00   1.3033e-02   3.4332e-01   1.3418e-01   1.8099e-01   1.0738e-01   1.0158e+00   7.4697e-02   8.6366e-01   8.4370e-02
-#> 75:    99.7847   -4.0500   -2.4401   -4.0018   -0.9252   -0.1013    4.4651    0.0124    0.3365    0.1399    0.1817    0.1020    1.0278    0.0779    0.9008    0.0841
-#> 76:    99.9526   -4.0482   -2.4819   -3.9947   -0.9049   -0.0557    4.2419    0.0126    0.3248    0.1494    0.1726    0.1135    1.0493    0.0778    0.9341    0.0804
-#> 77:    99.9982   -4.0184   -2.4951   -4.0043   -0.8927   -0.0688    5.2538    0.0120    0.3696    0.1419    0.1817    0.1078    1.0402    0.0839    0.9605    0.0848
-#> 78:    1.0007e+02  -4.0210e+00  -2.4725e+00  -4.0040e+00  -8.9827e-01   2.3164e-03   6.4464e+00   1.1395e-02   3.7410e-01   1.3481e-01   2.0294e-01   1.0879e-01   9.7822e-01   8.7445e-02   9.9990e-01   8.2845e-02
-#> 79:    99.3513   -4.0171   -2.5065   -4.0078   -0.8962   -0.0029    7.7527    0.0108    0.3554    0.1281    0.1928    0.1069    1.0455    0.0866    0.9982    0.0870
-#> 80:    98.9945   -4.0172   -2.5412   -4.0341   -0.8891   -0.0187    9.8218    0.0103    0.3376    0.1217    0.1831    0.1457    0.9733    0.0894    1.0164    0.0832
-#> 81:    99.0936   -4.0275   -2.5134   -4.0127   -0.8552   -0.0614   12.1567    0.0098    0.3494    0.1156    0.1740    0.1384    0.9509    0.0843    1.0171    0.0855
-#> 82:    99.2481   -3.9996   -2.4945   -4.0011   -0.8914   -0.0492   11.5489    0.0128    0.3792    0.1098    0.1653    0.1315    0.9915    0.0818    1.0405    0.0928
-#> 83:    99.6941   -3.9998   -2.4851   -3.9845   -0.8802   -0.0560   10.9714    0.0146    0.3602    0.1043    0.1570    0.1249    0.9934    0.0852    0.9707    0.0866
-#> 84:    99.2185   -3.9920   -2.4843   -4.0051   -0.8546   -0.0642   10.4228    0.0153    0.3422    0.0991    0.1492    0.1187    0.9923    0.0833    0.9799    0.0873
-#> 85:    98.8470   -3.9956   -2.4652   -4.0201   -0.8483   -0.0414    9.9017    0.0146    0.3251    0.0941    0.1417    0.1128    0.9732    0.0901    0.9035    0.0858
-#> 86:    98.5012   -3.9841   -2.5148   -4.0250   -0.8408   -0.0551    9.4066    0.0148    0.3088    0.0962    0.1346    0.1071    0.8570    0.0932    0.8532    0.0896
-#> 87:    99.0868   -4.0055   -2.5058   -4.0249   -0.8522   -0.0311   10.3528    0.0175    0.2934    0.1013    0.1411    0.1018    0.8802    0.0838    0.8849    0.0862
-#> 88:    99.5158   -4.0031   -2.4437   -3.9866   -0.8894   -0.0963    9.9832    0.0167    0.3049    0.1030    0.1447    0.0967    0.9955    0.0834    0.8861    0.0893
-#> 89:    99.5538   -4.0347   -2.4494   -4.0213   -0.8695   -0.0494    9.4841    0.0158    0.2897    0.0978    0.1543    0.0918    0.8597    0.0904    0.8959    0.0880
-#> 90:    99.4422   -4.0453   -2.4398   -4.0114   -0.9279   -0.0745    9.8221    0.0150    0.2842    0.0929    0.1466    0.0944    0.9009    0.0871    0.8696    0.0924
-#> 91:    98.8721   -4.0328   -2.4996   -4.0041   -0.8832   -0.0689    9.3310    0.0143    0.2700    0.0896    0.1444    0.1137    0.9567    0.0904    0.8680    0.0891
-#> 92:    99.8390   -4.0418   -2.4914   -4.0182   -0.9279   -0.0460   10.9801    0.0136    0.2585    0.0949    0.1461    0.1210    1.0043    0.0908    0.8310    0.0939
-#> 93:    1.0029e+02  -4.0313e+00  -2.4620e+00  -4.0187e+00  -8.9083e-01  -1.0908e-01   1.0431e+01   1.2890e-02   2.4559e-01   9.5757e-02   1.3878e-01   1.1565e-01   9.9174e-01   9.0056e-02   8.9538e-01   8.8925e-02
-#> 94:    99.3285   -4.0295   -2.4523   -4.0235   -0.8828   -0.1190   10.9003    0.0137    0.2333    0.0915    0.1318    0.1212    1.0729    0.0779    0.9543    0.0907
-#> 95:    99.4117   -4.0422   -2.3807   -4.0870   -0.8960   -0.0889   10.3553    0.0130    0.2216    0.0870    0.1253    0.1366    0.9127    0.0864    0.8901    0.0911
-#> 96:    99.3348   -4.0401   -2.4009   -4.0698   -0.8730   -0.0622    9.8375    0.0123    0.2106    0.0826    0.1241    0.1297    0.8504    0.0836    0.9140    0.0881
-#> 97:    99.4898   -4.0419   -2.4310   -4.0589   -0.8932   -0.0634    9.3456    0.0132    0.2000    0.0785    0.1224    0.1233    0.8770    0.0836    0.8715    0.0837
-#> 98:    99.3750   -4.0704   -2.4353   -4.0616   -0.9333   -0.0846    8.8783    0.0136    0.1900    0.0746    0.1245    0.1171    0.8907    0.0838    0.9066    0.0832
-#> 99:    99.6234   -4.0366   -2.3740   -4.0657   -0.9242   -0.0675    8.4344    0.0129    0.1805    0.0708    0.1182    0.1112    0.8814    0.0808    0.9511    0.0863
-#> 100:    1.0025e+02  -4.0420e+00  -2.3557e+00  -4.0579e+00  -9.5051e-01  -6.3418e-02   8.0319e+00   1.2286e-02   1.7150e-01   6.7291e-02   1.1232e-01   1.0568e-01   8.5851e-01   8.7881e-02   8.9363e-01   8.5897e-02
-#> 101:    1.0041e+02  -4.0461e+00  -2.3840e+00  -4.0384e+00  -9.3752e-01  -7.7594e-02   9.5649e+00   1.1672e-02   1.7509e-01   6.3926e-02   1.2760e-01   1.0039e-01   8.6733e-01   8.2748e-02   9.6277e-01   8.4274e-02
-#> 102:    1.0095e+02  -4.0372e+00  -2.3633e+00  -4.0286e+00  -9.1961e-01  -6.5350e-02   1.1428e+01   1.1088e-02   1.8557e-01   6.0730e-02   1.3211e-01   9.5374e-02   9.3928e-01   8.0161e-02   9.7913e-01   8.4081e-02
-#> 103:    1.0019e+02  -4.0236e+00  -2.4105e+00  -4.0337e+00  -9.1362e-01  -7.3859e-02   1.0856e+01   1.0534e-02   1.7629e-01   5.7693e-02   1.2695e-01   9.1362e-02   9.8491e-01   8.1430e-02   9.7682e-01   8.2250e-02
-#> 104:    99.7755   -4.0280   -2.4452   -4.0197   -0.9112   -0.0810   11.0317    0.0100    0.1796    0.0548    0.1301    0.0868    0.9418    0.0816    0.9170    0.0806
-#> 105:    1.0010e+02  -4.0418e+00  -2.4294e+00  -4.0225e+00  -9.1111e-01  -8.9920e-02   1.0480e+01   9.5070e-03   1.7060e-01   5.2068e-02   1.3987e-01   8.2454e-02   9.1944e-01   7.8110e-02   8.9266e-01   8.7228e-02
-#> 106:    1.0025e+02  -4.0507e+00  -2.4134e+00  -4.0343e+00  -9.0244e-01  -8.4683e-02   1.3506e+01   9.0316e-03   1.6207e-01   4.9465e-02   1.5337e-01   7.8331e-02   9.9609e-01   8.4473e-02   8.7046e-01   8.5479e-02
-#> 107:    1.0014e+02  -4.0468e+00  -2.3972e+00  -4.0196e+00  -9.3650e-01  -2.4087e-02   1.2830e+01   8.5801e-03   1.6027e-01   4.6992e-02   1.5429e-01   8.2493e-02   9.8959e-01   8.2626e-02   8.3427e-01   8.8197e-02
-#> 108:    1.0114e+02  -4.0338e+00  -2.4307e+00  -4.0724e+00  -9.1363e-01   1.1952e-02   1.2189e+01   8.1511e-03   1.5563e-01   4.4854e-02   1.7315e-01   7.8368e-02   9.8589e-01   7.8130e-02   9.0460e-01   8.2870e-02
-#> 109:    1.0066e+02  -4.0550e+00  -2.4094e+00  -4.0641e+00  -9.0945e-01  -1.5401e-03   1.3149e+01   7.7435e-03   1.4785e-01   4.2612e-02   1.7232e-01   7.4450e-02   1.0942e+00   7.4816e-02   9.1706e-01   8.5333e-02
-#> 110:    1.0111e+02  -4.0266e+00  -2.4047e+00  -4.0646e+00  -9.0541e-01  -1.7212e-02   1.2492e+01   7.3563e-03   1.4046e-01   4.0481e-02   1.8132e-01   7.0727e-02   1.0508e+00   7.9457e-02   9.8990e-01   8.2975e-02
-#> 111:    1.0155e+02  -4.0274e+00  -2.3645e+00  -4.0663e+00  -9.4902e-01  -1.8882e-02   1.1867e+01   8.7757e-03   1.4436e-01   3.8457e-02   1.7225e-01   6.7191e-02   1.0217e+00   7.7437e-02   9.9196e-01   8.1580e-02
-#> 112:    1.0209e+02  -4.0230e+00  -2.3938e+00  -4.0375e+00  -9.5447e-01  -5.0888e-02   1.4321e+01   8.3370e-03   1.4863e-01   3.6534e-02   1.6778e-01   8.2186e-02   9.3085e-01   8.3291e-02   9.8775e-01   7.9492e-02
-#> 113:    1.0188e+02  -4.0173e+00  -2.3804e+00  -4.0403e+00  -9.6152e-01  -7.7453e-02   1.3605e+01   7.9201e-03   1.5060e-01   3.4708e-02   1.7341e-01   8.4506e-02   9.0783e-01   8.7383e-02   9.4854e-01   8.2648e-02
-#> 114:    1.0239e+02  -4.0081e+00  -2.3724e+00  -4.0332e+00  -9.4315e-01  -7.4933e-02   1.2925e+01   7.5241e-03   1.4307e-01   3.2972e-02   1.6695e-01   8.0281e-02   9.2775e-01   8.4314e-02   9.6195e-01   7.9448e-02
-#> 115:    1.0199e+02  -4.0127e+00  -2.3773e+00  -4.0472e+00  -9.5157e-01  -2.0947e-02   1.2279e+01   7.4483e-03   1.3592e-01   3.1324e-02   1.6705e-01   7.6267e-02   9.4956e-01   7.6989e-02   1.0340e+00   8.5564e-02
-#> 116:    1.0122e+02  -4.0264e+00  -2.4014e+00  -4.0509e+00  -9.1462e-01  -2.3511e-02   1.1665e+01   7.0759e-03   1.2912e-01   2.9757e-02   1.5870e-01   7.2453e-02   9.3580e-01   8.2952e-02   9.3341e-01   8.3302e-02
-#> 117:    1.0112e+02  -4.0326e+00  -2.4093e+00  -4.0559e+00  -8.9743e-01  -2.0572e-02   1.1082e+01   6.7221e-03   1.2266e-01   2.8269e-02   1.5339e-01   6.8831e-02   9.0879e-01   8.4441e-02   9.1432e-01   8.0538e-02
-#> 118:    1.0123e+02  -4.0411e+00  -2.4077e+00  -4.0556e+00  -9.2971e-01  -2.1885e-02   1.0528e+01   6.3860e-03   1.1653e-01   3.3123e-02   1.6947e-01   6.5389e-02   9.7140e-01   8.6671e-02   8.9874e-01   8.1670e-02
-#> 119:    1.0098e+02  -4.0538e+00  -2.3515e+00  -4.0607e+00  -9.5433e-01  -7.5743e-02   1.0001e+01   6.0667e-03   1.1070e-01   3.1467e-02   1.8338e-01   6.2120e-02   9.1537e-01   8.4827e-02   9.2420e-01   8.2769e-02
-#> 120:    1.0076e+02  -4.0573e+00  -2.3627e+00  -4.0329e+00  -9.3251e-01  -6.7669e-02   9.5011e+00   5.7634e-03   1.0517e-01   3.2868e-02   1.7422e-01   6.6096e-02   9.5247e-01   8.5343e-02   9.4678e-01   8.5335e-02
-#> 121:    1.0085e+02  -4.0450e+00  -2.3478e+00  -4.0692e+00  -9.2333e-01  -9.8005e-03   9.0261e+00   5.4752e-03   9.9911e-02   3.1225e-02   1.6550e-01   7.1593e-02   8.5572e-01   8.8654e-02   1.0248e+00   8.0646e-02
-#> 122:    1.0164e+02  -4.0325e+00  -2.3562e+00  -4.0680e+00  -9.4287e-01  -1.2103e-02   8.5748e+00   5.3493e-03   9.4915e-02   2.9663e-02   1.6347e-01   6.8014e-02   8.4872e-01   8.6803e-02   1.0282e+00   8.0381e-02
-#> 123:    1.0184e+02  -4.0521e+00  -2.3504e+00  -4.0714e+00  -9.5966e-01  -9.1996e-05   8.1460e+00   5.0818e-03   9.8247e-02   3.0007e-02   1.7746e-01   6.4613e-02   9.7181e-01   8.0986e-02   9.8860e-01   8.0317e-02
-#> 124:    1.0235e+02  -4.0674e+00  -2.3315e+00  -4.0874e+00  -9.9802e-01   3.8818e-02   7.7387e+00   4.8277e-03   9.3335e-02   2.8506e-02   1.7611e-01   6.8940e-02   9.7376e-01   7.6658e-02   9.9156e-01   8.4407e-02
-#> 125:    1.0257e+02  -4.0718e+00  -2.3604e+00  -4.0627e+00  -1.0591e+00   2.4685e-02   7.3518e+00   4.5863e-03   8.8668e-02   3.0650e-02   1.8671e-01   6.5493e-02   1.0275e+00   8.2278e-02   1.0896e+00   8.0976e-02
-#> 126:    1.0287e+02  -4.0691e+00  -2.3103e+00  -4.0552e+00  -1.0174e+00   2.1863e-02   7.5644e+00   4.3570e-03   1.0937e-01   2.9117e-02   1.7738e-01   6.2218e-02   9.2668e-01   7.9560e-02   9.5409e-01   8.4671e-02
-#> 127:    1.0327e+02  -4.0528e+00  -2.3141e+00  -4.0522e+00  -1.0108e+00   4.4779e-03   7.1862e+00   4.1392e-03   1.2239e-01   2.7661e-02   1.6925e-01   5.9107e-02   9.1372e-01   7.9536e-02   9.9164e-01   8.2999e-02
-#> 128:    1.0352e+02  -4.0496e+00  -2.2880e+00  -4.0496e+00  -1.0063e+00  -1.3248e-02   7.6721e+00   3.9613e-03   1.1627e-01   2.6278e-02   1.7517e-01   8.0231e-02   8.4407e-01   8.5078e-02   9.4382e-01   8.7530e-02
-#> 129:    1.0345e+02  -4.0715e+00  -2.3090e+00  -4.0400e+00  -1.0276e+00  -1.8301e-02   8.2197e+00   3.7633e-03   1.1046e-01   2.7141e-02   1.9366e-01   7.6220e-02   9.3357e-01   8.2674e-02   9.7064e-01   8.6011e-02
-#> 130:    1.0245e+02  -4.0787e+00  -2.3263e+00  -4.0106e+00  -1.0200e+00  -8.5976e-02   7.8087e+00   4.0830e-03   1.3607e-01   2.6631e-02   2.2700e-01   7.2409e-02   9.8233e-01   7.9348e-02   9.6780e-01   8.2658e-02
-#> 131:    1.0217e+02  -4.0760e+00  -2.2525e+00  -4.0082e+00  -1.0099e+00  -1.6111e-01   7.4183e+00   3.8789e-03   1.3972e-01   2.5299e-02   2.2508e-01   6.8788e-02   1.0066e+00   7.8692e-02   9.4684e-01   8.4349e-02
-#> 132:    1.0185e+02  -4.0792e+00  -2.2309e+00  -3.9996e+00  -9.8302e-01  -2.2504e-01   7.0474e+00   4.0356e-03   1.3743e-01   2.4034e-02   2.1383e-01   7.7346e-02   9.4225e-01   7.9110e-02   9.5160e-01   8.4398e-02
-#> 133:    1.0135e+02  -4.0818e+00  -2.2219e+00  -4.0054e+00  -9.7264e-01  -1.8912e-01   7.1932e+00   3.8338e-03   1.3056e-01   2.2833e-02   2.0314e-01   7.6769e-02   1.0031e+00   8.5400e-02   1.0034e+00   8.4805e-02
-#> 134:    1.0148e+02  -4.0782e+00  -2.2492e+00  -3.9886e+00  -9.5184e-01  -1.5049e-01   6.8336e+00   3.6422e-03   1.2403e-01   2.3398e-02   1.9298e-01   7.2931e-02   9.3696e-01   8.3566e-02   9.4742e-01   8.9137e-02
-#> 135:    1.0145e+02  -4.0852e+00  -2.3062e+00  -4.0011e+00  -9.4444e-01  -1.6803e-01   6.4919e+00   3.4600e-03   1.1783e-01   2.2228e-02   1.8333e-01   6.9284e-02   9.4846e-01   8.3087e-02   9.7774e-01   8.2610e-02
-#> 136:    1.0177e+02  -4.0861e+00  -2.2785e+00  -3.9890e+00  -9.9625e-01  -1.8938e-01   6.1673e+00   3.2870e-03   1.1752e-01   2.1116e-02   1.8815e-01   6.5820e-02   9.3634e-01   8.5255e-02   1.1001e+00   8.5332e-02
-#> 137:    1.0200e+02  -4.0928e+00  -2.1946e+00  -3.9974e+00  -1.0098e+00  -1.8810e-01   5.8589e+00   3.1227e-03   1.2394e-01   2.1203e-02   1.7874e-01   7.2232e-02   1.0048e+00   7.3422e-02   1.0222e+00   8.3484e-02
-#> 138:    1.0214e+02  -4.0820e+00  -2.2052e+00  -3.9737e+00  -1.0420e+00  -2.0594e-01   5.5660e+00   3.8937e-03   1.9164e-01   2.0143e-02   1.6980e-01   6.8621e-02   1.0126e+00   7.6106e-02   1.0780e+00   8.2960e-02
-#> 139:    1.0249e+02  -4.0785e+00  -2.1649e+00  -3.9567e+00  -1.0095e+00  -2.8807e-01   5.2877e+00   3.6990e-03   1.8647e-01   1.9135e-02   1.6131e-01   6.5190e-02   1.0030e+00   7.9858e-02   1.0611e+00   8.4109e-02
-#> 140:    1.0184e+02  -4.0847e+00  -2.1800e+00  -3.9565e+00  -9.9415e-01  -2.8869e-01   5.0233e+00   4.0857e-03   1.9502e-01   1.8179e-02   1.6676e-01   6.2879e-02   9.5962e-01   7.8117e-02   9.9649e-01   8.4914e-02
-#> 141:    1.0195e+02  -4.1012e+00  -2.1831e+00  -3.9488e+00  -9.9515e-01  -3.1864e-01   4.7721e+00   3.8814e-03   1.8527e-01   1.7270e-02   1.6797e-01   6.1084e-02   9.0969e-01   8.2722e-02   1.0122e+00   8.2518e-02
-#> 142:    1.0233e+02  -4.1139e+00  -2.1692e+00  -3.9542e+00  -1.0023e+00  -3.3242e-01   4.5335e+00   3.6873e-03   2.0662e-01   1.6406e-02   1.5957e-01   5.8030e-02   9.4761e-01   8.4629e-02   1.0342e+00   8.3954e-02
-#> 143:    1.0217e+02  -4.1103e+00  -2.1380e+00  -3.9511e+00  -1.0300e+00  -2.5992e-01   5.2035e+00   4.7053e-03   1.9629e-01   1.5586e-02   1.5979e-01   5.5128e-02   8.9255e-01   7.9042e-02   1.0461e+00   8.6952e-02
-#> 144:    1.0185e+02  -4.1335e+00  -2.1911e+00  -3.9650e+00  -1.0440e+00  -2.4451e-01   5.0998e+00   4.4700e-03   1.8648e-01   1.9590e-02   1.5534e-01   5.2372e-02   9.7863e-01   8.3932e-02   1.0197e+00   8.7673e-02
-#> 145:    1.0242e+02  -4.1445e+00  -2.1203e+00  -3.9616e+00  -1.0426e+00  -2.7120e-01   4.8448e+00   4.2465e-03   1.7715e-01   1.8611e-02   1.4757e-01   4.9753e-02   1.0024e+00   8.4131e-02   1.0768e+00   8.5388e-02
-#> 146:    1.0236e+02  -4.1519e+00  -2.1958e+00  -3.9779e+00  -9.8615e-01  -2.5863e-01   4.6026e+00   4.0718e-03   1.6829e-01   1.7680e-02   1.6407e-01   4.7266e-02   1.0740e+00   8.2413e-02   1.0706e+00   8.3410e-02
-#> 147:    1.0251e+02  -4.1465e+00  -2.2042e+00  -3.9775e+00  -1.0317e+00  -2.2757e-01   4.3725e+00   3.8682e-03   1.5988e-01   1.6796e-02   1.7016e-01   4.4902e-02   9.7748e-01   8.3376e-02   1.0880e+00   8.1968e-02
-#> 148:    1.0244e+02  -4.1432e+00  -2.1786e+00  -3.9792e+00  -1.0442e+00  -2.2002e-01   4.9671e+00   3.6748e-03   1.5189e-01   1.5956e-02   2.2196e-01   4.2657e-02   1.0412e+00   7.8051e-02   1.1051e+00   8.1618e-02
-#> 149:    1.0219e+02  -4.1384e+00  -2.2318e+00  -3.9757e+00  -1.0438e+00  -2.4124e-01   4.7187e+00   3.4910e-03   1.4429e-01   1.6061e-02   2.1086e-01   4.0524e-02   1.0082e+00   8.0377e-02   1.1455e+00   8.0545e-02
-#> 150:    1.0264e+02  -4.1498e+00  -2.2352e+00  -3.9915e+00  -1.0669e+00  -2.1255e-01   4.4828e+00   3.3165e-03   1.3708e-01   1.7218e-02   2.0032e-01   3.8498e-02   9.5031e-01   8.7248e-02   9.8770e-01   8.3250e-02
-#> 151:    1.0250e+02  -4.1365e+00  -2.1876e+00  -3.9939e+00  -1.0568e+00  -1.8159e-01   4.2587e+00   3.1507e-03   1.3022e-01   1.7383e-02   1.9030e-01   3.6573e-02   9.6938e-01   8.0203e-02   1.0578e+00   8.3430e-02
-#> 152:    1.0256e+02  -4.1370e+00  -2.2238e+00  -4.0047e+00  -1.0406e+00  -1.8764e-01   1.9609e+00   1.4191e-03   1.1882e-01   1.7924e-02   1.6889e-01   4.1216e-02   9.1972e-01   7.8573e-02   1.0717e+00   8.0882e-02
-#> 153:    1.0219e+02  -4.1299e+00  -2.2139e+00  -3.9917e+00  -9.9964e-01  -2.0505e-01   1.8258e+00   1.0432e-03   8.4660e-02   2.1446e-02   1.7634e-01   3.5573e-02   9.3702e-01   8.4860e-02   1.0145e+00   8.3329e-02
-#> 154:    1.0199e+02  -4.1354e+00  -2.2231e+00  -3.9779e+00  -1.0155e+00  -2.2573e-01   2.6463e+00   5.8153e-04   8.8101e-02   2.3167e-02   1.6103e-01   3.3874e-02   9.5360e-01   8.6215e-02   9.5723e-01   8.4603e-02
-#> 155:    1.0234e+02  -4.1239e+00  -2.2137e+00  -3.9802e+00  -1.0070e+00  -2.3158e-01   2.9697e+00   6.6709e-04   1.1190e-01   2.0949e-02   1.8298e-01   3.1557e-02   9.2910e-01   8.2509e-02   9.8680e-01   8.5206e-02
-#> 156:    1.0253e+02  -4.1269e+00  -2.2370e+00  -3.9682e+00  -1.0420e+00  -2.1219e-01   2.7267e+00   6.8451e-04   8.9651e-02   2.4380e-02   1.6613e-01   3.4846e-02   9.3608e-01   8.7506e-02   9.0446e-01   8.1755e-02
-#> 157:    1.0265e+02  -4.1241e+00  -2.2179e+00  -3.9676e+00  -1.0308e+00  -2.2480e-01   2.1278e+00   4.9811e-04   6.7161e-02   1.9758e-02   1.5607e-01   4.4198e-02   9.4162e-01   8.7311e-02   9.9147e-01   7.9857e-02
-#> 158:    1.0239e+02  -4.1219e+00  -2.1615e+00  -3.9781e+00  -1.0384e+00  -2.6750e-01   2.5310e+00   4.8270e-04   6.5662e-02   1.8085e-02   1.7665e-01   4.4020e-02   8.8632e-01   8.6004e-02   1.0425e+00   8.2894e-02
-#> 159:    1.0270e+02  -4.1204e+00  -2.1837e+00  -3.9530e+00  -1.0587e+00  -2.5809e-01   3.4348e+00   5.6788e-04   6.5500e-02   1.9540e-02   1.8629e-01   4.0730e-02   9.5079e-01   8.2399e-02   9.9316e-01   8.3381e-02
-#> 160:    1.0282e+02  -4.1223e+00  -2.1325e+00  -3.9734e+00  -1.0068e+00  -2.8751e-01   3.9652e+00   7.6565e-04   8.5246e-02   1.7068e-02   1.7587e-01   3.0778e-02   9.1802e-01   8.0158e-02   9.9642e-01   8.1564e-02
-#> 161:    1.0330e+02  -4.1180e+00  -2.1879e+00  -3.9743e+00  -1.0268e+00  -2.8812e-01   4.9153e+00   5.8033e-04   8.0457e-02   1.8555e-02   1.7312e-01   3.3941e-02   8.6920e-01   8.2509e-02   9.5632e-01   8.1798e-02
-#> 162:    1.0335e+02  -4.1182e+00  -2.2089e+00  -3.9566e+00  -1.0409e+00  -2.7390e-01   3.6169e+00   2.8392e-04   1.0776e-01   1.9589e-02   1.6479e-01   2.8481e-02   8.8603e-01   8.7799e-02   9.5197e-01   7.9563e-02
-#> 163:    1.0294e+02  -4.1181e+00  -2.2025e+00  -3.9462e+00  -9.9783e-01  -3.0753e-01   3.7234e+00   1.6293e-04   9.6922e-02   2.4842e-02   1.9367e-01   3.1473e-02   9.0380e-01   9.1697e-02   9.4394e-01   8.2786e-02
-#> 164:    1.0246e+02  -4.1155e+00  -2.2157e+00  -3.9736e+00  -9.9866e-01  -2.9356e-01   3.9439e+00   1.9405e-04   1.0404e-01   2.8435e-02   1.9043e-01   3.1239e-02   8.9853e-01   8.9427e-02   9.2586e-01   8.3170e-02
-#> 165:    1.0204e+02  -4.1117e+00  -2.2133e+00  -3.9674e+00  -1.0079e+00  -2.6996e-01   3.0774e+00   1.6591e-04   7.0005e-02   2.8285e-02   2.0813e-01   2.4574e-02   8.9719e-01   9.1629e-02   9.8242e-01   8.3692e-02
-#> 166:    1.0207e+02  -4.1164e+00  -2.2192e+00  -3.9893e+00  -1.0354e+00  -2.7396e-01   1.8145e+00   8.4168e-05   9.0739e-02   2.7410e-02   2.1403e-01   2.4311e-02   8.9386e-01   9.2727e-02   9.4636e-01   8.4238e-02
-#> 167:    1.0187e+02  -4.1149e+00  -2.2185e+00  -3.9708e+00  -1.0036e+00  -2.5751e-01   1.5355e+00   4.0974e-05   9.9346e-02   2.2030e-02   2.1916e-01   2.6726e-02   9.1055e-01   8.1030e-02   1.0098e+00   7.9180e-02
-#> 168:    1.0172e+02  -4.1167e+00  -2.2673e+00  -3.9702e+00  -9.8388e-01  -2.1404e-01   1.4836e+00   2.7779e-05   7.7509e-02   2.9513e-02   1.9543e-01   3.4526e-02   1.0152e+00   8.1248e-02   9.7482e-01   8.0746e-02
-#> 169:    1.0175e+02  -4.1171e+00  -2.2634e+00  -3.9701e+00  -9.5962e-01  -2.4130e-01   1.4263e+00   4.7370e-05   5.0986e-02   2.8211e-02   2.2554e-01   3.9909e-02   9.8519e-01   7.8842e-02   1.0023e+00   8.5684e-02
-#> 170:    1.0177e+02  -4.1189e+00  -2.2417e+00  -3.9834e+00  -1.0059e+00  -2.6551e-01   9.9010e-01   3.7247e-05   4.2517e-02   2.9791e-02   1.8705e-01   4.2435e-02   9.6604e-01   8.8427e-02   9.6699e-01   8.3986e-02
-#> 171:    1.0182e+02  -4.1187e+00  -2.2464e+00  -3.9953e+00  -9.8154e-01  -2.5146e-01   7.4179e-01   3.2420e-05   5.0690e-02   3.0483e-02   1.7888e-01   6.3177e-02   9.2784e-01   8.4814e-02   1.0018e+00   8.4070e-02
-#> 172:    1.0184e+02  -4.1178e+00  -2.2483e+00  -4.0009e+00  -1.0096e+00  -2.2636e-01   9.6710e-01   2.6981e-05   3.1321e-02   2.7772e-02   1.9767e-01   7.4969e-02   9.9720e-01   8.1434e-02   9.5483e-01   8.3419e-02
-#> 173:    1.0160e+02  -4.1183e+00  -2.2513e+00  -3.9920e+00  -9.8456e-01  -2.0144e-01   4.9964e-01   2.1222e-05   4.1909e-02   2.8101e-02   2.1163e-01   1.2811e-01   9.6384e-01   8.0352e-02   9.2496e-01   8.2328e-02
-#> 174:    1.0159e+02  -4.1179e+00  -2.2334e+00  -4.0068e+00  -1.0316e+00  -2.0656e-01   4.6608e-01   1.8044e-05   4.4647e-02   2.8273e-02   2.0083e-01   1.2780e-01   9.4612e-01   8.3630e-02   8.9385e-01   8.3930e-02
-#> 175:    1.0159e+02  -4.1182e+00  -2.2567e+00  -3.9972e+00  -1.0299e+00  -1.6534e-01   4.5228e-01   2.0060e-05   8.5751e-02   2.5343e-02   1.7864e-01   8.6977e-02   9.5795e-01   7.8867e-02   8.9213e-01   8.4362e-02
-#> 176:    1.0159e+02  -4.1183e+00  -2.2109e+00  -3.9983e+00  -1.0210e+00  -2.0879e-01   5.3694e-01   2.0264e-05   1.2835e-01   2.5563e-02   1.9469e-01   6.0808e-02   9.1537e-01   7.8520e-02   9.3355e-01   8.3608e-02
-#> 177:    1.0155e+02  -4.1193e+00  -2.2587e+00  -3.9825e+00  -1.0180e+00  -1.6859e-01   4.4935e-01   3.0321e-05   1.3509e-01   2.4979e-02   2.0113e-01   6.3617e-02   9.7277e-01   7.8515e-02   9.2667e-01   8.5309e-02
-#> 178:    1.0158e+02  -4.1196e+00  -2.2679e+00  -4.0231e+00  -1.0143e+00  -1.6084e-01   6.7629e-01   3.2855e-05   6.8816e-02   2.7808e-02   1.8944e-01   8.1814e-02   8.8319e-01   8.0114e-02   9.5183e-01   8.2195e-02
-#> 179:    1.0166e+02  -4.1190e+00  -2.2764e+00  -3.9875e+00  -1.0061e+00  -1.8260e-01   7.1129e-01   3.8250e-05   7.5489e-02   2.4148e-02   1.8082e-01   7.1172e-02   9.1387e-01   8.0813e-02   9.6660e-01   8.2457e-02
-#> 180:    1.0179e+02  -4.1202e+00  -2.2848e+00  -3.9974e+00  -9.9825e-01  -2.0277e-01   5.5755e-01   2.8041e-05   8.6779e-02   2.7193e-02   1.8826e-01   6.5133e-02   8.8812e-01   8.2655e-02   9.2100e-01   7.9919e-02
-#> 181:    1.0176e+02  -4.1200e+00  -2.2704e+00  -3.9954e+00  -1.0194e+00  -1.6896e-01   4.3842e-01   2.2428e-05   7.4093e-02   3.0526e-02   2.3473e-01   1.0537e-01   9.2303e-01   8.2141e-02   9.2941e-01   8.4699e-02
-#> 182:    1.0182e+02  -4.1211e+00  -2.3159e+00  -4.0259e+00  -1.0162e+00  -1.2876e-01   3.4993e-01   1.5716e-05   5.9887e-02   2.6422e-02   2.1757e-01   1.0488e-01   9.1725e-01   9.4143e-02   9.7674e-01   8.8668e-02
-#> 183:    1.0184e+02  -4.1216e+00  -2.2985e+00  -4.0278e+00  -1.0136e+00  -1.3154e-01   2.6456e-01   1.2552e-05   5.7149e-02   3.2712e-02   2.0632e-01   1.5501e-01   9.2464e-01   8.5394e-02   8.8699e-01   8.4279e-02
-#> 184:    1.0172e+02  -4.1212e+00  -2.2726e+00  -4.0189e+00  -1.0280e+00  -1.2967e-01   3.0582e-01   7.5239e-06   8.2812e-02   2.9556e-02   1.9725e-01   1.3753e-01   9.0862e-01   8.1319e-02   9.0031e-01   8.3491e-02
-#> 185:    1.0178e+02  -4.1208e+00  -2.2858e+00  -4.0272e+00  -1.0063e+00  -1.6155e-01   3.0856e-01   4.5894e-06   8.8870e-02   2.5817e-02   1.9251e-01   1.0670e-01   9.1157e-01   7.7834e-02   9.6258e-01   7.8990e-02
-#> 186:    1.0198e+02  -4.1208e+00  -2.2682e+00  -4.0401e+00  -9.8523e-01  -1.1556e-01   2.4761e-01   3.2640e-06   7.5614e-02   2.1067e-02   1.9085e-01   9.0045e-02   8.5090e-01   8.6621e-02   1.0145e+00   8.1864e-02
-#> 187:    1.0197e+02  -4.1208e+00  -2.2788e+00  -4.0281e+00  -1.0066e+00  -1.0149e-01   2.0460e-01   4.5073e-06   7.8797e-02   2.3861e-02   2.0725e-01   7.9771e-02   9.6253e-01   8.2363e-02   9.3855e-01   8.3939e-02
-#> 188:    1.0196e+02  -4.1207e+00  -2.3105e+00  -4.0149e+00  -1.0217e+00  -9.0603e-02   2.2178e-01   3.6903e-06   8.9793e-02   2.1775e-02   1.9248e-01   8.2415e-02   9.4078e-01   8.1247e-02   9.1756e-01   8.2786e-02
-#> 189:    1.0202e+02  -4.1204e+00  -2.2702e+00  -4.0430e+00  -1.0032e+00  -1.1308e-01   2.2944e-01   3.5141e-06   7.8575e-02   2.4885e-02   2.0968e-01   8.2380e-02   9.5115e-01   8.1619e-02   9.2134e-01   8.9958e-02
-#> 190:    1.0195e+02  -4.1207e+00  -2.3126e+00  -4.0312e+00  -1.0154e+00  -6.3842e-02   2.5129e-01   2.6517e-06   4.2267e-02   2.2084e-02   1.9361e-01   7.0492e-02   9.3985e-01   8.5817e-02   9.3893e-01   8.7011e-02
-#> 191:    1.0203e+02  -4.1206e+00  -2.2758e+00  -4.0290e+00  -1.0102e+00  -3.1042e-02   1.7935e-01   3.4489e-06   5.7444e-02   2.3544e-02   1.9651e-01   7.9509e-02   9.5213e-01   8.2030e-02   1.0054e+00   8.7523e-02
-#> 192:    1.0199e+02  -4.1205e+00  -2.2969e+00  -4.0329e+00  -1.0364e+00  -8.3705e-02   1.5785e-01   3.5081e-06   7.4305e-02   2.2992e-02   1.9662e-01   7.7684e-02   9.2601e-01   8.3027e-02   9.8642e-01   8.3428e-02
-#> 193:    1.0196e+02  -4.1205e+00  -2.2661e+00  -4.0513e+00  -9.9271e-01  -4.6516e-02   1.2084e-01   2.6911e-06   6.8360e-02   3.5444e-02   1.9649e-01   7.5188e-02   9.1949e-01   7.9194e-02   1.0046e+00   8.5964e-02
-#> 194:    1.0198e+02  -4.1207e+00  -2.2817e+00  -4.0520e+00  -9.9852e-01  -8.4466e-02   1.3596e-01   1.5511e-06   6.5142e-02   4.1562e-02   1.9137e-01   9.6992e-02   9.6709e-01   7.6757e-02   9.7566e-01   8.3784e-02
-#> 195:    1.0200e+02  -4.1207e+00  -2.3076e+00  -4.0637e+00  -1.0028e+00  -7.2489e-02   1.0942e-01   1.6451e-06   6.1364e-02   4.6242e-02   1.9470e-01   9.3546e-02   9.9614e-01   8.1292e-02   9.7814e-01   8.1909e-02
-#> 196:    1.0194e+02  -4.1205e+00  -2.2970e+00  -4.0482e+00  -9.8816e-01  -6.8493e-02   1.1918e-01   1.2629e-06   4.2775e-02   3.6925e-02   2.3565e-01   7.7784e-02   8.9524e-01   9.2250e-02   9.8003e-01   8.2408e-02
-#> 197:    1.0199e+02  -4.1205e+00  -2.3075e+00  -4.0418e+00  -1.0196e+00  -6.8458e-02   1.7674e-01   7.5205e-07   5.2125e-02   2.9288e-02   2.1892e-01   8.4416e-02   8.9857e-01   9.1154e-02   1.0377e+00   8.3604e-02
-#> 198:    1.0197e+02  -4.1206e+00  -2.3051e+00  -4.0367e+00  -1.0252e+00  -6.9200e-02   9.1625e-02   6.6068e-07   4.7665e-02   2.8907e-02   1.8679e-01   7.5787e-02   9.0272e-01   8.8077e-02   9.2929e-01   8.0385e-02
-#> 199:    1.0192e+02  -4.1204e+00  -2.3163e+00  -4.0506e+00  -1.0152e+00  -5.3872e-02   6.8196e-02   5.5789e-07   6.0471e-02   3.1730e-02   2.0053e-01   6.8557e-02   9.0478e-01   8.5910e-02   9.3814e-01   8.2211e-02
-#> 200:    1.0195e+02  -4.1205e+00  -2.3141e+00  -4.0728e+00  -1.0010e+00  -2.5675e-03   6.5235e-02   6.9762e-07   5.8458e-02   2.8504e-02   2.0377e-01   4.9513e-02   8.5640e-01   8.6640e-02   9.5731e-01   8.4390e-02
-#> 201:    1.0195e+02  -4.1205e+00  -2.3106e+00  -4.0774e+00  -9.9012e-01   5.1724e-03   5.1225e-02   5.4222e-07   6.0577e-02   3.3554e-02   2.0505e-01   4.4738e-02   8.8073e-01   8.5488e-02   9.6928e-01   8.4895e-02
-#> 202:    1.0194e+02  -4.1205e+00  -2.3078e+00  -4.0767e+00  -9.9283e-01   3.9328e-03   4.5461e-02   4.8520e-07   6.7405e-02   3.4599e-02   2.1312e-01   4.6664e-02   9.0528e-01   8.4189e-02   9.8043e-01   8.5266e-02
-#> 203:    1.0193e+02  -4.1205e+00  -2.3029e+00  -4.0790e+00  -9.8990e-01  -9.1380e-03   4.7128e-02   5.0468e-07   6.8524e-02   3.6050e-02   2.1378e-01   5.1774e-02   9.0923e-01   8.4899e-02   9.8928e-01   8.4613e-02
-#> 204:    1.0192e+02  -4.1205e+00  -2.3080e+00  -4.0760e+00  -9.8833e-01  -1.2434e-02   4.8184e-02   4.9472e-07   6.4152e-02   3.5604e-02   2.0954e-01   5.1354e-02   9.1294e-01   8.5219e-02   9.8301e-01   8.4374e-02
-#> 205:    1.0192e+02  -4.1205e+00  -2.3100e+00  -4.0712e+00  -9.9253e-01  -2.3365e-02   4.5888e-02   5.0564e-07   5.9894e-02   3.5053e-02   2.0322e-01   5.4423e-02   9.0925e-01   8.5899e-02   9.8418e-01   8.3421e-02
-#> 206:    1.0192e+02  -4.1205e+00  -2.3095e+00  -4.0715e+00  -9.9721e-01  -2.6262e-02   4.3985e-02   5.1954e-07   5.8681e-02   3.4539e-02   2.0202e-01   5.8248e-02   9.1301e-01   8.5459e-02   9.8621e-01   8.3465e-02
-#> 207:    1.0192e+02  -4.1205e+00  -2.3179e+00  -4.0731e+00  -9.9906e-01  -2.3191e-02   4.3649e-02   5.3824e-07   5.7537e-02   3.4790e-02   2.0220e-01   6.0242e-02   9.1783e-01   8.5307e-02   9.8436e-01   8.3111e-02
-#> 208:    1.0191e+02  -4.1205e+00  -2.3238e+00  -4.0734e+00  -9.9920e-01  -1.9434e-02   4.3223e-02   5.3831e-07   5.7908e-02   3.4909e-02   2.0126e-01   6.0353e-02   9.2010e-01   8.5244e-02   9.8002e-01   8.2975e-02
-#> 209:    1.0191e+02  -4.1205e+00  -2.3279e+00  -4.0726e+00  -1.0053e+00  -1.5390e-02   4.1064e-02   5.3171e-07   5.8749e-02   3.4510e-02   1.9942e-01   6.3063e-02   9.3192e-01   8.4436e-02   9.8298e-01   8.3187e-02
-#> 210:    1.0191e+02  -4.1205e+00  -2.3310e+00  -4.0705e+00  -1.0061e+00  -1.3507e-02   3.8265e-02   5.2762e-07   5.9344e-02   3.3374e-02   1.9612e-01   6.7006e-02   9.3199e-01   8.4573e-02   9.8382e-01   8.3227e-02
-#> 211:    1.0191e+02  -4.1205e+00  -2.3383e+00  -4.0683e+00  -1.0043e+00  -1.3973e-02   3.6076e-02   5.2584e-07   6.1568e-02   3.2369e-02   1.9504e-01   6.9982e-02   9.4179e-01   8.4625e-02   9.9145e-01   8.3067e-02
-#> 212:    1.0192e+02  -4.1204e+00  -2.3396e+00  -4.0662e+00  -1.0055e+00  -1.8011e-02   3.4746e-02   5.4375e-07   6.2747e-02   3.1588e-02   1.9405e-01   7.2360e-02   9.4525e-01   8.4466e-02   9.9581e-01   8.2952e-02
-#> 213:    1.0192e+02  -4.1204e+00  -2.3407e+00  -4.0649e+00  -1.0066e+00  -2.1077e-02   3.4708e-02   5.5843e-07   6.1940e-02   3.0715e-02   1.9382e-01   7.4602e-02   9.4611e-01   8.4322e-02   9.9397e-01   8.2717e-02
-#> 214:    1.0192e+02  -4.1204e+00  -2.3392e+00  -4.0648e+00  -1.0076e+00  -2.3417e-02   3.4282e-02   5.8157e-07   6.1893e-02   3.0158e-02   1.9322e-01   7.8942e-02   9.5250e-01   8.3922e-02   9.9723e-01   8.2793e-02
-#> 215:    1.0192e+02  -4.1204e+00  -2.3410e+00  -4.0645e+00  -1.0087e+00  -2.1950e-02   3.5820e-02   6.0691e-07   6.2032e-02   2.9890e-02   1.9172e-01   8.3774e-02   9.5617e-01   8.3280e-02   1.0003e+00   8.2881e-02
-#> 216:    1.0192e+02  -4.1203e+00  -2.3425e+00  -4.0628e+00  -1.0069e+00  -2.4268e-02   3.7597e-02   6.4187e-07   6.1733e-02   2.9353e-02   1.9092e-01   8.8150e-02   9.5834e-01   8.3091e-02   1.0027e+00   8.2753e-02
-#> 217:    1.0192e+02  -4.1203e+00  -2.3439e+00  -4.0613e+00  -1.0064e+00  -2.4197e-02   3.9291e-02   6.5775e-07   6.2318e-02   2.8903e-02   1.8958e-01   9.0470e-02   9.5766e-01   8.3234e-02   1.0020e+00   8.2707e-02
-#> 218:    1.0191e+02  -4.1203e+00  -2.3441e+00  -4.0619e+00  -1.0065e+00  -2.2460e-02   4.0043e-02   6.4921e-07   6.2280e-02   2.8349e-02   1.8800e-01   9.4476e-02   9.5499e-01   8.3416e-02   1.0036e+00   8.2628e-02
-#> 219:    1.0191e+02  -4.1203e+00  -2.3437e+00  -4.0624e+00  -1.0066e+00  -1.9698e-02   3.9735e-02   6.3365e-07   6.2264e-02   2.7720e-02   1.8768e-01   9.7275e-02   9.4994e-01   8.3350e-02   1.0047e+00   8.2572e-02
-#> 220:    1.0191e+02  -4.1203e+00  -2.3447e+00  -4.0630e+00  -1.0070e+00  -1.5871e-02   4.0198e-02   6.3507e-07   6.1981e-02   2.7259e-02   1.8786e-01   9.9168e-02   9.4781e-01   8.3355e-02   1.0046e+00   8.2752e-02
-#> 221:    1.0191e+02  -4.1203e+00  -2.3459e+00  -4.0638e+00  -1.0069e+00  -1.4298e-02   4.0161e-02   6.2865e-07   6.2113e-02   2.7201e-02   1.8810e-01   1.0163e-01   9.4863e-01   8.3154e-02   1.0042e+00   8.2670e-02
-#> 222:    1.0191e+02  -4.1203e+00  -2.3472e+00  -4.0646e+00  -1.0064e+00  -1.0921e-02   4.0310e-02   6.2450e-07   6.2436e-02   2.6979e-02   1.8736e-01   1.0306e-01   9.4914e-01   8.3081e-02   1.0050e+00   8.2757e-02
-#> 223:    1.0191e+02  -4.1203e+00  -2.3478e+00  -4.0650e+00  -1.0063e+00  -1.1053e-02   3.9741e-02   6.1973e-07   6.2918e-02   2.6636e-02   1.8806e-01   1.0506e-01   9.4996e-01   8.2927e-02   1.0054e+00   8.2579e-02
-#> 224:    1.0191e+02  -4.1203e+00  -2.3478e+00  -4.0653e+00  -1.0061e+00  -1.0324e-02   3.9480e-02   6.1421e-07   6.4180e-02   2.6403e-02   1.8833e-01   1.0733e-01   9.4750e-01   8.2697e-02   1.0033e+00   8.2517e-02
-#> 225:    1.0191e+02  -4.1203e+00  -2.3479e+00  -4.0654e+00  -1.0060e+00  -1.0650e-02   3.9188e-02   6.0959e-07   6.3862e-02   2.6122e-02   1.8815e-01   1.0786e-01   9.4504e-01   8.2833e-02   1.0002e+00   8.2398e-02
-#> 226:    1.0192e+02  -4.1204e+00  -2.3469e+00  -4.0657e+00  -1.0052e+00  -1.0205e-02   3.9129e-02   6.0577e-07   6.4045e-02   2.5875e-02   1.8762e-01   1.0921e-01   9.4663e-01   8.2599e-02   9.9857e-01   8.2472e-02
-#> 227:    1.0192e+02  -4.1203e+00  -2.3467e+00  -4.0658e+00  -1.0053e+00  -1.0189e-02   3.8797e-02   6.0837e-07   6.5125e-02   2.5679e-02   1.8721e-01   1.1060e-01   9.4729e-01   8.2470e-02   9.9802e-01   8.2753e-02
-#> 228:    1.0192e+02  -4.1204e+00  -2.3469e+00  -4.0657e+00  -1.0054e+00  -1.0575e-02   3.8741e-02   6.0738e-07   6.5467e-02   2.5448e-02   1.8548e-01   1.1134e-01   9.4840e-01   8.2580e-02   9.9829e-01   8.2888e-02
-#> 229:    1.0192e+02  -4.1204e+00  -2.3479e+00  -4.0650e+00  -1.0056e+00  -1.1215e-02   3.9360e-02   6.0182e-07   6.4817e-02   2.5237e-02   1.8448e-01   1.1090e-01   9.5039e-01   8.2625e-02   9.9900e-01   8.2896e-02
-#> 230:    1.0192e+02  -4.1204e+00  -2.3482e+00  -4.0652e+00  -1.0060e+00  -9.9775e-03   3.9501e-02   5.9385e-07   6.4132e-02   2.5093e-02   1.8510e-01   1.1122e-01   9.4938e-01   8.2763e-02   9.9961e-01   8.2886e-02
-#> 231:    1.0192e+02  -4.1204e+00  -2.3479e+00  -4.0654e+00  -1.0070e+00  -8.9509e-03   3.9907e-02   5.9290e-07   6.3744e-02   2.4829e-02   1.8560e-01   1.1062e-01   9.4790e-01   8.2872e-02   1.0022e+00   8.2955e-02
-#> 232:    1.0192e+02  -4.1204e+00  -2.3484e+00  -4.0657e+00  -1.0081e+00  -6.9066e-03   4.0738e-02   5.7862e-07   6.3242e-02   2.4729e-02   1.8626e-01   1.0975e-01   9.4866e-01   8.2846e-02   1.0036e+00   8.3065e-02
-#> 233:    1.0191e+02  -4.1204e+00  -2.3487e+00  -4.0660e+00  -1.0080e+00  -5.1163e-03   4.0708e-02   5.7326e-07   6.2392e-02   2.4475e-02   1.8701e-01   1.0932e-01   9.4816e-01   8.2933e-02   1.0059e+00   8.3155e-02
-#> 234:    1.0191e+02  -4.1204e+00  -2.3500e+00  -4.0660e+00  -1.0077e+00  -4.0637e-03   4.1065e-02   5.6885e-07   6.1938e-02   2.4418e-02   1.8673e-01   1.0923e-01   9.5001e-01   8.3005e-02   1.0080e+00   8.3207e-02
-#> 235:    1.0191e+02  -4.1204e+00  -2.3526e+00  -4.0653e+00  -1.0074e+00  -3.6541e-03   4.1151e-02   5.6498e-07   6.2228e-02   2.4447e-02   1.8667e-01   1.0995e-01   9.5059e-01   8.3101e-02   1.0055e+00   8.3101e-02
-#> 236:    1.0191e+02  -4.1204e+00  -2.3540e+00  -4.0648e+00  -1.0078e+00  -4.0127e-03   4.0966e-02   5.7047e-07   6.1779e-02   2.4457e-02   1.8777e-01   1.0971e-01   9.4919e-01   8.3203e-02   1.0044e+00   8.3078e-02
-#> 237:    1.0191e+02  -4.1204e+00  -2.3528e+00  -4.0645e+00  -1.0078e+00  -4.4251e-03   4.0491e-02   5.6811e-07   6.1507e-02   2.4421e-02   1.8827e-01   1.1047e-01   9.4870e-01   8.3149e-02   1.0031e+00   8.3008e-02
-#> 238:    1.0190e+02  -4.1204e+00  -2.3517e+00  -4.0647e+00  -1.0076e+00  -5.2540e-03   3.9988e-02   5.6832e-07   6.1612e-02   2.4262e-02   1.8801e-01   1.1019e-01   9.4737e-01   8.3172e-02   1.0037e+00   8.2959e-02
-#> 239:    1.0190e+02  -4.1204e+00  -2.3509e+00  -4.0650e+00  -1.0089e+00  -5.1598e-03   3.9373e-02   5.6396e-07   6.1635e-02   2.4040e-02   1.8812e-01   1.1055e-01   9.4885e-01   8.3140e-02   1.0053e+00   8.2956e-02
-#> 240:    1.0190e+02  -4.1204e+00  -2.3515e+00  -4.0643e+00  -1.0095e+00  -4.5817e-03   3.9031e-02   5.5929e-07   6.2233e-02   2.3840e-02   1.8766e-01   1.1014e-01   9.5018e-01   8.3172e-02   1.0066e+00   8.2937e-02
-#> 241:    1.0190e+02  -4.1204e+00  -2.3524e+00  -4.0642e+00  -1.0097e+00  -3.9061e-03   3.8686e-02   5.5496e-07   6.3349e-02   2.3663e-02   1.8759e-01   1.1046e-01   9.4922e-01   8.3162e-02   1.0064e+00   8.2940e-02
-#> 242:    1.0190e+02  -4.1204e+00  -2.3535e+00  -4.0642e+00  -1.0092e+00  -2.9411e-03   3.8674e-02   5.5359e-07   6.3930e-02   2.3604e-02   1.8742e-01   1.1027e-01   9.4748e-01   8.3177e-02   1.0052e+00   8.2955e-02
-#> 243:    1.0190e+02  -4.1204e+00  -2.3551e+00  -4.0642e+00  -1.0089e+00  -1.6071e-03   3.8635e-02   5.4669e-07   6.4141e-02   2.3570e-02   1.8666e-01   1.1022e-01   9.4770e-01   8.3208e-02   1.0048e+00   8.2962e-02
-#> 244:    1.0190e+02  -4.1204e+00  -2.3566e+00  -4.0645e+00  -1.0093e+00  -7.0474e-04   3.8502e-02   5.4194e-07   6.4399e-02   2.3591e-02   1.8627e-01   1.0938e-01   9.4615e-01   8.3402e-02   1.0043e+00   8.2891e-02
-#> 245:    1.0189e+02  -4.1204e+00  -2.3575e+00  -4.0649e+00  -1.0093e+00   1.3351e-03   3.8372e-02   5.4266e-07   6.4935e-02   2.3511e-02   1.8609e-01   1.0840e-01   9.4586e-01   8.3393e-02   1.0041e+00   8.2835e-02
-#> 246:    1.0189e+02  -4.1204e+00  -2.3595e+00  -4.0655e+00  -1.0085e+00   4.2316e-03   3.8487e-02   5.4393e-07   6.5284e-02   2.3457e-02   1.8581e-01   1.0811e-01   9.4656e-01   8.3372e-02   1.0036e+00   8.2746e-02
-#> 247:    1.0189e+02  -4.1204e+00  -2.3608e+00  -4.0659e+00  -1.0081e+00   6.1314e-03   3.8249e-02   5.4752e-07   6.5440e-02   2.3455e-02   1.8584e-01   1.0706e-01   9.4795e-01   8.3330e-02   1.0025e+00   8.2710e-02
-#> 248:    1.0189e+02  -4.1204e+00  -2.3617e+00  -4.0662e+00  -1.0084e+00   8.1978e-03   3.8017e-02   5.4713e-07   6.5853e-02   2.3439e-02   1.8637e-01   1.0634e-01   9.4748e-01   8.3377e-02   1.0016e+00   8.2677e-02
-#> 249:    1.0189e+02  -4.1204e+00  -2.3633e+00  -4.0667e+00  -1.0085e+00   9.8011e-03   3.7934e-02   5.5069e-07   6.6442e-02   2.3533e-02   1.8652e-01   1.0606e-01   9.4761e-01   8.3449e-02   1.0009e+00   8.2712e-02
-#> 250:    1.0189e+02  -4.1204e+00  -2.3644e+00  -4.0668e+00  -1.0087e+00   1.0992e-02   3.8199e-02   5.5486e-07   6.6746e-02   2.3638e-02   1.8739e-01   1.0611e-01   9.4838e-01   8.3442e-02   9.9958e-01   8.2692e-02
-#> 251:    1.0189e+02  -4.1204e+00  -2.3644e+00  -4.0671e+00  -1.0097e+00   1.2215e-02   3.8648e-02   5.5448e-07   6.6916e-02   2.3592e-02   1.8753e-01   1.0607e-01   9.4773e-01   8.3511e-02   9.9919e-01   8.2701e-02
-#> 252:    1.0189e+02  -4.1204e+00  -2.3645e+00  -4.0671e+00  -1.0100e+00   1.2881e-02   3.8792e-02   5.5615e-07   6.7323e-02   2.3559e-02   1.8811e-01   1.0641e-01   9.4665e-01   8.3575e-02   9.9809e-01   8.2743e-02
-#> 253:    1.0189e+02  -4.1204e+00  -2.3646e+00  -4.0675e+00  -1.0100e+00   1.3605e-02   3.9013e-02   5.5568e-07   6.7625e-02   2.3432e-02   1.8825e-01   1.0688e-01   9.4424e-01   8.3598e-02   9.9825e-01   8.2702e-02
-#> 254:    1.0189e+02  -4.1204e+00  -2.3642e+00  -4.0677e+00  -1.0101e+00   1.3119e-02   3.8838e-02   5.5231e-07   6.7802e-02   2.3429e-02   1.8849e-01   1.0680e-01   9.4281e-01   8.3706e-02   9.9829e-01   8.2631e-02
-#> 255:    1.0189e+02  -4.1204e+00  -2.3627e+00  -4.0679e+00  -1.0104e+00   1.2490e-02   3.8574e-02   5.4955e-07   6.8395e-02   2.3368e-02   1.8890e-01   1.0661e-01   9.4101e-01   8.3756e-02   9.9798e-01   8.2674e-02
-#> 256:    1.0189e+02  -4.1204e+00  -2.3615e+00  -4.0677e+00  -1.0102e+00   1.1525e-02   3.8502e-02   5.4764e-07   6.8824e-02   2.3405e-02   1.8912e-01   1.0649e-01   9.4109e-01   8.3709e-02   9.9811e-01   8.2698e-02
-#> 257:    1.0189e+02  -4.1204e+00  -2.3604e+00  -4.0673e+00  -1.0104e+00   1.0381e-02   3.8286e-02   5.4694e-07   6.9020e-02   2.3338e-02   1.8925e-01   1.0614e-01   9.4075e-01   8.3695e-02   9.9738e-01   8.2689e-02
-#> 258:    1.0189e+02  -4.1204e+00  -2.3591e+00  -4.0670e+00  -1.0103e+00   8.9559e-03   3.7972e-02   5.4665e-07   6.9077e-02   2.3267e-02   1.8919e-01   1.0590e-01   9.4089e-01   8.3618e-02   9.9742e-01   8.2681e-02
-#> 259:    1.0189e+02  -4.1204e+00  -2.3585e+00  -4.0669e+00  -1.0099e+00   8.6011e-03   3.7874e-02   5.4788e-07   6.9455e-02   2.3264e-02   1.8885e-01   1.0519e-01   9.3952e-01   8.3583e-02   9.9610e-01   8.2650e-02
-#> 260:    1.0189e+02  -4.1204e+00  -2.3584e+00  -4.0666e+00  -1.0098e+00   8.0471e-03   3.7771e-02   5.5294e-07   7.0269e-02   2.3292e-02   1.8877e-01   1.0442e-01   9.3898e-01   8.3519e-02   9.9504e-01   8.2641e-02
-#> 261:    1.0189e+02  -4.1204e+00  -2.3583e+00  -4.0664e+00  -1.0100e+00   7.9344e-03   3.7597e-02   5.5650e-07   7.1087e-02   2.3370e-02   1.8867e-01   1.0399e-01   9.3810e-01   8.3488e-02   9.9419e-01   8.2673e-02
-#> 262:    1.0189e+02  -4.1204e+00  -2.3575e+00  -4.0662e+00  -1.0106e+00   7.2123e-03   3.7203e-02   5.5375e-07   7.1794e-02   2.3393e-02   1.8855e-01   1.0356e-01   9.3773e-01   8.3458e-02   9.9406e-01   8.2739e-02
-#> 263:    1.0189e+02  -4.1204e+00  -2.3564e+00  -4.0659e+00  -1.0112e+00   6.6044e-03   3.6977e-02   5.5306e-07   7.2290e-02   2.3475e-02   1.8847e-01   1.0316e-01   9.3744e-01   8.3383e-02   9.9341e-01   8.2818e-02
-#> 264:    1.0189e+02  -4.1204e+00  -2.3549e+00  -4.0657e+00  -1.0118e+00   6.0119e-03   3.6749e-02   5.5152e-07   7.2896e-02   2.3530e-02   1.8849e-01   1.0277e-01   9.3658e-01   8.3443e-02   9.9248e-01   8.2877e-02
-#> 265:    1.0189e+02  -4.1204e+00  -2.3545e+00  -4.0655e+00  -1.0121e+00   5.6547e-03   3.6562e-02   5.4816e-07   7.3238e-02   2.3560e-02   1.8863e-01   1.0269e-01   9.3597e-01   8.3434e-02   9.9139e-01   8.2879e-02
-#> 266:    1.0189e+02  -4.1204e+00  -2.3545e+00  -4.0651e+00  -1.0121e+00   5.0995e-03   3.6357e-02   5.4458e-07   7.3522e-02   2.3561e-02   1.8883e-01   1.0270e-01   9.3607e-01   8.3407e-02   9.9133e-01   8.2857e-02
-#> 267:    1.0189e+02  -4.1204e+00  -2.3541e+00  -4.0648e+00  -1.0122e+00   4.0105e-03   3.6306e-02   5.4160e-07   7.3833e-02   2.3499e-02   1.8889e-01   1.0317e-01   9.3624e-01   8.3359e-02   9.9151e-01   8.2865e-02
-#> 268:    1.0189e+02  -4.1204e+00  -2.3530e+00  -4.0646e+00  -1.0122e+00   3.0925e-03   3.6248e-02   5.3845e-07   7.4663e-02   2.3413e-02   1.8895e-01   1.0371e-01   9.3624e-01   8.3277e-02   9.9210e-01   8.2909e-02
-#> 269:    1.0189e+02  -4.1204e+00  -2.3518e+00  -4.0643e+00  -1.0123e+00   2.0507e-03   3.6181e-02   5.3602e-07   7.5442e-02   2.3291e-02   1.8886e-01   1.0397e-01   9.3581e-01   8.3260e-02   9.9238e-01   8.2898e-02
-#> 270:    1.0189e+02  -4.1204e+00  -2.3513e+00  -4.0640e+00  -1.0127e+00   1.3309e-03   3.5900e-02   5.3234e-07   7.6677e-02   2.3220e-02   1.8860e-01   1.0367e-01   9.3573e-01   8.3250e-02   9.9169e-01   8.2904e-02
-#> 271:    1.0189e+02  -4.1204e+00  -2.3514e+00  -4.0637e+00  -1.0129e+00   1.1237e-03   3.5608e-02   5.3092e-07   7.7065e-02   2.3102e-02   1.8826e-01   1.0384e-01   9.3645e-01   8.3228e-02   9.9173e-01   8.2896e-02
-#> 272:    1.0189e+02  -4.1204e+00  -2.3510e+00  -4.0639e+00  -1.0134e+00   9.7855e-04   3.5328e-02   5.3100e-07   7.7173e-02   2.3014e-02   1.8817e-01   1.0367e-01   9.3538e-01   8.3266e-02   9.9139e-01   8.2943e-02
-#> 273:    1.0189e+02  -4.1204e+00  -2.3501e+00  -4.0643e+00  -1.0133e+00   1.1275e-03   3.5187e-02   5.3298e-07   7.7467e-02   2.2923e-02   1.8793e-01   1.0344e-01   9.3474e-01   8.3194e-02   9.9249e-01   8.2973e-02
-#> 274:    1.0189e+02  -4.1204e+00  -2.3498e+00  -4.0643e+00  -1.0134e+00   1.4524e-03   3.4996e-02   5.3407e-07   7.7929e-02   2.2819e-02   1.8837e-01   1.0316e-01   9.3399e-01   8.3168e-02   9.9307e-01   8.2981e-02
-#> 275:    1.0189e+02  -4.1204e+00  -2.3500e+00  -4.0641e+00  -1.0136e+00   1.3605e-03   3.4786e-02   5.3269e-07   7.8177e-02   2.2747e-02   1.8855e-01   1.0305e-01   9.3319e-01   8.3205e-02   9.9277e-01   8.2938e-02
-#> 276:    1.0189e+02  -4.1204e+00  -2.3504e+00  -4.0641e+00  -1.0136e+00   1.5273e-03   3.4581e-02   5.3172e-07   7.8495e-02   2.2764e-02   1.8824e-01   1.0297e-01   9.3267e-01   8.3223e-02   9.9204e-01   8.2884e-02
-#> 277:    1.0189e+02  -4.1204e+00  -2.3506e+00  -4.0643e+00  -1.0133e+00   1.2961e-03   3.4373e-02   5.2917e-07   7.8721e-02   2.2791e-02   1.8801e-01   1.0288e-01   9.3253e-01   8.3185e-02   9.9192e-01   8.2854e-02
-#> 278:    1.0189e+02  -4.1204e+00  -2.3508e+00  -4.0643e+00  -1.0129e+00   1.1750e-03   3.4396e-02   5.2693e-07   7.8999e-02   2.2787e-02   1.8793e-01   1.0278e-01   9.3279e-01   8.3113e-02   9.9144e-01   8.2856e-02
-#> 279:    1.0189e+02  -4.1204e+00  -2.3507e+00  -4.0642e+00  -1.0126e+00   1.2755e-03   3.4381e-02   5.2405e-07   7.9351e-02   2.2804e-02   1.8779e-01   1.0255e-01   9.3319e-01   8.3049e-02   9.9099e-01   8.2875e-02
-#> 280:    1.0189e+02  -4.1204e+00  -2.3507e+00  -4.0641e+00  -1.0127e+00   6.3408e-04   3.4519e-02   5.2180e-07   7.9825e-02   2.2801e-02   1.8775e-01   1.0292e-01   9.3349e-01   8.2970e-02   9.9076e-01   8.2918e-02
-#> 281:    1.0189e+02  -4.1204e+00  -2.3508e+00  -4.0639e+00  -1.0124e+00   6.2438e-04   3.4782e-02   5.1859e-07   8.0328e-02   2.2816e-02   1.8757e-01   1.0299e-01   9.3299e-01   8.3025e-02   9.9050e-01   8.2897e-02
-#> 282:    1.0189e+02  -4.1205e+00  -2.3511e+00  -4.0641e+00  -1.0122e+00   1.1770e-03   3.4754e-02   5.1798e-07   8.0649e-02   2.2836e-02   1.8766e-01   1.0297e-01   9.3351e-01   8.2989e-02   9.9171e-01   8.2893e-02
-#> 283:    1.0189e+02  -4.1205e+00  -2.3519e+00  -4.0644e+00  -1.0120e+00   2.1716e-03   3.4711e-02   5.1567e-07   8.0910e-02   2.2836e-02   1.8774e-01   1.0270e-01   9.3288e-01   8.3029e-02   9.9246e-01   8.2853e-02
-#> 284:    1.0189e+02  -4.1205e+00  -2.3524e+00  -4.0647e+00  -1.0115e+00   2.6623e-03   3.4646e-02   5.1350e-07   8.1153e-02   2.2950e-02   1.8775e-01   1.0277e-01   9.3238e-01   8.2990e-02   9.9212e-01   8.2836e-02
-#> 285:    1.0189e+02  -4.1205e+00  -2.3531e+00  -4.0649e+00  -1.0116e+00   3.7830e-03   3.4626e-02   5.1216e-07   8.1058e-02   2.3007e-02   1.8782e-01   1.0270e-01   9.3232e-01   8.3017e-02   9.9094e-01   8.2829e-02
-#> 286:    1.0189e+02  -4.1205e+00  -2.3539e+00  -4.0651e+00  -1.0111e+00   5.1752e-03   3.4599e-02   5.0989e-07   8.0970e-02   2.3004e-02   1.8757e-01   1.0254e-01   9.3280e-01   8.3006e-02   9.9130e-01   8.2818e-02
-#> 287:    1.0189e+02  -4.1205e+00  -2.3541e+00  -4.0654e+00  -1.0112e+00   6.3747e-03   3.4592e-02   5.0930e-07   8.1117e-02   2.2959e-02   1.8756e-01   1.0222e-01   9.3212e-01   8.3146e-02   9.9183e-01   8.2863e-02
-#> 288:    1.0189e+02  -4.1205e+00  -2.3540e+00  -4.0656e+00  -1.0115e+00   6.5668e-03   3.4598e-02   5.0976e-07   8.1125e-02   2.2895e-02   1.8782e-01   1.0183e-01   9.3310e-01   8.3169e-02   9.9404e-01   8.2836e-02
-#> 289:    1.0189e+02  -4.1205e+00  -2.3539e+00  -4.0658e+00  -1.0119e+00   7.3521e-03   3.4525e-02   5.1097e-07   8.1097e-02   2.2869e-02   1.8753e-01   1.0126e-01   9.3336e-01   8.3244e-02   9.9435e-01   8.2833e-02
-#> 290:    1.0189e+02  -4.1205e+00  -2.3539e+00  -4.0659e+00  -1.0122e+00   7.5226e-03   3.4377e-02   5.0846e-07   8.1212e-02   2.2831e-02   1.8724e-01   1.0073e-01   9.3292e-01   8.3261e-02   9.9415e-01   8.2837e-02
-#> 291:    1.0189e+02  -4.1205e+00  -2.3536e+00  -4.0659e+00  -1.0122e+00   7.2889e-03   3.4263e-02   5.0823e-07   8.1182e-02   2.2801e-02   1.8711e-01   1.0056e-01   9.3309e-01   8.3300e-02   9.9427e-01   8.2805e-02
-#> 292:    1.0189e+02  -4.1205e+00  -2.3531e+00  -4.0659e+00  -1.0123e+00   7.1827e-03   3.4146e-02   5.0825e-07   8.1696e-02   2.2760e-02   1.8703e-01   1.0039e-01   9.3324e-01   8.3306e-02   9.9379e-01   8.2784e-02
-#> 293:    1.0189e+02  -4.1205e+00  -2.3528e+00  -4.0660e+00  -1.0125e+00   7.7142e-03   3.4126e-02   5.0971e-07   8.2026e-02   2.2705e-02   1.8721e-01   1.0036e-01   9.3316e-01   8.3316e-02   9.9357e-01   8.2756e-02
-#> 294:    1.0188e+02  -4.1204e+00  -2.3529e+00  -4.0663e+00  -1.0126e+00   8.5146e-03   3.4314e-02   5.0823e-07   8.2197e-02   2.2608e-02   1.8743e-01   1.0009e-01   9.3356e-01   8.3308e-02   9.9367e-01   8.2719e-02
-#> 295:    1.0188e+02  -4.1204e+00  -2.3532e+00  -4.0666e+00  -1.0123e+00   9.2199e-03   3.4472e-02   5.0839e-07   8.2550e-02   2.2529e-02   1.8745e-01   9.9731e-02   9.3393e-01   8.3255e-02   9.9373e-01   8.2686e-02
-#> 296:    1.0188e+02  -4.1204e+00  -2.3537e+00  -4.0667e+00  -1.0121e+00   9.7869e-03   3.4678e-02   5.0983e-07   8.3059e-02   2.2497e-02   1.8729e-01   9.9260e-02   9.3395e-01   8.3198e-02   9.9300e-01   8.2681e-02
-#> 297:    1.0188e+02  -4.1204e+00  -2.3540e+00  -4.0670e+00  -1.0118e+00   1.0166e-02   3.4957e-02   5.1049e-07   8.3080e-02   2.2448e-02   1.8710e-01   9.8969e-02   9.3321e-01   8.3178e-02   9.9255e-01   8.2663e-02
-#> 298:    1.0188e+02  -4.1204e+00  -2.3544e+00  -4.0673e+00  -1.0117e+00   1.0649e-02   3.5259e-02   5.1103e-07   8.3179e-02   2.2383e-02   1.8704e-01   9.8442e-02   9.3227e-01   8.3199e-02   9.9266e-01   8.2646e-02
-#> 299:    1.0188e+02  -4.1204e+00  -2.3542e+00  -4.0676e+00  -1.0117e+00   1.0927e-02   3.5438e-02   5.1128e-07   8.3068e-02   2.2378e-02   1.8699e-01   9.8203e-02   9.3263e-01   8.3138e-02   9.9353e-01   8.2671e-02
-#> 300:    1.0188e+02  -4.1204e+00  -2.3544e+00  -4.0678e+00  -1.0116e+00   1.1083e-02   3.5694e-02   5.1107e-07   8.2896e-02   2.2344e-02   1.8733e-01   9.7775e-02   9.3179e-01   8.3124e-02   9.9379e-01   8.2657e-02
-#> 301:    1.0188e+02  -4.1204e+00  -2.3542e+00  -4.0680e+00  -1.0115e+00   1.0992e-02   3.5896e-02   5.1262e-07   8.2816e-02   2.2349e-02   1.8753e-01   9.7431e-02   9.3209e-01   8.3086e-02   9.9388e-01   8.2674e-02
-#> 302:    1.0188e+02  -4.1204e+00  -2.3540e+00  -4.0681e+00  -1.0113e+00   1.0410e-02   3.6050e-02   5.1256e-07   8.2817e-02   2.2308e-02   1.8734e-01   9.7153e-02   9.3221e-01   8.3073e-02   9.9402e-01   8.2670e-02
-#> 303:    1.0188e+02  -4.1204e+00  -2.3540e+00  -4.0681e+00  -1.0112e+00   1.0301e-02   3.6150e-02   5.1127e-07   8.2826e-02   2.2325e-02   1.8730e-01   9.6656e-02   9.3192e-01   8.3040e-02   9.9393e-01   8.2665e-02
-#> 304:    1.0188e+02  -4.1204e+00  -2.3536e+00  -4.0681e+00  -1.0113e+00   1.0235e-02   3.6393e-02   5.1176e-07   8.2606e-02   2.2353e-02   1.8724e-01   9.6171e-02   9.3161e-01   8.3068e-02   9.9361e-01   8.2698e-02
-#> 305:    1.0188e+02  -4.1204e+00  -2.3533e+00  -4.0683e+00  -1.0112e+00   9.9655e-03   3.6369e-02   5.1442e-07   8.2520e-02   2.2378e-02   1.8707e-01   9.5656e-02   9.3113e-01   8.3109e-02   9.9338e-01   8.2731e-02
-#> 306:    1.0188e+02  -4.1204e+00  -2.3531e+00  -4.0684e+00  -1.0110e+00   9.9701e-03   3.6346e-02   5.1546e-07   8.2789e-02   2.2360e-02   1.8702e-01   9.5116e-02   9.3102e-01   8.3065e-02   9.9405e-01   8.2761e-02
-#> 307:    1.0188e+02  -4.1204e+00  -2.3530e+00  -4.0684e+00  -1.0112e+00   1.0194e-02   3.6300e-02   5.1196e-07   8.3035e-02   2.2381e-02   1.8704e-01   9.4760e-02   9.3082e-01   8.3003e-02   9.9410e-01   8.2779e-02
-#> 308:    1.0189e+02  -4.1204e+00  -2.3530e+00  -4.0685e+00  -1.0109e+00   9.9531e-03   3.6400e-02   5.1140e-07   8.3511e-02   2.2334e-02   1.8726e-01   9.4494e-02   9.3151e-01   8.2910e-02   9.9484e-01   8.2760e-02
-#> 309:    1.0188e+02  -4.1204e+00  -2.3530e+00  -4.0685e+00  -1.0107e+00   1.0089e-02   3.6382e-02   5.1081e-07   8.3917e-02   2.2276e-02   1.8728e-01   9.4285e-02   9.3133e-01   8.2875e-02   9.9545e-01   8.2757e-02
-#> 310:    1.0188e+02  -4.1204e+00  -2.3533e+00  -4.0685e+00  -1.0105e+00   1.0805e-02   3.6375e-02   5.1041e-07   8.4245e-02   2.2246e-02   1.8753e-01   9.3894e-02   9.3052e-01   8.2899e-02   9.9500e-01   8.2743e-02
-#> 311:    1.0188e+02  -4.1204e+00  -2.3534e+00  -4.0685e+00  -1.0103e+00   1.1449e-02   3.6311e-02   5.0884e-07   8.4434e-02   2.2231e-02   1.8783e-01   9.3542e-02   9.3039e-01   8.2864e-02   9.9458e-01   8.2733e-02
-#> 312:    1.0188e+02  -4.1204e+00  -2.3535e+00  -4.0685e+00  -1.0102e+00   1.2173e-02   3.6373e-02   5.0821e-07   8.4730e-02   2.2176e-02   1.8769e-01   9.3317e-02   9.2982e-01   8.2916e-02   9.9438e-01   8.2740e-02
-#> 313:    1.0188e+02  -4.1204e+00  -2.3533e+00  -4.0688e+00  -1.0103e+00   1.2812e-02   3.6558e-02   5.0751e-07   8.5211e-02   2.2131e-02   1.8754e-01   9.3387e-02   9.2962e-01   8.2892e-02   9.9458e-01   8.2741e-02
-#> 314:    1.0188e+02  -4.1204e+00  -2.3534e+00  -4.0690e+00  -1.0103e+00   1.3241e-02   3.6680e-02   5.0887e-07   8.5667e-02   2.2079e-02   1.8772e-01   9.3442e-02   9.2941e-01   8.2947e-02   9.9511e-01   8.2743e-02
-#> 315:    1.0188e+02  -4.1204e+00  -2.3534e+00  -4.0691e+00  -1.0104e+00   1.3699e-02   3.6924e-02   5.0965e-07   8.5865e-02   2.2028e-02   1.8766e-01   9.3264e-02   9.2904e-01   8.2986e-02   9.9543e-01   8.2763e-02
-#> 316:    1.0188e+02  -4.1204e+00  -2.3533e+00  -4.0693e+00  -1.0103e+00   1.4121e-02   3.7218e-02   5.1041e-07   8.6216e-02   2.2076e-02   1.8773e-01   9.3035e-02   9.2917e-01   8.3013e-02   9.9486e-01   8.2782e-02
-#> 317:    1.0188e+02  -4.1204e+00  -2.3533e+00  -4.0694e+00  -1.0102e+00   1.4588e-02   3.7304e-02   5.0994e-07   8.6513e-02   2.2128e-02   1.8773e-01   9.2766e-02   9.2943e-01   8.3025e-02   9.9441e-01   8.2779e-02
-#> 318:    1.0188e+02  -4.1204e+00  -2.3534e+00  -4.0693e+00  -1.0101e+00   1.4714e-02   3.7538e-02   5.0773e-07   8.6801e-02   2.2128e-02   1.8767e-01   9.2698e-02   9.2907e-01   8.3052e-02   9.9378e-01   8.2780e-02
-#> 319:    1.0187e+02  -4.1204e+00  -2.3533e+00  -4.0692e+00  -1.0099e+00   1.4582e-02   3.7563e-02   5.0550e-07   8.6669e-02   2.2135e-02   1.8775e-01   9.2604e-02   9.2925e-01   8.3042e-02   9.9356e-01   8.2773e-02
-#> 320:    1.0187e+02  -4.1204e+00  -2.3535e+00  -4.0690e+00  -1.0102e+00   1.4511e-02   3.7580e-02   5.0281e-07   8.6617e-02   2.2121e-02   1.8780e-01   9.2508e-02   9.3001e-01   8.3032e-02   9.9322e-01   8.2780e-02
-#> 321:    1.0187e+02  -4.1204e+00  -2.3534e+00  -4.0688e+00  -1.0100e+00   1.4288e-02   3.7624e-02   5.0172e-07   8.6311e-02   2.2115e-02   1.8783e-01   9.2445e-02   9.3011e-01   8.3054e-02   9.9288e-01   8.2772e-02
-#> 322:    1.0187e+02  -4.1204e+00  -2.3532e+00  -4.0687e+00  -1.0098e+00   1.3834e-02   3.7497e-02   5.0086e-07   8.6187e-02   2.2111e-02   1.8791e-01   9.2699e-02   9.3037e-01   8.3069e-02   9.9284e-01   8.2773e-02
-#> 323:    1.0187e+02  -4.1204e+00  -2.3524e+00  -4.0683e+00  -1.0097e+00   1.2977e-02   3.7420e-02   4.9925e-07   8.6082e-02   2.2084e-02   1.8818e-01   9.3123e-02   9.3036e-01   8.3012e-02   9.9265e-01   8.2813e-02
-#> 324:    1.0187e+02  -4.1204e+00  -2.3523e+00  -4.0682e+00  -1.0096e+00   1.2679e-02   3.7420e-02   4.9836e-07   8.5721e-02   2.2071e-02   1.8829e-01   9.3535e-02   9.3062e-01   8.3011e-02   9.9241e-01   8.2827e-02
-#> 325:    1.0187e+02  -4.1204e+00  -2.3520e+00  -4.0680e+00  -1.0094e+00   1.2196e-02   3.7298e-02   4.9735e-07   8.5411e-02   2.2028e-02   1.8848e-01   9.3706e-02   9.3043e-01   8.3020e-02   9.9256e-01   8.2826e-02
-#> 326:    1.0187e+02  -4.1204e+00  -2.3517e+00  -4.0678e+00  -1.0091e+00   1.1924e-02   3.7185e-02   4.9661e-07   8.5453e-02   2.1983e-02   1.8830e-01   9.3688e-02   9.3050e-01   8.2996e-02   9.9284e-01   8.2806e-02
-#> 327:    1.0187e+02  -4.1204e+00  -2.3516e+00  -4.0677e+00  -1.0090e+00   1.1449e-02   3.7155e-02   4.9755e-07   8.5761e-02   2.1967e-02   1.8819e-01   9.3936e-02   9.3052e-01   8.2912e-02   9.9245e-01   8.2806e-02
-#> 328:    1.0187e+02  -4.1204e+00  -2.3514e+00  -4.0675e+00  -1.0089e+00   1.0758e-02   3.7146e-02   4.9892e-07   8.6019e-02   2.1971e-02   1.8806e-01   9.4361e-02   9.3070e-01   8.2833e-02   9.9182e-01   8.2840e-02
-#> 329:    1.0187e+02  -4.1204e+00  -2.3515e+00  -4.0672e+00  -1.0087e+00   1.0256e-02   3.7342e-02   5.0019e-07   8.5965e-02   2.1989e-02   1.8796e-01   9.4614e-02   9.3067e-01   8.2818e-02   9.9159e-01   8.2858e-02
-#> 330:    1.0187e+02  -4.1204e+00  -2.3520e+00  -4.0670e+00  -1.0086e+00   1.0021e-02   3.7376e-02   4.9911e-07   8.6124e-02   2.1978e-02   1.8796e-01   9.4836e-02   9.3036e-01   8.2819e-02   9.9148e-01   8.2866e-02
-#> 331:    1.0187e+02  -4.1204e+00  -2.3521e+00  -4.0668e+00  -1.0086e+00   9.5790e-03   3.7296e-02   4.9753e-07   8.6122e-02   2.1951e-02   1.8782e-01   9.5042e-02   9.3064e-01   8.2783e-02   9.9196e-01   8.2863e-02
-#> 332:    1.0187e+02  -4.1204e+00  -2.3523e+00  -4.0667e+00  -1.0085e+00   9.2971e-03   3.7221e-02   4.9729e-07   8.6215e-02   2.1952e-02   1.8787e-01   9.5082e-02   9.3103e-01   8.2782e-02   9.9224e-01   8.2861e-02
-#> 333:    1.0187e+02  -4.1204e+00  -2.3524e+00  -4.0667e+00  -1.0084e+00   9.2591e-03   3.7097e-02   4.9556e-07   8.6302e-02   2.1922e-02   1.8792e-01   9.5155e-02   9.3058e-01   8.2798e-02   9.9202e-01   8.2831e-02
-#> 334:    1.0187e+02  -4.1204e+00  -2.3528e+00  -4.0667e+00  -1.0082e+00   9.5799e-03   3.6997e-02   4.9398e-07   8.6409e-02   2.1911e-02   1.8792e-01   9.5231e-02   9.3035e-01   8.2810e-02   9.9157e-01   8.2803e-02
-#> 335:    1.0187e+02  -4.1204e+00  -2.3529e+00  -4.0667e+00  -1.0080e+00   9.5724e-03   3.6912e-02   4.9206e-07   8.6310e-02   2.1923e-02   1.8791e-01   9.5379e-02   9.3054e-01   8.2759e-02   9.9143e-01   8.2776e-02
-#> 336:    1.0187e+02  -4.1204e+00  -2.3525e+00  -4.0667e+00  -1.0082e+00   9.5794e-03   3.6983e-02   4.9255e-07   8.6282e-02   2.1882e-02   1.8789e-01   9.5422e-02   9.3064e-01   8.2705e-02   9.9138e-01   8.2778e-02
-#> 337:    1.0187e+02  -4.1204e+00  -2.3525e+00  -4.0669e+00  -1.0083e+00   1.0008e-02   3.6943e-02   4.9278e-07   8.6483e-02   2.1844e-02   1.8794e-01   9.5240e-02   9.2981e-01   8.2753e-02   9.9100e-01   8.2774e-02
-#> 338:    1.0187e+02  -4.1204e+00  -2.3525e+00  -4.0669e+00  -1.0084e+00   1.0297e-02   3.6869e-02   4.9309e-07   8.6547e-02   2.1803e-02   1.8808e-01   9.5094e-02   9.2978e-01   8.2764e-02   9.9113e-01   8.2759e-02
-#> 339:    1.0187e+02  -4.1204e+00  -2.3528e+00  -4.0669e+00  -1.0083e+00   1.0465e-02   3.6822e-02   4.9257e-07   8.6779e-02   2.1769e-02   1.8813e-01   9.5062e-02   9.3020e-01   8.2702e-02   9.9135e-01   8.2750e-02
-#> 340:    1.0187e+02  -4.1204e+00  -2.3531e+00  -4.0668e+00  -1.0083e+00   1.0321e-02   3.6733e-02   4.9228e-07   8.7033e-02   2.1721e-02   1.8827e-01   9.4862e-02   9.3062e-01   8.2698e-02   9.9195e-01   8.2729e-02
-#> 341:    1.0187e+02  -4.1204e+00  -2.3531e+00  -4.0670e+00  -1.0085e+00   1.0501e-02   3.6671e-02   4.9236e-07   8.7297e-02   2.1733e-02   1.8820e-01   9.4558e-02   9.3121e-01   8.2677e-02   9.9238e-01   8.2713e-02
-#> 342:    1.0187e+02  -4.1204e+00  -2.3534e+00  -4.0670e+00  -1.0085e+00   1.0818e-02   3.6715e-02   4.9084e-07   8.7450e-02   2.1726e-02   1.8801e-01   9.4252e-02   9.3160e-01   8.2657e-02   9.9232e-01   8.2708e-02
-#> 343:    1.0187e+02  -4.1204e+00  -2.3535e+00  -4.0670e+00  -1.0087e+00   1.1046e-02   3.6784e-02   4.8872e-07   8.7718e-02   2.1718e-02   1.8799e-01   9.3887e-02   9.3187e-01   8.2645e-02   9.9225e-01   8.2722e-02
-#> 344:    1.0187e+02  -4.1204e+00  -2.3535e+00  -4.0670e+00  -1.0087e+00   1.0652e-02   3.6736e-02   4.8852e-07   8.7725e-02   2.1730e-02   1.8792e-01   9.3731e-02   9.3202e-01   8.2618e-02   9.9218e-01   8.2712e-02
-#> 345:    1.0187e+02  -4.1204e+00  -2.3536e+00  -4.0669e+00  -1.0090e+00   1.0445e-02   3.6714e-02   4.8690e-07   8.7782e-02   2.1751e-02   1.8798e-01   9.3450e-02   9.3223e-01   8.2582e-02   9.9239e-01   8.2706e-02
-#> 346:    1.0187e+02  -4.1204e+00  -2.3536e+00  -4.0668e+00  -1.0089e+00   1.0173e-02   3.6743e-02   4.8656e-07   8.7733e-02   2.1810e-02   1.8814e-01   9.3151e-02   9.3257e-01   8.2575e-02   9.9181e-01   8.2708e-02
-#> 347:    1.0187e+02  -4.1205e+00  -2.3532e+00  -4.0668e+00  -1.0089e+00   9.9457e-03   3.6808e-02   4.8756e-07   8.7948e-02   2.1819e-02   1.8813e-01   9.3040e-02   9.3255e-01   8.2599e-02   9.9124e-01   8.2727e-02
-#> 348:    1.0187e+02  -4.1205e+00  -2.3534e+00  -4.0667e+00  -1.0090e+00   9.8498e-03   3.6967e-02   4.8883e-07   8.7998e-02   2.1841e-02   1.8820e-01   9.3180e-02   9.3259e-01   8.2612e-02   9.9148e-01   8.2750e-02
-#> 349:    1.0187e+02  -4.1205e+00  -2.3535e+00  -4.0668e+00  -1.0091e+00   9.6211e-03   3.6891e-02   4.8867e-07   8.8006e-02   2.1930e-02   1.8819e-01   9.3390e-02   9.3232e-01   8.2612e-02   9.9073e-01   8.2738e-02
-#> 350:    1.0187e+02  -4.1205e+00  -2.3534e+00  -4.0669e+00  -1.0090e+00   9.7176e-03   3.6813e-02   4.8925e-07   8.7923e-02   2.1964e-02   1.8820e-01   9.3434e-02   9.3224e-01   8.2600e-02   9.9031e-01   8.2734e-02
-#> 351:    1.0187e+02  -4.1204e+00  -2.3535e+00  -4.0669e+00  -1.0090e+00   9.6652e-03   3.6769e-02   4.8873e-07   8.7985e-02   2.2046e-02   1.8814e-01   9.3529e-02   9.3220e-01   8.2558e-02   9.8978e-01   8.2728e-02
-#> 352:    1.0187e+02  -4.1204e+00  -2.3536e+00  -4.0669e+00  -1.0089e+00   9.8745e-03   3.6732e-02   4.8969e-07   8.8016e-02   2.2094e-02   1.8799e-01   9.3644e-02   9.3168e-01   8.2577e-02   9.8913e-01   8.2722e-02
-#> 353:    1.0187e+02  -4.1204e+00  -2.3537e+00  -4.0669e+00  -1.0088e+00   9.7530e-03   3.6700e-02   4.9008e-07   8.7949e-02   2.2116e-02   1.8798e-01   9.3769e-02   9.3165e-01   8.2559e-02   9.8871e-01   8.2711e-02
-#> 354:    1.0187e+02  -4.1204e+00  -2.3538e+00  -4.0667e+00  -1.0089e+00   9.4103e-03   3.6653e-02   4.9045e-07   8.7894e-02   2.2118e-02   1.8793e-01   9.3872e-02   9.3188e-01   8.2551e-02   9.8887e-01   8.2692e-02
-#> 355:    1.0187e+02  -4.1204e+00  -2.3540e+00  -4.0666e+00  -1.0088e+00   9.1684e-03   3.6536e-02   4.9125e-07   8.7920e-02   2.2107e-02   1.8812e-01   9.4123e-02   9.3223e-01   8.2517e-02   9.8893e-01   8.2687e-02
-#> 356:    1.0187e+02  -4.1204e+00  -2.3542e+00  -4.0664e+00  -1.0086e+00   8.9025e-03   3.6431e-02   4.9325e-07   8.7949e-02   2.2110e-02   1.8827e-01   9.4135e-02   9.3252e-01   8.2503e-02   9.8907e-01   8.2649e-02
-#> 357:    1.0187e+02  -4.1204e+00  -2.3542e+00  -4.0663e+00  -1.0085e+00   8.6757e-03   3.6417e-02   4.9505e-07   8.8052e-02   2.2096e-02   1.8848e-01   9.4192e-02   9.3281e-01   8.2490e-02   9.8957e-01   8.2624e-02
-#> 358:    1.0187e+02  -4.1204e+00  -2.3542e+00  -4.0661e+00  -1.0084e+00   8.1812e-03   3.6349e-02   4.9610e-07   8.8344e-02   2.2104e-02   1.8844e-01   9.4129e-02   9.3294e-01   8.2493e-02   9.8977e-01   8.2614e-02
-#> 359:    1.0187e+02  -4.1204e+00  -2.3541e+00  -4.0659e+00  -1.0083e+00   8.0905e-03   3.6475e-02   4.9675e-07   8.8647e-02   2.2116e-02   1.8831e-01   9.4146e-02   9.3322e-01   8.2473e-02   9.8978e-01   8.2622e-02
-#> 360:    1.0187e+02  -4.1204e+00  -2.3542e+00  -4.0657e+00  -1.0082e+00   7.8390e-03   3.6468e-02   4.9649e-07   8.8981e-02   2.2120e-02   1.8815e-01   9.4249e-02   9.3361e-01   8.2430e-02   9.8997e-01   8.2616e-02
-#> 361:    1.0187e+02  -4.1204e+00  -2.3545e+00  -4.0656e+00  -1.0083e+00   7.9104e-03   3.6434e-02   4.9737e-07   8.9447e-02   2.2133e-02   1.8808e-01   9.4085e-02   9.3387e-01   8.2426e-02   9.9025e-01   8.2616e-02
-#> 362:    1.0187e+02  -4.1204e+00  -2.3547e+00  -4.0655e+00  -1.0087e+00   7.6341e-03   3.6428e-02   4.9748e-07   8.9872e-02   2.2148e-02   1.8805e-01   9.4025e-02   9.3407e-01   8.2456e-02   9.9070e-01   8.2609e-02
-#> 363:    1.0187e+02  -4.1204e+00  -2.3546e+00  -4.0653e+00  -1.0087e+00   7.2351e-03   3.6392e-02   4.9842e-07   9.0125e-02   2.2179e-02   1.8818e-01   9.4157e-02   9.3437e-01   8.2439e-02   9.9051e-01   8.2626e-02
-#> 364:    1.0187e+02  -4.1204e+00  -2.3543e+00  -4.0651e+00  -1.0089e+00   6.7851e-03   3.6303e-02   4.9890e-07   9.0448e-02   2.2189e-02   1.8831e-01   9.4432e-02   9.3513e-01   8.2433e-02   9.9051e-01   8.2655e-02
-#> 365:    1.0187e+02  -4.1204e+00  -2.3538e+00  -4.0650e+00  -1.0089e+00   6.2935e-03   3.6267e-02   4.9829e-07   9.0718e-02   2.2204e-02   1.8818e-01   9.4507e-02   9.3580e-01   8.2387e-02   9.9049e-01   8.2678e-02
-#> 366:    1.0187e+02  -4.1204e+00  -2.3535e+00  -4.0649e+00  -1.0088e+00   5.8910e-03   3.6339e-02   4.9911e-07   9.0727e-02   2.2231e-02   1.8801e-01   9.4683e-02   9.3567e-01   8.2359e-02   9.8997e-01   8.2681e-02
-#> 367:    1.0187e+02  -4.1204e+00  -2.3533e+00  -4.0649e+00  -1.0088e+00   5.8610e-03   3.6366e-02   5.0123e-07   9.0732e-02   2.2245e-02   1.8793e-01   9.4666e-02   9.3556e-01   8.2339e-02   9.8945e-01   8.2691e-02
-#> 368:    1.0187e+02  -4.1204e+00  -2.3531e+00  -4.0650e+00  -1.0088e+00   6.1043e-03   3.6424e-02   5.0107e-07   9.0729e-02   2.2248e-02   1.8780e-01   9.4599e-02   9.3554e-01   8.2315e-02   9.8903e-01   8.2705e-02
-#> 369:    1.0187e+02  -4.1204e+00  -2.3530e+00  -4.0650e+00  -1.0088e+00   6.1767e-03   3.6436e-02   5.0046e-07   9.0617e-02   2.2226e-02   1.8787e-01   9.4410e-02   9.3504e-01   8.2361e-02   9.8843e-01   8.2694e-02
-#> 370:    1.0187e+02  -4.1204e+00  -2.3528e+00  -4.0651e+00  -1.0088e+00   6.2532e-03   3.6467e-02   5.0024e-07   9.0741e-02   2.2223e-02   1.8794e-01   9.4288e-02   9.3472e-01   8.2374e-02   9.8781e-01   8.2703e-02
-#> 371:    1.0186e+02  -4.1204e+00  -2.3525e+00  -4.0652e+00  -1.0088e+00   6.2117e-03   3.6465e-02   4.9964e-07   9.0904e-02   2.2220e-02   1.8788e-01   9.4310e-02   9.3470e-01   8.2367e-02   9.8730e-01   8.2731e-02
-#> 372:    1.0186e+02  -4.1204e+00  -2.3524e+00  -4.0651e+00  -1.0089e+00   6.1363e-03   3.6367e-02   5.0037e-07   9.1177e-02   2.2230e-02   1.8783e-01   9.4288e-02   9.3496e-01   8.2365e-02   9.8699e-01   8.2729e-02
-#> 373:    1.0186e+02  -4.1204e+00  -2.3523e+00  -4.0650e+00  -1.0089e+00   6.0384e-03   3.6353e-02   5.0195e-07   9.1402e-02   2.2219e-02   1.8764e-01   9.4343e-02   9.3478e-01   8.2430e-02   9.8641e-01   8.2747e-02
-#> 374:    1.0186e+02  -4.1204e+00  -2.3523e+00  -4.0650e+00  -1.0091e+00   5.9821e-03   3.6377e-02   5.0424e-07   9.1532e-02   2.2243e-02   1.8761e-01   9.4219e-02   9.3466e-01   8.2418e-02   9.8614e-01   8.2735e-02
-#> 375:    1.0186e+02  -4.1204e+00  -2.3524e+00  -4.0649e+00  -1.0091e+00   5.8843e-03   3.6358e-02   5.0568e-07   9.1556e-02   2.2250e-02   1.8768e-01   9.4173e-02   9.3432e-01   8.2413e-02   9.8592e-01   8.2728e-02
-#> 376:    1.0186e+02  -4.1204e+00  -2.3526e+00  -4.0649e+00  -1.0090e+00   5.7256e-03   3.6406e-02   5.0673e-07   9.1590e-02   2.2260e-02   1.8765e-01   9.4159e-02   9.3417e-01   8.2400e-02   9.8565e-01   8.2701e-02
-#> 377:    1.0186e+02  -4.1204e+00  -2.3527e+00  -4.0647e+00  -1.0091e+00   5.2782e-03   3.6397e-02   5.0740e-07   9.1564e-02   2.2263e-02   1.8765e-01   9.4084e-02   9.3434e-01   8.2395e-02   9.8563e-01   8.2680e-02
-#> 378:    1.0186e+02  -4.1204e+00  -2.3524e+00  -4.0646e+00  -1.0091e+00   4.8184e-03   3.6478e-02   5.0759e-07   9.1590e-02   2.2213e-02   1.8766e-01   9.4162e-02   9.3432e-01   8.2353e-02   9.8595e-01   8.2681e-02
-#> 379:    1.0186e+02  -4.1204e+00  -2.3521e+00  -4.0646e+00  -1.0089e+00   4.4861e-03   3.6557e-02   5.0710e-07   9.1595e-02   2.2159e-02   1.8767e-01   9.3894e-02   9.3395e-01   8.2341e-02   9.8636e-01   8.2671e-02
-#> 380:    1.0186e+02  -4.1204e+00  -2.3517e+00  -4.0644e+00  -1.0089e+00   3.9799e-03   3.6543e-02   5.0682e-07   9.1532e-02   2.2143e-02   1.8768e-01   9.3854e-02   9.3372e-01   8.2331e-02   9.8640e-01   8.2678e-02
-#> 381:    1.0186e+02  -4.1204e+00  -2.3515e+00  -4.0643e+00  -1.0089e+00   3.6269e-03   3.6531e-02   5.0770e-07   9.1364e-02   2.2157e-02   1.8768e-01   9.3897e-02   9.3383e-01   8.2326e-02   9.8630e-01   8.2675e-02
-#> 382:    1.0186e+02  -4.1204e+00  -2.3513e+00  -4.0643e+00  -1.0090e+00   3.1691e-03   3.6469e-02   5.0860e-07   9.1318e-02   2.2188e-02   1.8767e-01   9.3787e-02   9.3433e-01   8.2306e-02   9.8643e-01   8.2670e-02
-#> 383:    1.0186e+02  -4.1204e+00  -2.3508e+00  -4.0642e+00  -1.0090e+00   2.6209e-03   3.6416e-02   5.0893e-07   9.1374e-02   2.2165e-02   1.8759e-01   9.3654e-02   9.3443e-01   8.2289e-02   9.8663e-01   8.2672e-02
-#> 384:    1.0186e+02  -4.1204e+00  -2.3505e+00  -4.0640e+00  -1.0090e+00   2.1556e-03   3.6403e-02   5.0834e-07   9.1550e-02   2.2148e-02   1.8750e-01   9.3422e-02   9.3444e-01   8.2277e-02   9.8639e-01   8.2670e-02
-#> 385:    1.0186e+02  -4.1204e+00  -2.3505e+00  -4.0638e+00  -1.0089e+00   1.7048e-03   3.6391e-02   5.0788e-07   9.1717e-02   2.2160e-02   1.8746e-01   9.3178e-02   9.3457e-01   8.2261e-02   9.8616e-01   8.2636e-02
-#> 386:    1.0186e+02  -4.1204e+00  -2.3504e+00  -4.0637e+00  -1.0089e+00   1.4309e-03   3.6372e-02   5.0847e-07   9.1895e-02   2.2157e-02   1.8754e-01   9.2918e-02   9.3439e-01   8.2246e-02   9.8601e-01   8.2617e-02
-#> 387:    1.0186e+02  -4.1204e+00  -2.3505e+00  -4.0636e+00  -1.0089e+00   1.3524e-03   3.6446e-02   5.0896e-07   9.2022e-02   2.2182e-02   1.8768e-01   9.2684e-02   9.3470e-01   8.2216e-02   9.8593e-01   8.2620e-02
-#> 388:    1.0186e+02  -4.1204e+00  -2.3506e+00  -4.0635e+00  -1.0089e+00   1.2887e-03   3.6478e-02   5.0904e-07   9.2117e-02   2.2174e-02   1.8761e-01   9.2506e-02   9.3463e-01   8.2221e-02   9.8563e-01   8.2609e-02
-#> 389:    1.0186e+02  -4.1204e+00  -2.3505e+00  -4.0635e+00  -1.0089e+00   1.2044e-03   3.6479e-02   5.0969e-07   9.2068e-02   2.2180e-02   1.8751e-01   9.2308e-02   9.3438e-01   8.2241e-02   9.8516e-01   8.2592e-02
-#> 390:    1.0186e+02  -4.1204e+00  -2.3506e+00  -4.0635e+00  -1.0087e+00   1.1442e-03   3.6497e-02   5.0878e-07   9.1995e-02   2.2156e-02   1.8744e-01   9.2169e-02   9.3410e-01   8.2257e-02   9.8511e-01   8.2581e-02
-#> 391:    1.0186e+02  -4.1204e+00  -2.3508e+00  -4.0635e+00  -1.0089e+00   1.0925e-03   3.6454e-02   5.0876e-07   9.1945e-02   2.2177e-02   1.8739e-01   9.1989e-02   9.3439e-01   8.2254e-02   9.8472e-01   8.2579e-02
-#> 392:    1.0186e+02  -4.1204e+00  -2.3506e+00  -4.0633e+00  -1.0091e+00   7.9940e-04   3.6417e-02   5.0874e-07   9.1956e-02   2.2185e-02   1.8730e-01   9.1977e-02   9.3422e-01   8.2244e-02   9.8463e-01   8.2589e-02
-#> 393:    1.0186e+02  -4.1204e+00  -2.3504e+00  -4.0632e+00  -1.0093e+00   4.2112e-04   3.6433e-02   5.0843e-07   9.1868e-02   2.2211e-02   1.8739e-01   9.2106e-02   9.3464e-01   8.2217e-02   9.8458e-01   8.2594e-02
-#> 394:    1.0186e+02  -4.1204e+00  -2.3502e+00  -4.0631e+00  -1.0093e+00   1.4926e-04   3.6534e-02   5.0862e-07   9.1713e-02   2.2244e-02   1.8735e-01   9.2105e-02   9.3454e-01   8.2239e-02   9.8410e-01   8.2601e-02
-#> 395:    1.0186e+02  -4.1204e+00  -2.3499e+00  -4.0630e+00  -1.0095e+00   5.2506e-05   3.6667e-02   5.0955e-07   9.1548e-02   2.2269e-02   1.8733e-01   9.2151e-02   9.3450e-01   8.2256e-02   9.8389e-01   8.2612e-02
-#> 396:    1.0186e+02  -4.1204e+00  -2.3497e+00  -4.0630e+00  -1.0097e+00   1.6581e-05   3.6789e-02   5.1002e-07   9.1431e-02   2.2299e-02   1.8742e-01   9.2120e-02   9.3450e-01   8.2252e-02   9.8367e-01   8.2620e-02
-#> 397:    1.0186e+02  -4.1205e+00  -2.3495e+00  -4.0629e+00  -1.0098e+00  -5.0310e-05   3.6860e-02   5.0949e-07   9.1311e-02   2.2323e-02   1.8738e-01   9.2130e-02   9.3467e-01   8.2250e-02   9.8388e-01   8.2628e-02
-#> 398:    1.0186e+02  -4.1205e+00  -2.3494e+00  -4.0629e+00  -1.0097e+00  -1.4918e-04   3.6902e-02   5.0935e-07   9.1211e-02   2.2330e-02   1.8747e-01   9.2144e-02   9.3478e-01   8.2260e-02   9.8420e-01   8.2632e-02
-#> 399:    1.0186e+02  -4.1205e+00  -2.3497e+00  -4.0628e+00  -1.0097e+00  -2.2152e-04   3.6932e-02   5.0927e-07   9.1209e-02   2.2377e-02   1.8750e-01   9.2136e-02   9.3481e-01   8.2286e-02   9.8431e-01   8.2622e-02
-#> 400:    1.0186e+02  -4.1205e+00  -2.3499e+00  -4.0629e+00  -1.0097e+00   3.2878e-05   3.6943e-02   5.0892e-07   9.1092e-02   2.2388e-02   1.8752e-01   9.2072e-02   9.3534e-01   8.2276e-02   9.8472e-01   8.2615e-02
-#> 401:    1.0186e+02  -4.1205e+00  -2.3501e+00  -4.0630e+00  -1.0097e+00   2.6776e-04   3.6950e-02   5.0860e-07   9.1038e-02   2.2395e-02   1.8740e-01   9.1911e-02   9.3515e-01   8.2331e-02   9.8459e-01   8.2615e-02
-#> 402:    1.0186e+02  -4.1205e+00  -2.3502e+00  -4.0632e+00  -1.0097e+00   3.9988e-04   3.6912e-02   5.0849e-07   9.0944e-02   2.2401e-02   1.8737e-01   9.1701e-02   9.3494e-01   8.2353e-02   9.8479e-01   8.2609e-02
-#> 403:    1.0186e+02  -4.1205e+00  -2.3503e+00  -4.0633e+00  -1.0098e+00   4.9714e-04   3.6935e-02   5.0805e-07   9.0895e-02   2.2404e-02   1.8741e-01   9.1609e-02   9.3444e-01   8.2372e-02   9.8505e-01   8.2638e-02
-#> 404:    1.0186e+02  -4.1205e+00  -2.3504e+00  -4.0633e+00  -1.0100e+00   5.8465e-04   3.6978e-02   5.0889e-07   9.0862e-02   2.2453e-02   1.8746e-01   9.1650e-02   9.3491e-01   8.2364e-02   9.8484e-01   8.2653e-02
-#> 405:    1.0186e+02  -4.1205e+00  -2.3505e+00  -4.0634e+00  -1.0099e+00   5.5970e-04   3.6999e-02   5.0964e-07   9.0930e-02   2.2480e-02   1.8742e-01   9.1823e-02   9.3458e-01   8.2371e-02   9.8465e-01   8.2670e-02
-#> 406:    1.0186e+02  -4.1205e+00  -2.3507e+00  -4.0634e+00  -1.0098e+00   5.4464e-04   3.7123e-02   5.1046e-07   9.1008e-02   2.2478e-02   1.8749e-01   9.1930e-02   9.3449e-01   8.2361e-02   9.8440e-01   8.2666e-02
-#> 407:    1.0186e+02  -4.1205e+00  -2.3506e+00  -4.0634e+00  -1.0097e+00   3.5564e-04   3.7226e-02   5.0978e-07   9.0891e-02   2.2469e-02   1.8751e-01   9.2130e-02   9.3444e-01   8.2380e-02   9.8462e-01   8.2660e-02
-#> 408:    1.0186e+02  -4.1205e+00  -2.3506e+00  -4.0635e+00  -1.0097e+00   3.8362e-04   3.7354e-02   5.0967e-07   9.0892e-02   2.2461e-02   1.8747e-01   9.2230e-02   9.3453e-01   8.2363e-02   9.8466e-01   8.2661e-02
-#> 409:    1.0186e+02  -4.1205e+00  -2.3505e+00  -4.0635e+00  -1.0097e+00   2.6671e-04   3.7473e-02   5.0928e-07   9.0894e-02   2.2519e-02   1.8751e-01   9.2243e-02   9.3447e-01   8.2347e-02   9.8449e-01   8.2667e-02
-#> 410:    1.0186e+02  -4.1205e+00  -2.3506e+00  -4.0634e+00  -1.0098e+00   1.7963e-04   3.7438e-02   5.0981e-07   9.0898e-02   2.2600e-02   1.8764e-01   9.2237e-02   9.3502e-01   8.2320e-02   9.8430e-01   8.2663e-02
-#> 411:    1.0186e+02  -4.1205e+00  -2.3506e+00  -4.0634e+00  -1.0098e+00   1.0085e-04   3.7381e-02   5.0970e-07   9.0820e-02   2.2618e-02   1.8767e-01   9.2103e-02   9.3480e-01   8.2324e-02   9.8427e-01   8.2652e-02
-#> 412:    1.0186e+02  -4.1205e+00  -2.3508e+00  -4.0633e+00  -1.0097e+00   1.9452e-04   3.7315e-02   5.0984e-07   9.0784e-02   2.2605e-02   1.8772e-01   9.2118e-02   9.3504e-01   8.2314e-02   9.8431e-01   8.2636e-02
-#> 413:    1.0186e+02  -4.1205e+00  -2.3508e+00  -4.0632e+00  -1.0097e+00   1.8432e-04   3.7243e-02   5.0946e-07   9.0798e-02   2.2604e-02   1.8765e-01   9.2206e-02   9.3499e-01   8.2299e-02   9.8426e-01   8.2636e-02
-#> 414:    1.0186e+02  -4.1205e+00  -2.3505e+00  -4.0632e+00  -1.0097e+00   2.1744e-04   3.7203e-02   5.0880e-07   9.0769e-02   2.2604e-02   1.8757e-01   9.2403e-02   9.3516e-01   8.2279e-02   9.8414e-01   8.2659e-02
-#> 415:    1.0186e+02  -4.1205e+00  -2.3505e+00  -4.0633e+00  -1.0097e+00   1.9330e-04   3.7197e-02   5.0896e-07   9.0657e-02   2.2618e-02   1.8764e-01   9.2565e-02   9.3514e-01   8.2264e-02   9.8435e-01   8.2655e-02
-#> 416:    1.0186e+02  -4.1205e+00  -2.3501e+00  -4.0634e+00  -1.0097e+00   2.1450e-04   3.7144e-02   5.0882e-07   9.0762e-02   2.2645e-02   1.8761e-01   9.2614e-02   9.3511e-01   8.2277e-02   9.8415e-01   8.2669e-02
-#> 417:    1.0186e+02  -4.1205e+00  -2.3498e+00  -4.0634e+00  -1.0099e+00   1.0737e-04   3.7092e-02   5.0932e-07   9.0804e-02   2.2631e-02   1.8754e-01   9.2581e-02   9.3509e-01   8.2284e-02   9.8430e-01   8.2667e-02
-#> 418:    1.0186e+02  -4.1205e+00  -2.3495e+00  -4.0633e+00  -1.0099e+00   2.4734e-05   3.7061e-02   5.0972e-07   9.0913e-02   2.2624e-02   1.8736e-01   9.2572e-02   9.3482e-01   8.2275e-02   9.8413e-01   8.2682e-02
-#> 419:    1.0186e+02  -4.1205e+00  -2.3495e+00  -4.0633e+00  -1.0099e+00  -3.9197e-05   3.7070e-02   5.1000e-07   9.1084e-02   2.2644e-02   1.8727e-01   9.2636e-02   9.3494e-01   8.2259e-02   9.8382e-01   8.2673e-02
-#> 420:    1.0186e+02  -4.1205e+00  -2.3494e+00  -4.0633e+00  -1.0098e+00  -1.2434e-04   3.7103e-02   5.1037e-07   9.1152e-02   2.2631e-02   1.8733e-01   9.2862e-02   9.3515e-01   8.2244e-02   9.8388e-01   8.2656e-02
-#> 421:    1.0186e+02  -4.1205e+00  -2.3494e+00  -4.0632e+00  -1.0097e+00  -1.5440e-04   3.7123e-02   5.1205e-07   9.1233e-02   2.2626e-02   1.8744e-01   9.2935e-02   9.3523e-01   8.2241e-02   9.8360e-01   8.2652e-02
-#> 422:    1.0186e+02  -4.1205e+00  -2.3495e+00  -4.0633e+00  -1.0095e+00  -8.9184e-05   3.7182e-02   5.1296e-07   9.1123e-02   2.2617e-02   1.8749e-01   9.2915e-02   9.3509e-01   8.2276e-02   9.8367e-01   8.2637e-02
-#> 423:    1.0186e+02  -4.1205e+00  -2.3497e+00  -4.0634e+00  -1.0095e+00   6.7469e-05   3.7194e-02   5.1323e-07   9.1083e-02   2.2642e-02   1.8739e-01   9.3097e-02   9.3529e-01   8.2270e-02   9.8367e-01   8.2642e-02
-#> 424:    1.0186e+02  -4.1205e+00  -2.3498e+00  -4.0635e+00  -1.0094e+00   1.5970e-04   3.7258e-02   5.1292e-07   9.0998e-02   2.2667e-02   1.8730e-01   9.3311e-02   9.3525e-01   8.2262e-02   9.8362e-01   8.2648e-02
-#> 425:    1.0186e+02  -4.1205e+00  -2.3499e+00  -4.0636e+00  -1.0095e+00   2.7004e-04   3.7298e-02   5.1307e-07   9.0839e-02   2.2665e-02   1.8744e-01   9.3429e-02   9.3497e-01   8.2282e-02   9.8395e-01   8.2657e-02
-#> 426:    1.0186e+02  -4.1205e+00  -2.3499e+00  -4.0636e+00  -1.0094e+00   3.9201e-04   3.7303e-02   5.1305e-07   9.0647e-02   2.2675e-02   1.8743e-01   9.3523e-02   9.3477e-01   8.2314e-02   9.8371e-01   8.2655e-02
-#> 427:    1.0186e+02  -4.1205e+00  -2.3496e+00  -4.0636e+00  -1.0093e+00   2.9359e-04   3.7366e-02   5.1245e-07   9.0630e-02   2.2673e-02   1.8738e-01   9.3813e-02   9.3495e-01   8.2291e-02   9.8368e-01   8.2653e-02
-#> 428:    1.0186e+02  -4.1204e+00  -2.3496e+00  -4.0635e+00  -1.0094e+00   2.5099e-04   3.7411e-02   5.1144e-07   9.0647e-02   2.2674e-02   1.8732e-01   9.3993e-02   9.3493e-01   8.2273e-02   9.8373e-01   8.2652e-02
-#> 429:    1.0186e+02  -4.1204e+00  -2.3495e+00  -4.0635e+00  -1.0095e+00   2.4723e-04   3.7543e-02   5.1084e-07   9.0600e-02   2.2677e-02   1.8723e-01   9.4269e-02   9.3518e-01   8.2286e-02   9.8396e-01   8.2659e-02
-#> 430:    1.0186e+02  -4.1204e+00  -2.3494e+00  -4.0635e+00  -1.0096e+00   2.7711e-04   3.7579e-02   5.1022e-07   9.0496e-02   2.2679e-02   1.8708e-01   9.4484e-02   9.3525e-01   8.2309e-02   9.8433e-01   8.2672e-02
-#> 431:    1.0186e+02  -4.1204e+00  -2.3494e+00  -4.0634e+00  -1.0095e+00   1.3934e-05   3.7631e-02   5.0908e-07   9.0378e-02   2.2671e-02   1.8708e-01   9.4770e-02   9.3528e-01   8.2302e-02   9.8470e-01   8.2682e-02
-#> 432:    1.0186e+02  -4.1204e+00  -2.3495e+00  -4.0633e+00  -1.0096e+00  -8.9401e-05   3.7677e-02   5.0861e-07   9.0278e-02   2.2654e-02   1.8702e-01   9.4882e-02   9.3518e-01   8.2318e-02   9.8488e-01   8.2667e-02
-#> 433:    1.0186e+02  -4.1205e+00  -2.3495e+00  -4.0633e+00  -1.0096e+00  -3.6841e-04   3.7706e-02   5.0854e-07   9.0108e-02   2.2652e-02   1.8703e-01   9.5039e-02   9.3487e-01   8.2331e-02   9.8494e-01   8.2669e-02
-#> 434:    1.0186e+02  -4.1205e+00  -2.3493e+00  -4.0632e+00  -1.0096e+00  -4.3399e-04   3.7671e-02   5.0796e-07   9.0036e-02   2.2661e-02   1.8701e-01   9.5122e-02   9.3474e-01   8.2331e-02   9.8474e-01   8.2675e-02
-#> 435:    1.0186e+02  -4.1205e+00  -2.3491e+00  -4.0632e+00  -1.0096e+00  -6.1398e-04   3.7654e-02   5.0727e-07   8.9940e-02   2.2664e-02   1.8691e-01   9.5242e-02   9.3451e-01   8.2346e-02   9.8466e-01   8.2677e-02
-#> 436:    1.0186e+02  -4.1205e+00  -2.3487e+00  -4.0632e+00  -1.0094e+00  -7.2148e-04   3.7647e-02   5.0649e-07   8.9838e-02   2.2661e-02   1.8694e-01   9.5465e-02   9.3429e-01   8.2365e-02   9.8475e-01   8.2683e-02
-#> 437:    1.0186e+02  -4.1205e+00  -2.3484e+00  -4.0632e+00  -1.0093e+00  -1.1480e-03   3.7613e-02   5.0662e-07   8.9719e-02   2.2674e-02   1.8698e-01   9.5631e-02   9.3419e-01   8.2380e-02   9.8490e-01   8.2684e-02
-#> 438:    1.0186e+02  -4.1204e+00  -2.3482e+00  -4.0631e+00  -1.0092e+00  -1.5547e-03   3.7583e-02   5.0753e-07   8.9612e-02   2.2680e-02   1.8703e-01   9.5913e-02   9.3413e-01   8.2394e-02   9.8523e-01   8.2678e-02
-#> 439:    1.0186e+02  -4.1205e+00  -2.3483e+00  -4.0630e+00  -1.0093e+00  -1.9392e-03   3.7463e-02   5.0769e-07   8.9410e-02   2.2706e-02   1.8706e-01   9.6149e-02   9.3392e-01   8.2425e-02   9.8512e-01   8.2670e-02
-#> 440:    1.0186e+02  -4.1205e+00  -2.3482e+00  -4.0629e+00  -1.0094e+00  -2.1940e-03   3.7360e-02   5.0743e-07   8.9245e-02   2.2742e-02   1.8710e-01   9.6213e-02   9.3400e-01   8.2445e-02   9.8490e-01   8.2676e-02
-#> 441:    1.0186e+02  -4.1205e+00  -2.3481e+00  -4.0629e+00  -1.0095e+00  -2.3414e-03   3.7297e-02   5.0838e-07   8.9137e-02   2.2806e-02   1.8721e-01   9.6155e-02   9.3405e-01   8.2450e-02   9.8470e-01   8.2684e-02
-#> 442:    1.0186e+02  -4.1205e+00  -2.3481e+00  -4.0628e+00  -1.0095e+00  -2.6378e-03   3.7241e-02   5.0923e-07   8.9066e-02   2.2846e-02   1.8727e-01   9.6135e-02   9.3389e-01   8.2465e-02   9.8454e-01   8.2686e-02
-#> 443:    1.0186e+02  -4.1205e+00  -2.3482e+00  -4.0627e+00  -1.0094e+00  -2.8716e-03   3.7214e-02   5.1026e-07   8.9128e-02   2.2901e-02   1.8740e-01   9.6077e-02   9.3386e-01   8.2444e-02   9.8421e-01   8.2692e-02
-#> 444:    1.0186e+02  -4.1205e+00  -2.3483e+00  -4.0627e+00  -1.0092e+00  -2.9147e-03   3.7196e-02   5.1104e-07   8.9190e-02   2.2985e-02   1.8744e-01   9.5999e-02   9.3390e-01   8.2424e-02   9.8381e-01   8.2696e-02
-#> 445:    1.0186e+02  -4.1205e+00  -2.3484e+00  -4.0626e+00  -1.0090e+00  -2.9638e-03   3.7251e-02   5.1283e-07   8.9335e-02   2.3004e-02   1.8756e-01   9.5788e-02   9.3382e-01   8.2416e-02   9.8347e-01   8.2683e-02
-#> 446:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0627e+00  -1.0090e+00  -2.8796e-03   3.7331e-02   5.1479e-07   8.9470e-02   2.3017e-02   1.8762e-01   9.5656e-02   9.3368e-01   8.2405e-02   9.8325e-01   8.2680e-02
-#> 447:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0628e+00  -1.0091e+00  -2.7695e-03   3.7473e-02   5.1656e-07   8.9568e-02   2.3030e-02   1.8757e-01   9.5575e-02   9.3379e-01   8.2386e-02   9.8306e-01   8.2690e-02
-#> 448:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0629e+00  -1.0091e+00  -2.6293e-03   3.7498e-02   5.1814e-07   8.9776e-02   2.3052e-02   1.8762e-01   9.5422e-02   9.3373e-01   8.2372e-02   9.8274e-01   8.2685e-02
-#> 449:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0628e+00  -1.0092e+00  -2.5640e-03   3.7542e-02   5.1867e-07   8.9888e-02   2.3056e-02   1.8763e-01   9.5364e-02   9.3400e-01   8.2365e-02   9.8239e-01   8.2691e-02
-#> 450:    1.0186e+02  -4.1205e+00  -2.3483e+00  -4.0628e+00  -1.0093e+00  -2.5816e-03   3.7622e-02   5.1849e-07   9.0050e-02   2.3061e-02   1.8765e-01   9.5274e-02   9.3435e-01   8.2341e-02   9.8235e-01   8.2699e-02
-#> 451:    1.0186e+02  -4.1205e+00  -2.3483e+00  -4.0627e+00  -1.0094e+00  -2.4837e-03   3.7631e-02   5.1931e-07   9.0177e-02   2.3053e-02   1.8766e-01   9.5103e-02   9.3459e-01   8.2322e-02   9.8226e-01   8.2715e-02
-#> 452:    1.0186e+02  -4.1205e+00  -2.3482e+00  -4.0627e+00  -1.0094e+00  -2.4156e-03   3.7606e-02   5.1901e-07   9.0333e-02   2.3047e-02   1.8763e-01   9.4959e-02   9.3485e-01   8.2289e-02   9.8210e-01   8.2713e-02
-#> 453:    1.0186e+02  -4.1205e+00  -2.3484e+00  -4.0627e+00  -1.0093e+00  -2.4619e-03   3.7552e-02   5.1874e-07   9.0495e-02   2.3066e-02   1.8761e-01   9.4960e-02   9.3485e-01   8.2293e-02   9.8178e-01   8.2703e-02
-#> 454:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0627e+00  -1.0092e+00  -2.4816e-03   3.7514e-02   5.1835e-07   9.0606e-02   2.3073e-02   1.8754e-01   9.4896e-02   9.3491e-01   8.2277e-02   9.8154e-01   8.2696e-02
-#> 455:    1.0186e+02  -4.1205e+00  -2.3484e+00  -4.0627e+00  -1.0092e+00  -2.3708e-03   3.7457e-02   5.1742e-07   9.0715e-02   2.3099e-02   1.8756e-01   9.4804e-02   9.3481e-01   8.2272e-02   9.8122e-01   8.2688e-02
-#> 456:    1.0186e+02  -4.1205e+00  -2.3484e+00  -4.0627e+00  -1.0093e+00  -2.2313e-03   3.7409e-02   5.1680e-07   9.0906e-02   2.3131e-02   1.8743e-01   9.4814e-02   9.3476e-01   8.2261e-02   9.8108e-01   8.2694e-02
-#> 457:    1.0186e+02  -4.1205e+00  -2.3483e+00  -4.0628e+00  -1.0095e+00  -2.1182e-03   3.7342e-02   5.1630e-07   9.0986e-02   2.3158e-02   1.8733e-01   9.4843e-02   9.3488e-01   8.2244e-02   9.8094e-01   8.2700e-02
-#> 458:    1.0186e+02  -4.1205e+00  -2.3483e+00  -4.0628e+00  -1.0095e+00  -1.9242e-03   3.7244e-02   5.1605e-07   9.1085e-02   2.3168e-02   1.8720e-01   9.4820e-02   9.3509e-01   8.2228e-02   9.8093e-01   8.2703e-02
-#> 459:    1.0186e+02  -4.1205e+00  -2.3484e+00  -4.0629e+00  -1.0095e+00  -1.7643e-03   3.7203e-02   5.1566e-07   9.1179e-02   2.3175e-02   1.8715e-01   9.4809e-02   9.3516e-01   8.2216e-02   9.8087e-01   8.2690e-02
-#> 460:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0629e+00  -1.0094e+00  -1.5479e-03   3.7151e-02   5.1547e-07   9.1211e-02   2.3155e-02   1.8712e-01   9.4703e-02   9.3539e-01   8.2201e-02   9.8100e-01   8.2683e-02
-#> 461:    1.0186e+02  -4.1205e+00  -2.3484e+00  -4.0630e+00  -1.0095e+00  -1.4993e-03   3.7111e-02   5.1446e-07   9.1225e-02   2.3159e-02   1.8705e-01   9.4569e-02   9.3555e-01   8.2183e-02   9.8078e-01   8.2680e-02
-#> 462:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0629e+00  -1.0094e+00  -1.4890e-03   3.7056e-02   5.1361e-07   9.1446e-02   2.3158e-02   1.8694e-01   9.4494e-02   9.3557e-01   8.2171e-02   9.8058e-01   8.2688e-02
-#> 463:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0629e+00  -1.0094e+00  -1.3999e-03   3.6996e-02   5.1319e-07   9.1659e-02   2.3176e-02   1.8695e-01   9.4436e-02   9.3570e-01   8.2153e-02   9.8053e-01   8.2686e-02
-#> 464:    1.0186e+02  -4.1205e+00  -2.3484e+00  -4.0630e+00  -1.0095e+00  -1.1544e-03   3.6949e-02   5.1300e-07   9.1885e-02   2.3162e-02   1.8688e-01   9.4378e-02   9.3599e-01   8.2134e-02   9.8051e-01   8.2694e-02
-#> 465:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0630e+00  -1.0097e+00  -9.7372e-04   3.6943e-02   5.1235e-07   9.2014e-02   2.3136e-02   1.8692e-01   9.4288e-02   9.3605e-01   8.2141e-02   9.8053e-01   8.2693e-02
-#> 466:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0630e+00  -1.0097e+00  -9.2442e-04   3.6916e-02   5.1246e-07   9.2074e-02   2.3132e-02   1.8688e-01   9.4254e-02   9.3590e-01   8.2131e-02   9.8016e-01   8.2691e-02
-#> 467:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0631e+00  -1.0098e+00  -8.2540e-04   3.6928e-02   5.1340e-07   9.2164e-02   2.3141e-02   1.8690e-01   9.4382e-02   9.3620e-01   8.2106e-02   9.7996e-01   8.2705e-02
-#> 468:    1.0186e+02  -4.1205e+00  -2.3484e+00  -4.0631e+00  -1.0097e+00  -7.3368e-04   3.6925e-02   5.1395e-07   9.2218e-02   2.3136e-02   1.8695e-01   9.4504e-02   9.3629e-01   8.2094e-02   9.7985e-01   8.2716e-02
-#> 469:    1.0186e+02  -4.1205e+00  -2.3483e+00  -4.0630e+00  -1.0096e+00  -7.4343e-04   3.6891e-02   5.1401e-07   9.2204e-02   2.3114e-02   1.8700e-01   9.4639e-02   9.3643e-01   8.2078e-02   9.7996e-01   8.2709e-02
-#> 470:    1.0186e+02  -4.1205e+00  -2.3482e+00  -4.0630e+00  -1.0096e+00  -7.8250e-04   3.6874e-02   5.1370e-07   9.2209e-02   2.3083e-02   1.8702e-01   9.4728e-02   9.3646e-01   8.2073e-02   9.7989e-01   8.2703e-02
-#> 471:    1.0186e+02  -4.1205e+00  -2.3480e+00  -4.0629e+00  -1.0095e+00  -1.0440e-03   3.6843e-02   5.1358e-07   9.2194e-02   2.3062e-02   1.8710e-01   9.4741e-02   9.3649e-01   8.2082e-02   9.8003e-01   8.2701e-02
-#> 472:    1.0186e+02  -4.1205e+00  -2.3481e+00  -4.0629e+00  -1.0096e+00  -9.5438e-04   3.6869e-02   5.1330e-07   9.2176e-02   2.3050e-02   1.8712e-01   9.4766e-02   9.3666e-01   8.2080e-02   9.7996e-01   8.2691e-02
-#> 473:    1.0186e+02  -4.1205e+00  -2.3481e+00  -4.0630e+00  -1.0096e+00  -8.2178e-04   3.6877e-02   5.1283e-07   9.2191e-02   2.3021e-02   1.8703e-01   9.4747e-02   9.3670e-01   8.2072e-02   9.8007e-01   8.2693e-02
-#> 474:    1.0186e+02  -4.1205e+00  -2.3481e+00  -4.0630e+00  -1.0096e+00  -7.0189e-04   3.6927e-02   5.1196e-07   9.2195e-02   2.2989e-02   1.8702e-01   9.4746e-02   9.3669e-01   8.2054e-02   9.8029e-01   8.2689e-02
-#> 475:    1.0186e+02  -4.1205e+00  -2.3481e+00  -4.0630e+00  -1.0095e+00  -7.1989e-04   3.6993e-02   5.1125e-07   9.2159e-02   2.2963e-02   1.8700e-01   9.4813e-02   9.3681e-01   8.2051e-02   9.8027e-01   8.2680e-02
-#> 476:    1.0186e+02  -4.1205e+00  -2.3481e+00  -4.0629e+00  -1.0096e+00  -7.1806e-04   3.7018e-02   5.1105e-07   9.2091e-02   2.2933e-02   1.8696e-01   9.4837e-02   9.3713e-01   8.2067e-02   9.8033e-01   8.2674e-02
-#> 477:    1.0186e+02  -4.1205e+00  -2.3479e+00  -4.0630e+00  -1.0097e+00  -7.3438e-04   3.6986e-02   5.1121e-07   9.2046e-02   2.2909e-02   1.8698e-01   9.4809e-02   9.3743e-01   8.2059e-02   9.8045e-01   8.2693e-02
-#> 478:    1.0186e+02  -4.1205e+00  -2.3478e+00  -4.0630e+00  -1.0096e+00  -7.9338e-04   3.6912e-02   5.1224e-07   9.2056e-02   2.2881e-02   1.8698e-01   9.4791e-02   9.3757e-01   8.2042e-02   9.8072e-01   8.2682e-02
-#> 479:    1.0186e+02  -4.1205e+00  -2.3476e+00  -4.0629e+00  -1.0096e+00  -8.6158e-04   3.6882e-02   5.1284e-07   9.2159e-02   2.2867e-02   1.8694e-01   9.4774e-02   9.3749e-01   8.2051e-02   9.8088e-01   8.2679e-02
-#> 480:    1.0186e+02  -4.1205e+00  -2.3474e+00  -4.0629e+00  -1.0096e+00  -1.1334e-03   3.6851e-02   5.1423e-07   9.2253e-02   2.2869e-02   1.8696e-01   9.4820e-02   9.3751e-01   8.2063e-02   9.8097e-01   8.2693e-02
-#> 481:    1.0186e+02  -4.1205e+00  -2.3470e+00  -4.0629e+00  -1.0096e+00  -1.2444e-03   3.6785e-02   5.1490e-07   9.2397e-02   2.2853e-02   1.8694e-01   9.4838e-02   9.3770e-01   8.2031e-02   9.8124e-01   8.2707e-02
-#> 482:    1.0186e+02  -4.1205e+00  -2.3467e+00  -4.0629e+00  -1.0095e+00  -1.3612e-03   3.6750e-02   5.1658e-07   9.2440e-02   2.2842e-02   1.8683e-01   9.4800e-02   9.3786e-01   8.2041e-02   9.8107e-01   8.2719e-02
-#> 483:    1.0186e+02  -4.1205e+00  -2.3467e+00  -4.0628e+00  -1.0096e+00  -1.5168e-03   3.6783e-02   5.1708e-07   9.2590e-02   2.2804e-02   1.8674e-01   9.4804e-02   9.3790e-01   8.2042e-02   9.8116e-01   8.2719e-02
-#> 484:    1.0186e+02  -4.1205e+00  -2.3466e+00  -4.0628e+00  -1.0097e+00  -1.5218e-03   3.6848e-02   5.1669e-07   9.2717e-02   2.2775e-02   1.8670e-01   9.4940e-02   9.3798e-01   8.2028e-02   9.8106e-01   8.2719e-02
-#> 485:    1.0186e+02  -4.1205e+00  -2.3464e+00  -4.0628e+00  -1.0097e+00  -1.4177e-03   3.6867e-02   5.1615e-07   9.2806e-02   2.2765e-02   1.8669e-01   9.5018e-02   9.3816e-01   8.2020e-02   9.8090e-01   8.2721e-02
-#> 486:    1.0186e+02  -4.1205e+00  -2.3462e+00  -4.0628e+00  -1.0098e+00  -1.5257e-03   3.6968e-02   5.1513e-07   9.3019e-02   2.2762e-02   1.8663e-01   9.5111e-02   9.3816e-01   8.2013e-02   9.8071e-01   8.2732e-02
-#> 487:    1.0186e+02  -4.1205e+00  -2.3460e+00  -4.0628e+00  -1.0097e+00  -1.7055e-03   3.7021e-02   5.1446e-07   9.3161e-02   2.2732e-02   1.8652e-01   9.5373e-02   9.3832e-01   8.1997e-02   9.8078e-01   8.2737e-02
-#> 488:    1.0186e+02  -4.1205e+00  -2.3459e+00  -4.0628e+00  -1.0097e+00  -1.8502e-03   3.7069e-02   5.1391e-07   9.3282e-02   2.2741e-02   1.8641e-01   9.5414e-02   9.3818e-01   8.2001e-02   9.8064e-01   8.2738e-02
-#> 489:    1.0186e+02  -4.1205e+00  -2.3458e+00  -4.0628e+00  -1.0097e+00  -1.9091e-03   3.7017e-02   5.1291e-07   9.3286e-02   2.2738e-02   1.8639e-01   9.5453e-02   9.3808e-01   8.1991e-02   9.8047e-01   8.2728e-02
-#> 490:    1.0186e+02  -4.1205e+00  -2.3456e+00  -4.0628e+00  -1.0097e+00  -1.8766e-03   3.6969e-02   5.1220e-07   9.3297e-02   2.2728e-02   1.8635e-01   9.5468e-02   9.3793e-01   8.1988e-02   9.8034e-01   8.2726e-02
-#> 491:    1.0186e+02  -4.1205e+00  -2.3457e+00  -4.0627e+00  -1.0097e+00  -1.7736e-03   3.6915e-02   5.1153e-07   9.3298e-02   2.2716e-02   1.8634e-01   9.5548e-02   9.3772e-01   8.2005e-02   9.8025e-01   8.2722e-02
-#> 492:    1.0186e+02  -4.1205e+00  -2.3457e+00  -4.0627e+00  -1.0097e+00  -1.7747e-03   3.6877e-02   5.1077e-07   9.3336e-02   2.2697e-02   1.8635e-01   9.5593e-02   9.3778e-01   8.2001e-02   9.8013e-01   8.2725e-02
-#> 493:    1.0186e+02  -4.1205e+00  -2.3456e+00  -4.0628e+00  -1.0094e+00  -1.6324e-03   3.6857e-02   5.1020e-07   9.3348e-02   2.2668e-02   1.8636e-01   9.5735e-02   9.3764e-01   8.1984e-02   9.8019e-01   8.2723e-02
-#> 494:    1.0186e+02  -4.1205e+00  -2.3456e+00  -4.0629e+00  -1.0094e+00  -1.5393e-03   3.6842e-02   5.1022e-07   9.3359e-02   2.2649e-02   1.8637e-01   9.5812e-02   9.3739e-01   8.1982e-02   9.8033e-01   8.2708e-02
-#> 495:    1.0186e+02  -4.1205e+00  -2.3456e+00  -4.0629e+00  -1.0094e+00  -1.5166e-03   3.6841e-02   5.1004e-07   9.3321e-02   2.2642e-02   1.8640e-01   9.5849e-02   9.3716e-01   8.1979e-02   9.8016e-01   8.2700e-02
-#> 496:    1.0186e+02  -4.1205e+00  -2.3456e+00  -4.0630e+00  -1.0095e+00  -1.4947e-03   3.6841e-02   5.0969e-07   9.3236e-02   2.2646e-02   1.8640e-01   9.5916e-02   9.3719e-01   8.1963e-02   9.8028e-01   8.2702e-02
-#> 497:    1.0186e+02  -4.1205e+00  -2.3457e+00  -4.0629e+00  -1.0094e+00  -1.4507e-03   3.6827e-02   5.0937e-07   9.3185e-02   2.2663e-02   1.8638e-01   9.5991e-02   9.3707e-01   8.1954e-02   9.8047e-01   8.2718e-02
-#> 498:    1.0186e+02  -4.1205e+00  -2.3459e+00  -4.0630e+00  -1.0094e+00  -1.2569e-03   3.6805e-02   5.0854e-07   9.3089e-02   2.2677e-02   1.8634e-01   9.5931e-02   9.3719e-01   8.1952e-02   9.8051e-01   8.2718e-02
-#> 499:    1.0186e+02  -4.1205e+00  -2.3460e+00  -4.0630e+00  -1.0093e+00  -1.0466e-03   3.6769e-02   5.0789e-07   9.3029e-02   2.2690e-02   1.8631e-01   9.5862e-02   9.3729e-01   8.1956e-02   9.8046e-01   8.2731e-02
-#> 500:    1.0186e+02  -4.1205e+00  -2.3464e+00  -4.0630e+00  -1.0093e+00  -7.3346e-04   3.6766e-02   5.0769e-07   9.3093e-02   2.2701e-02   1.8633e-01   9.5687e-02   9.3739e-01   8.1977e-02   9.8039e-01   8.2728e-02
#> Calculating covariance matrix
#> 
#> → creating full model...
#> → pruning branches (`if`/`else`)...
#> ✔ done
#> → loading into symengine environment...
#> ✔ done
#> → compiling EBE model...
#>  
#> ✔ done
#> Needed Covariates:
#> [1] "CMT"
#> Calculating residuals/tables
#> done
# The following takes a very long time but gives
+
#> With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#>  
#> → generate SAEM model
#> ✔ done
#> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc,     ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG,     addProp = .addProp, tol = .tol, itmax = .itmax, type = .type,     powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_m1
#> Timing stopped at: 1.281 0.142 1.422
# The following takes a very long time but gives
 f_nlmixr_dfop_sfo_focei <- nlmixr(f_mmkin_dfop_sfo, est = "focei")
-
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#> → creating full model...
#> → pruning branches (`if`/`else`)...
#> ✔ done
#> → loading into symengine environment...
#> ✔ done
#> → creating full model...
#> → pruning branches (`if`/`else`)...
#> ✔ done
#> → loading into symengine environment...
#> ✔ done
#> → calculate jacobian
#> 
#> → calculate sensitivities
#> 
#> → calculate ∂(f)/∂(η)
#> 
#> → calculate ∂(R²)/∂(η)
#> 
#> → finding duplicate expressions in inner model...
#> 
#> → optimizing duplicate expressions in inner model...
#> 
#> → finding duplicate expressions in EBE model...
#> 
#> → optimizing duplicate expressions in EBE model...
#> 
#> → compiling inner model...
#>  
#> ✔ done
#> → finding duplicate expressions in FD model...
#> 
#> → optimizing duplicate expressions in FD model...
#> 
#> → compiling EBE model...
#>  
#> ✔ done
#> → compiling events FD model...
#>  
#> ✔ done
#> Needed Covariates:
#> [1] "CMT"
-#> Key: U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation
-#> F: Forward difference gradient approximation
-#> C: Central difference gradient approximation
-#> M: Mixed forward and central difference gradient approximation
-#> Unscaled parameters for Omegas=chol(solve(omega));
-#> Diagonals are transformed, as specified by foceiControl(diagXform=)
-#> |-----+---------------+-----------+-----------+-----------+-----------|
-#> |    #| Objective Fun |  parent_0 |  log_k_m1 |f_parent_qlogis |    log_k1 |
-#> |.....................|    log_k2 |  g_qlogis | sigma_low |  rsd_high |
-#> |.....................|        o1 |        o2 |        o3 |        o4 |
-#> |.....................|        o5 |        o6 |...........|...........|
-#> |    1|     496.98032 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
-#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
-#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
-#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
-#> |    U|     496.98032 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
-#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    X|     496.98032 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
-#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    G|    Gill Diff. |     57.10 |   -0.1453 |   -0.1275 |    0.2854 |
-#> |.....................|   -0.6156 |  0.007043 |    -23.49 |    -32.87 |
-#> |.....................|     3.669 |    -17.46 |    -13.05 |    -13.08 |
-#> |.....................|    -16.16 |    -9.766 |...........|...........|
-#> |    2|     3094.8373 |    0.2572 |   -0.9978 |   -0.9392 |   -0.9714 |
-#> |.....................|   -0.9920 |   -0.9233 |   -0.6037 |   -0.4942 |
-#> |.....................|   -0.9579 |   -0.6658 |   -0.7293 |   -0.7310 |
-#> |.....................|   -0.6848 |   -0.7742 |...........|...........|
-#> |    U|     3094.8373 |     26.15 |    -4.052 |   -0.9415 |    -2.363 |
-#> |.....................|    -4.062 |  -0.01133 |    0.8386 |   0.08074 |
-#> |.....................|    0.6445 |     1.946 |     1.477 |     1.348 |
-#> |.....................|     1.794 |     1.297 |...........|...........|
-#> |    X|     3094.8373 |     26.15 |   0.01739 |    0.2806 |   0.09412 |
-#> |.....................|   0.01721 |    0.4972 |    0.8386 |   0.08074 |
-#> |.....................|    0.6445 |     1.946 |     1.477 |     1.348 |
-#> |.....................|     1.794 |     1.297 |...........|...........|
-#> |    3|     557.60681 |    0.9257 |   -0.9995 |   -0.9407 |   -0.9680 |
-#> |.....................|   -0.9992 |   -0.9232 |   -0.8787 |   -0.8790 |
-#> |.....................|   -0.9150 |   -0.8703 |   -0.8821 |   -0.8842 |
-#> |.....................|   -0.8739 |   -0.8885 |...........|...........|
-#> |    U|     557.60681 |     94.11 |    -4.053 |   -0.9430 |    -2.360 |
-#> |.....................|    -4.069 |  -0.01133 |    0.7386 |   0.06794 |
-#> |.....................|    0.6735 |     1.622 |     1.284 |     1.172 |
-#> |.....................|     1.513 |     1.165 |...........|...........|
-#> |    X|     557.60681 |     94.11 |   0.01736 |    0.2803 |   0.09444 |
-#> |.....................|   0.01709 |    0.4972 |    0.7386 |   0.06794 |
-#> |.....................|    0.6735 |     1.622 |     1.284 |     1.172 |
-#> |.....................|     1.513 |     1.165 |...........|...........|
-#> |    4|     543.47785 |    0.9926 |   -0.9997 |   -0.9408 |   -0.9677 |
-#> |.....................|   -0.9999 |   -0.9232 |   -0.9062 |   -0.9175 |
-#> |.....................|   -0.9107 |   -0.8907 |   -0.8974 |   -0.8995 |
-#> |.....................|   -0.8929 |   -0.9000 |...........|...........|
-#> |    U|     543.47785 |     100.9 |    -4.054 |   -0.9431 |    -2.359 |
-#> |.....................|    -4.070 |  -0.01132 |    0.7286 |   0.06666 |
-#> |.....................|    0.6764 |     1.589 |     1.264 |     1.154 |
-#> |.....................|     1.485 |     1.152 |...........|...........|
-#> |    X|     543.47785 |     100.9 |   0.01736 |    0.2803 |   0.09447 |
-#> |.....................|   0.01708 |    0.4972 |    0.7286 |   0.06666 |
-#> |.....................|    0.6764 |     1.589 |     1.264 |     1.154 |
-#> |.....................|     1.485 |     1.152 |...........|...........|
-#> |    5|     544.09017 |    0.9993 |   -0.9997 |   -0.9408 |   -0.9676 |
-#> |.....................|    -1.000 |   -0.9232 |   -0.9089 |   -0.9213 |
-#> |.....................|   -0.9103 |   -0.8928 |   -0.8990 |   -0.9010 |
-#> |.....................|   -0.8948 |   -0.9011 |...........|...........|
-#> |    U|     544.09017 |     101.6 |    -4.054 |   -0.9431 |    -2.359 |
-#> |.....................|    -4.070 |  -0.01132 |    0.7276 |   0.06654 |
-#> |.....................|    0.6767 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    X|     544.09017 |     101.6 |   0.01736 |    0.2803 |   0.09447 |
-#> |.....................|   0.01708 |    0.4972 |    0.7276 |   0.06654 |
-#> |.....................|    0.6767 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    6|     544.17109 |    0.9999 |   -0.9997 |   -0.9408 |   -0.9676 |
-#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
-#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
-#> |.....................|   -0.8949 |   -0.9012 |...........|...........|
-#> |    U|     544.17109 |     101.6 |    -4.054 |   -0.9431 |    -2.359 |
-#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    X|     544.17109 |     101.6 |   0.01736 |    0.2803 |   0.09447 |
-#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    7|     544.17937 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
-#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
-#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
-#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
-#> |    U|     544.17937 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
-#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    X|     544.17937 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
-#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    8|     544.18025 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
-#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
-#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
-#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
-#> |    U|     544.18025 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
-#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    X|     544.18025 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
-#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    9|     544.18033 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
-#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
-#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
-#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
-#> |    U|     544.18033 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
-#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    X|     544.18033 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
-#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |   10|     544.18034 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
-#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
-#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
-#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
-#> |    U|     544.18034 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
-#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    X|     544.18034 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
-#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |   11|     544.18036 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
-#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
-#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
-#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
-#> |    U|     544.18036 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
-#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    X|     544.18036 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
-#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |   12|     544.18036 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
-#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
-#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
-#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
-#> |    U|     544.18036 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
-#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    X|     544.18036 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
-#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |   13|     544.18036 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
-#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
-#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
-#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
-#> |    U|     544.18036 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
-#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    X|     544.18036 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
-#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |   14|     544.18036 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
-#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
-#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
-#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
-#> |    U|     544.18036 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
-#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    X|     544.18036 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
-#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |   15|     544.18036 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
-#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
-#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
-#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
-#> |    U|     544.18036 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
-#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    X|     544.18036 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
-#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |   16|     544.18036 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
-#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
-#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
-#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
-#> |    U|     544.18036 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
-#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    X|     544.18036 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
-#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |   17|     544.18036 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
-#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
-#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
-#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
-#> |    U|     544.18036 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
-#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    X|     544.18036 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
-#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> calculating covariance matrix
-#> done
#> Calculating residuals/tables
#> done
#> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
#> Warning: last objective function was not at minimum, possible problems in optimization
#> Warning: using R matrix to calculate covariance, can check sandwich or S matrix with $covRS and $covS
#> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
AIC(f_nlmixr_dfop_sfo_saem$nm, f_nlmixr_dfop_sfo_focei$nm)
-
#> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)
#> 
#>                            df      AIC
-#> f_nlmixr_dfop_sfo_saem$nm  16      Inf
-#> f_nlmixr_dfop_sfo_focei$nm 14 886.4573
summary(f_nlmixr_dfop_sfo_sfo, data = TRUE)
+
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#> → creating full model...
#> → pruning branches (`if`/`else`)...
#> ✔ done
#> → loading into symengine environment...
#> ✔ done
#> → creating full model...
#> → pruning branches (`if`/`else`)...
#> ✔ done
#> → loading into symengine environment...
#> ✔ done
#> → calculate jacobian
#> 
#> → calculate sensitivities
#> 
#> → calculate ∂(f)/∂(η)
#> 
#> → calculate ∂(R²)/∂(η)
#> 
#> → finding duplicate expressions in inner model...
#> 
#> → optimizing duplicate expressions in inner model...
#> 
#> → finding duplicate expressions in EBE model...
#> 
#> → optimizing duplicate expressions in EBE model...
#> 
#> → compiling inner model...
#>  
#> ✔ done
#> → finding duplicate expressions in FD model...
#> 
#> → optimizing duplicate expressions in FD model...
#> 
#> → compiling EBE model...
#>  
#> ✔ done
#> → compiling events FD model...
#>  
#> ✔ done
#> Model:
#> cmt(parent);
+#> cmt(m1);
+#> rx_expr_6~ETA[1]+THETA[1];
+#> parent(0)=rx_expr_6;
+#> rx_expr_7~ETA[4]+THETA[4];
+#> rx_expr_8~ETA[6]+THETA[6];
+#> rx_expr_9~ETA[5]+THETA[5];
+#> rx_expr_12~exp(rx_expr_7);
+#> rx_expr_13~exp(rx_expr_9);
+#> rx_expr_15~t*rx_expr_12;
+#> rx_expr_16~t*rx_expr_13;
+#> rx_expr_19~exp(-(rx_expr_8));
+#> rx_expr_21~1+rx_expr_19;
+#> rx_expr_26~1/(rx_expr_21);
+#> rx_expr_28~(rx_expr_26);
+#> rx_expr_29~1-rx_expr_28;
+#> d/dt(parent)=-parent*(exp(rx_expr_7-rx_expr_15)/(rx_expr_21)+exp(rx_expr_9-rx_expr_16)*(rx_expr_29))/(exp(-t*rx_expr_12)/(rx_expr_21)+exp(-t*rx_expr_13)*(rx_expr_29));
+#> rx_expr_10~ETA[2]+THETA[2];
+#> rx_expr_14~exp(rx_expr_10);
+#> d/dt(m1)=-rx_expr_14*m1+parent*f_parent_to_m1*(exp(rx_expr_7-rx_expr_15)/(rx_expr_21)+exp(rx_expr_9-rx_expr_16)*(rx_expr_29))/(exp(-t*rx_expr_12)/(rx_expr_21)+exp(-t*rx_expr_13)*(rx_expr_29));
+#> rx_expr_0~CMT==2;
+#> rx_expr_1~CMT==1;
+#> rx_expr_2~1-(rx_expr_0);
+#> rx_yj_~2*(rx_expr_2)*(rx_expr_1)+2*(rx_expr_0);
+#> rx_expr_3~(rx_expr_0);
+#> rx_expr_5~(rx_expr_2);
+#> rx_expr_20~rx_expr_5*(rx_expr_1);
+#> rx_lambda_~rx_expr_20+rx_expr_3;
+#> rx_hi_~rx_expr_20+rx_expr_3;
+#> rx_low_~0;
+#> rx_expr_4~m1*(rx_expr_0);
+#> rx_expr_11~parent*(rx_expr_2);
+#> rx_expr_24~rx_expr_11*(rx_expr_1);
+#> rx_pred_=(rx_expr_4+rx_expr_24)*(rx_expr_0)+(rx_expr_4+rx_expr_24)*(rx_expr_2)*(rx_expr_1);
+#> rx_expr_17~Rx_pow_di(THETA[8],2);
+#> rx_expr_18~Rx_pow_di(THETA[7],2);
+#> rx_r_=(Rx_pow_di(((rx_expr_4+rx_expr_24)*(rx_expr_0)+(rx_expr_4+rx_expr_24)*(rx_expr_2)*(rx_expr_1)),2)*rx_expr_17+rx_expr_18)*(rx_expr_0)+(Rx_pow_di(((rx_expr_4+rx_expr_24)*(rx_expr_1)),2)*rx_expr_17+rx_expr_18)*(rx_expr_2)*(rx_expr_1);
+#> parent_0=THETA[1];
+#> log_k_m1=THETA[2];
+#> f_parent_qlogis=THETA[3];
+#> log_k1=THETA[4];
+#> log_k2=THETA[5];
+#> g_qlogis=THETA[6];
+#> sigma_low=THETA[7];
+#> rsd_high=THETA[8];
+#> eta.parent_0=ETA[1];
+#> eta.log_k_m1=ETA[2];
+#> eta.f_parent_qlogis=ETA[3];
+#> eta.log_k1=ETA[4];
+#> eta.log_k2=ETA[5];
+#> eta.g_qlogis=ETA[6];
+#> parent_0_model=rx_expr_6;
+#> k_m1=rx_expr_14;
+#> k1=rx_expr_12;
+#> k2=rx_expr_13;
+#> f_parent=1/(1+exp(-(ETA[3]+THETA[3])));
+#> g=1/(rx_expr_21);
+#> tad=tad();
+#> dosenum=dosenum();
#> Needed Covariates:
#> [1] "f_parent_to_m1" "CMT"           
#> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
#> Timing stopped at: 19.01 0.403 19.42
AIC(f_nlmixr_dfop_sfo_saem$nm, f_nlmixr_dfop_sfo_focei$nm)
+
#> Error in AIC(f_nlmixr_dfop_sfo_saem$nm, f_nlmixr_dfop_sfo_focei$nm): object 'f_nlmixr_dfop_sfo_saem' not found
summary(f_nlmixr_dfop_sfo_sfo, data = TRUE)
 
#> Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'f_nlmixr_dfop_sfo_sfo' not found
# }
diff --git a/docs/dev/reference/tffm0.html b/docs/dev/reference/tffm0.html index d993e8ff..67f26b85 100644 --- a/docs/dev/reference/tffm0.html +++ b/docs/dev/reference/tffm0.html @@ -81,7 +81,7 @@ from RxODE." /> mkin - 1.0.5 + 1.1.0 diff --git a/docs/dev/sitemap.xml b/docs/dev/sitemap.xml index 150840e1..b5e83f34 100644 --- a/docs/dev/sitemap.xml +++ b/docs/dev/sitemap.xml @@ -246,4 +246,7 @@ https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html + + https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html + diff --git a/vignettes/web_only/dimethenamid_2018.html b/vignettes/web_only/dimethenamid_2018.html index e84a435c..df8200eb 100644 --- a/vignettes/web_only/dimethenamid_2018.html +++ b/vignettes/web_only/dimethenamid_2018.html @@ -1594,7 +1594,7 @@ div.tocify {

Example evaluations of the dimethenamid data from 2018

Johannes Ranke

-

Last change 23 June 2021, built on 25 Jun 2021

+

Last change 27 July 2021, built on 27 Jul 2021

@@ -1655,18 +1655,20 @@ f_parent_mkin_tc <- mmkin(c("SFO", "DFOP"), dmta_ds,

nlme

The nlme package was the first R extension providing facilities to fit nonlinear mixed-effects models. We use would like to do model selection from all four combinations of degradation models and error models based on the AIC. However, fitting the DFOP model with constant variance and using default control parameters results in an error, signalling that the maximum number of 50 iterations was reached, potentially indicating overparameterisation. However, the algorithm converges when the two-component error model is used in combination with the DFOP model. This can be explained by the fact that the smaller residues observed at later sampling times get more weight when using the two-component error model which will counteract the tendency of the algorithm to try parameter combinations unsuitable for fitting these data.

-
f_parent_nlme_sfo_const <- nlme(f_parent_mkin_const["SFO", ])
-#f_parent_nlme_dfop_const <- nlme(f_parent_mkin_const["DFOP", ]) # error
+
library(nlme)
+f_parent_nlme_sfo_const <- nlme(f_parent_mkin_const["SFO", ])
+#f_parent_nlme_dfop_const <- nlme(f_parent_mkin_const["DFOP", ])
+# maxIter = 50 reached
 f_parent_nlme_sfo_tc <- nlme(f_parent_mkin_tc["SFO", ])
 f_parent_nlme_dfop_tc <- nlme(f_parent_mkin_tc["DFOP", ])

 
Note that overparameterisation is also indicated by warnings obtained when fitting SFO or DFOP with the two-component error model (‘false convergence’ in the ‘LME step’ in some iterations). In addition to these fits, attempts were also made to include correlations between random effects by using the log Cholesky parameterisation of the matrix specifying them. The code used for these attempts can be made visible below.

 
f_parent_nlme_sfo_const_logchol <- nlme(f_parent_mkin_const["SFO", ],
   random = pdLogChol(list(DMTA_0 ~ 1, log_k_DMTA ~ 1)))
 anova(f_parent_nlme_sfo_const, f_parent_nlme_sfo_const_logchol) # not better
-f_parent_nlme_dfop_tc_logchol <- update(f_parent_nlme_dfop_tc,
-  random = pdLogChol(list(DMTA_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)))
+#f_parent_nlme_dfop_tc_logchol <- update(f_parent_nlme_dfop_tc,
+#  random = pdLogChol(list(DMTA_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)))
 # using log Cholesky parameterisation for random effects (nlme default) does
-# not converge and gives lots of warnings about the LME step not converging

+# not converge here and gives lots of warnings about the LME step not converging

The model comparison function of the nlme package can directly be applied to these fits showing a similar goodness-of-fit of the SFO model, but a much lower AIC for the DFOP model fitted with the two-component error model. Also, the likelihood ratio test indicates that this difference is significant. as the p-value is below 0.0001.

anova(
   f_parent_nlme_sfo_const, f_parent_nlme_sfo_tc, f_parent_nlme_dfop_tc
@@ -1685,24 +1687,24 @@ f_parent_nlme_dfop_tc       3 10 687.84 718.59 -333.92 2 vs 3 140.771  <.0001
 
The corresponding SAEM fits of the four combinations of degradation and error models are fitted below. As there is no convergence criterion implemented in the saemix package, the convergence plots need to be manually checked for every fit.

 
The convergence plot for the SFO model using constant variance is shown below.

 
library(saemix)
-f_parent_saemix_sfo_const <- saem(f_parent_mkin_const["SFO", ], quiet = TRUE,
+f_parent_saemix_sfo_const <- mkin::saem(f_parent_mkin_const["SFO", ], quiet = TRUE,
   transformations = "saemix")
 plot(f_parent_saemix_sfo_const$so, plot.type = "convergence")

 

 
Obviously the default number of iterations is sufficient to reach convergence. This can also be said for the SFO fit using the two-component error model.

-
f_parent_saemix_sfo_tc <- saem(f_parent_mkin_tc["SFO", ], quiet = TRUE,
+
f_parent_saemix_sfo_tc <- mkin::saem(f_parent_mkin_tc["SFO", ], quiet = TRUE,
   transformations = "saemix")
 plot(f_parent_saemix_sfo_tc$so, plot.type = "convergence")

 

-
When fitting the DFOP model with constant variance, parameter convergence is not as unambiguous. Therefore, the number of iterations in the first phase of the algorithm was increased, leading to visually satisfying convergence.

-
f_parent_saemix_dfop_const <- saem(f_parent_mkin_const["DFOP", ], quiet = TRUE,
+
When fitting the DFOP model with constant variance, parameter convergence is not as unambiguous (see the failure of nlme with the default number of iterations above). Therefore, the number of iterations in the first phase of the algorithm was increased, leading to visually satisfying convergence.

+
f_parent_saemix_dfop_const <- mkin::saem(f_parent_mkin_const["DFOP", ], quiet = TRUE,
   control = saemixControl(nbiter.saemix = c(800, 200), print = FALSE,
     save = FALSE, save.graphs = FALSE, displayProgress = FALSE),
   transformations = "saemix")
 plot(f_parent_saemix_dfop_const$so, plot.type = "convergence")

 

-
The same applies to the case where the DFOP model is fitted with the two-component error model.

-
f_parent_saemix_dfop_tc_moreiter <- saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE,
+
The same applies to the case where the DFOP model is fitted with the two-component error model. Convergence of the variance of k2 is enhanced by using the two-component error, it remains more or less stable already after 200 iterations of the first phase.

+
f_parent_saemix_dfop_tc_moreiter <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE,
   control = saemixControl(nbiter.saemix = c(800, 200), print = FALSE,
     save = FALSE, save.graphs = FALSE, displayProgress = FALSE),
   transformations = "saemix")
@@ -1710,20 +1712,31 @@ plot(f_parent_saemix_dfop_tc_moreiter$so, plot.type = "convergence")

 
The four combinations can be compared using the model comparison function from the saemix package:

 
compare.saemix(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so,
-  f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc$so)

+  f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc_moreiter$so)

 
Likelihoods calculated by importance sampling

 
     AIC    BIC
 1 818.37 817.33
 2 820.38 819.14
 3 725.91 724.04
-4 688.09 686.01

+4 683.64 681.55

 
As in the case of nlme fits, the DFOP model fitted with two-component error (number 4) gives the lowest AIC. The numeric values are reasonably close to the ones obtained using nlme, considering that the algorithms for fitting the model and for the likelihood calculation are quite different.

+
In order to check the influence of the likelihood calculation algorithms implemented in saemix, the likelihood from Gaussian quadrature is added to the best fit, and the AIC values obtained from the three methods are compared.

+
f_parent_saemix_dfop_tc_moreiter$so <-
+  llgq.saemix(f_parent_saemix_dfop_tc_moreiter$so)
+AIC(f_parent_saemix_dfop_tc_moreiter$so)

+
[1] 683.64

+
AIC(f_parent_saemix_dfop_tc_moreiter$so, method = "gq")

+
[1] 683.7

+
AIC(f_parent_saemix_dfop_tc_moreiter$so, method = "lin")

+
[1] 683.17

+
The AIC values based on importance sampling and Gaussian quadrature are quite similar. Using linearisation is less accurate, but still gives a similar value.

nlmixr

In the last years, a lot of effort has been put into the nlmixr package which is designed for pharmacokinetics, where nonlinear mixed-effects models are routinely used, but which can also be used for related data like chemical degradation data. A current development branch of the mkin package provides an interface between mkin and nlmixr. Here, we check if we get equivalent results when using a refined version of the First Order Conditional Estimation (FOCE) algorithm used in nlme, namely First Order Conditional Estimation with Interaction (FOCEI), and the SAEM algorithm as implemented in nlmixr.

First, the focei algorithm is used for the four model combinations and the goodness of fit of the results is compared.

-
f_parent_nlmixr_focei_sfo_const <- nlmixr(f_parent_mkin_const["SFO", ], est = "focei")
+
library(nlmixr)
+f_parent_nlmixr_focei_sfo_const <- nlmixr(f_parent_mkin_const["SFO", ], est = "focei")
 f_parent_nlmixr_focei_sfo_tc <- nlmixr(f_parent_mkin_tc["SFO", ], est = "focei")
 f_parent_nlmixr_focei_dfop_const <- nlmixr(f_parent_mkin_const["DFOP", ], est = "focei")
 f_parent_nlmixr_focei_dfop_tc<- nlmixr(f_parent_mkin_tc["DFOP", ], est = "focei")

@@ -1734,7 +1747,14 @@ f_parent_nlmixr_focei_sfo_const$nm   5 818.63
 f_parent_nlmixr_focei_sfo_tc$nm      6 820.61
 f_parent_nlmixr_focei_dfop_const$nm  9 728.11
 f_parent_nlmixr_focei_dfop_tc$nm    10 687.82
-

The AIC values are very close to the ones obtained with nlme.

+

The AIC values are very close to the ones obtained with nlme which are repeated below for convenience.

+
AIC(
+  f_parent_nlme_sfo_const, f_parent_nlme_sfo_tc, f_parent_nlme_dfop_tc
+)
+
                        df    AIC
+f_parent_nlme_sfo_const  5 818.63
+f_parent_nlme_sfo_tc     6 820.61
+f_parent_nlme_dfop_tc   10 687.84

Secondly, we use the SAEM estimation routine and check the convergence plots for SFO with constant variance

f_parent_nlmixr_saem_sfo_const <- nlmixr(f_parent_mkin_const["SFO", ], est = "saem",
   control = nlmixr::saemControl(logLik = TRUE))
@@ -1743,17 +1763,17 @@ traceplot(f_parent_nlmixr_saem_sfo_const$nm)

for SFO with two-component error

f_parent_nlmixr_saem_sfo_tc <- nlmixr(f_parent_mkin_tc["SFO", ], est = "saem",
   control = nlmixr::saemControl(logLik = TRUE))
-nlmixr::traceplot(f_parent_nlmixr_saem_sfo_tc$nm)
+traceplot(f_parent_nlmixr_saem_sfo_tc$nm)

For DFOP with constant variance, the convergence plots show considerable instability of the fit, which can be alleviated by increasing the number of iterations and the number of parallel chains for the first phase of algorithm.

f_parent_nlmixr_saem_dfop_const <- nlmixr(f_parent_mkin_const["DFOP", ], est = "saem",
   control = nlmixr::saemControl(logLik = TRUE, nBurn = 1000), nmc = 15)
-nlmixr::traceplot(f_parent_nlmixr_saem_dfop_const$nm)
+traceplot(f_parent_nlmixr_saem_dfop_const$nm)

For DFOP with two-component error, the same increase in iterations and parallel chains was used, but using the two-component error appears to lead to a less erratic convergence, so this may not be necessary to this degree.

f_parent_nlmixr_saem_dfop_tc <- nlmixr(f_parent_mkin_tc["DFOP", ], est = "saem",
   control = nlmixr::saemControl(logLik = TRUE, nBurn = 1000, nmc = 15))
-nlmixr::traceplot(f_parent_nlmixr_saem_dfop_tc$nm)
+traceplot(f_parent_nlmixr_saem_dfop_tc$nm)

The AIC values are internally calculated using Gaussian quadrature. For an unknown reason, the AIC value obtained for the DFOP fit using the two-component error model is given as Infinity.

AIC(f_parent_nlmixr_saem_sfo_const$nm, f_parent_nlmixr_saem_sfo_tc$nm,
@@ -1761,8 +1781,55 @@ nlmixr::traceplot(f_parent_nlmixr_saem_dfop_tc$nm)
                                   df    AIC
 f_parent_nlmixr_saem_sfo_const$nm   5 820.54
 f_parent_nlmixr_saem_sfo_tc$nm      6 835.26
-f_parent_nlmixr_saem_dfop_const$nm  9 850.72
-f_parent_nlmixr_saem_dfop_tc$nm    10    Inf
+f_parent_nlmixr_saem_dfop_const$nm 9 842.84 +f_parent_nlmixr_saem_dfop_tc$nm 10 684.51 +

The following table gives the AIC values obtained with the three packages.

+
AIC_all <- data.frame(
+  nlme = c(AIC(f_parent_nlme_sfo_const), AIC(f_parent_nlme_sfo_tc), NA, AIC(f_parent_nlme_dfop_tc)),
+  nlmixr_focei = sapply(list(f_parent_nlmixr_focei_sfo_const$nm, f_parent_nlmixr_focei_sfo_tc$nm,
+  f_parent_nlmixr_focei_dfop_const$nm, f_parent_nlmixr_focei_dfop_tc$nm), AIC),
+  saemix = sapply(list(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so,
+    f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc_moreiter$so), AIC),
+  nlmixr_saem = sapply(list(f_parent_nlmixr_saem_sfo_const$nm, f_parent_nlmixr_saem_sfo_tc$nm,
+  f_parent_nlmixr_saem_dfop_const$nm, f_parent_nlmixr_saem_dfop_tc$nm), AIC)
+)
+kable(AIC_all)
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
nlmenlmixr_foceisaemixnlmixr_saem
818.63818.63818.37820.54
820.61820.61820.38835.26
NA728.11725.91842.84
687.84687.82683.64684.51
diff --git a/vignettes/web_only/dimethenamid_2018.rmd b/vignettes/web_only/dimethenamid_2018.rmd index d3541a34..30325044 100644 --- a/vignettes/web_only/dimethenamid_2018.rmd +++ b/vignettes/web_only/dimethenamid_2018.rmd @@ -1,7 +1,7 @@ --- title: Example evaluations of the dimethenamid data from 2018 author: Johannes Ranke -date: Last change 23 June 2021, built on `r format(Sys.Date(), format = "%d %b %Y")` +date: Last change 27 July 2021, built on `r format(Sys.Date(), format = "%d %b %Y")` output: html_document: toc: true @@ -163,8 +163,10 @@ tendency of the algorithm to try parameter combinations unsuitable for fitting these data. ```{r f_parent_nlme, warning = FALSE} +library(nlme) f_parent_nlme_sfo_const <- nlme(f_parent_mkin_const["SFO", ]) -#f_parent_nlme_dfop_const <- nlme(f_parent_mkin_const["DFOP", ]) # error +#f_parent_nlme_dfop_const <- nlme(f_parent_mkin_const["DFOP", ]) +# maxIter = 50 reached f_parent_nlme_sfo_tc <- nlme(f_parent_mkin_tc["SFO", ]) f_parent_nlme_dfop_tc <- nlme(f_parent_mkin_tc["DFOP", ]) ``` @@ -180,10 +182,10 @@ used for these attempts can be made visible below. f_parent_nlme_sfo_const_logchol <- nlme(f_parent_mkin_const["SFO", ], random = pdLogChol(list(DMTA_0 ~ 1, log_k_DMTA ~ 1))) anova(f_parent_nlme_sfo_const, f_parent_nlme_sfo_const_logchol) # not better -f_parent_nlme_dfop_tc_logchol <- update(f_parent_nlme_dfop_tc, - random = pdLogChol(list(DMTA_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1))) +#f_parent_nlme_dfop_tc_logchol <- update(f_parent_nlme_dfop_tc, +# random = pdLogChol(list(DMTA_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1))) # using log Cholesky parameterisation for random effects (nlme default) does -# not converge and gives lots of warnings about the LME step not converging +# not converge here and gives lots of warnings about the LME step not converging ``` The model comparison function of the nlme package can directly be applied @@ -221,7 +223,7 @@ The convergence plot for the SFO model using constant variance is shown below. ```{r f_parent_saemix_sfo_const, results = 'hide'} library(saemix) -f_parent_saemix_sfo_const <- saem(f_parent_mkin_const["SFO", ], quiet = TRUE, +f_parent_saemix_sfo_const <- mkin::saem(f_parent_mkin_const["SFO", ], quiet = TRUE, transformations = "saemix") plot(f_parent_saemix_sfo_const$so, plot.type = "convergence") ``` @@ -230,18 +232,19 @@ Obviously the default number of iterations is sufficient to reach convergence. This can also be said for the SFO fit using the two-component error model. ```{r f_parent_saemix_sfo_tc, results = 'hide'} -f_parent_saemix_sfo_tc <- saem(f_parent_mkin_tc["SFO", ], quiet = TRUE, +f_parent_saemix_sfo_tc <- mkin::saem(f_parent_mkin_tc["SFO", ], quiet = TRUE, transformations = "saemix") plot(f_parent_saemix_sfo_tc$so, plot.type = "convergence") ``` When fitting the DFOP model with constant variance, parameter convergence -is not as unambiguous. Therefore, the number of iterations in the first +is not as unambiguous (see the failure of nlme with the default number of +iterations above). Therefore, the number of iterations in the first phase of the algorithm was increased, leading to visually satisfying convergence. ```{r f_parent_saemix_dfop_const, results = 'hide'} -f_parent_saemix_dfop_const <- saem(f_parent_mkin_const["DFOP", ], quiet = TRUE, +f_parent_saemix_dfop_const <- mkin::saem(f_parent_mkin_const["DFOP", ], quiet = TRUE, control = saemixControl(nbiter.saemix = c(800, 200), print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE), transformations = "saemix") @@ -250,11 +253,11 @@ plot(f_parent_saemix_dfop_const$so, plot.type = "convergence") The same applies to the case where the DFOP model is fitted with the two-component error model. Convergence of the variance of k2 is enhanced -by using the two-component error, it remains pretty stable already after 200 +by using the two-component error, it remains more or less stable already after 200 iterations of the first phase. ```{r f_parent_saemix_dfop_tc_moreiter, results = 'hide'} -f_parent_saemix_dfop_tc_moreiter <- saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE, +f_parent_saemix_dfop_tc_moreiter <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE, control = saemixControl(nbiter.saemix = c(800, 200), print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE), transformations = "saemix") @@ -306,6 +309,7 @@ First, the focei algorithm is used for the four model combinations and the goodness of fit of the results is compared. ```{r f_parent_nlmixr_focei, results = "hide", message = FALSE, warning = FALSE} +library(nlmixr) f_parent_nlmixr_focei_sfo_const <- nlmixr(f_parent_mkin_const["SFO", ], est = "focei") f_parent_nlmixr_focei_sfo_tc <- nlmixr(f_parent_mkin_tc["SFO", ], est = "focei") f_parent_nlmixr_focei_dfop_const <- nlmixr(f_parent_mkin_const["DFOP", ], est = "focei") @@ -317,7 +321,14 @@ AIC(f_parent_nlmixr_focei_sfo_const$nm, f_parent_nlmixr_focei_sfo_tc$nm, f_parent_nlmixr_focei_dfop_const$nm, f_parent_nlmixr_focei_dfop_tc$nm) ``` -The AIC values are very close to the ones obtained with nlme. +The AIC values are very close to the ones obtained with nlme which are repeated below +for convenience. + +```{r AIC_parent_nlme_rep} +AIC( + f_parent_nlme_sfo_const, f_parent_nlme_sfo_tc, f_parent_nlme_dfop_tc +) +``` Secondly, we use the SAEM estimation routine and check the convergence plots for SFO with constant variance @@ -333,7 +344,7 @@ for SFO with two-component error ```{r f_parent_nlmixr_saem_sfo_tc, results = "hide", warning = FALSE, message = FALSE} f_parent_nlmixr_saem_sfo_tc <- nlmixr(f_parent_mkin_tc["SFO", ], est = "saem", control = nlmixr::saemControl(logLik = TRUE)) -nlmixr::traceplot(f_parent_nlmixr_saem_sfo_tc$nm) +traceplot(f_parent_nlmixr_saem_sfo_tc$nm) ``` For DFOP with constant variance, the convergence plots show considerable instability @@ -343,7 +354,7 @@ the number of parallel chains for the first phase of algorithm. ```{r f_parent_nlmixr_saem_dfop_const, results = "hide", warning = FALSE, message = FALSE} f_parent_nlmixr_saem_dfop_const <- nlmixr(f_parent_mkin_const["DFOP", ], est = "saem", control = nlmixr::saemControl(logLik = TRUE, nBurn = 1000), nmc = 15) -nlmixr::traceplot(f_parent_nlmixr_saem_dfop_const$nm) +traceplot(f_parent_nlmixr_saem_dfop_const$nm) ``` For DFOP with two-component error, the same increase in iterations and parallel @@ -354,7 +365,7 @@ erratic convergence, so this may not be necessary to this degree. ```{r f_parent_nlmixr_saem_dfop_tc, results = "hide", warning = FALSE, message = FALSE} f_parent_nlmixr_saem_dfop_tc <- nlmixr(f_parent_mkin_tc["DFOP", ], est = "saem", control = nlmixr::saemControl(logLik = TRUE, nBurn = 1000, nmc = 15)) -nlmixr::traceplot(f_parent_nlmixr_saem_dfop_tc$nm) +traceplot(f_parent_nlmixr_saem_dfop_tc$nm) ``` The AIC values are internally calculated using Gaussian quadrature. For an @@ -366,8 +377,20 @@ AIC(f_parent_nlmixr_saem_sfo_const$nm, f_parent_nlmixr_saem_sfo_tc$nm, f_parent_nlmixr_saem_dfop_const$nm, f_parent_nlmixr_saem_dfop_tc$nm) ``` - - +The following table gives the AIC values obtained with the three packages. + +```{r AIC_all} +AIC_all <- data.frame( + nlme = c(AIC(f_parent_nlme_sfo_const), AIC(f_parent_nlme_sfo_tc), NA, AIC(f_parent_nlme_dfop_tc)), + nlmixr_focei = sapply(list(f_parent_nlmixr_focei_sfo_const$nm, f_parent_nlmixr_focei_sfo_tc$nm, + f_parent_nlmixr_focei_dfop_const$nm, f_parent_nlmixr_focei_dfop_tc$nm), AIC), + saemix = sapply(list(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so, + f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc_moreiter$so), AIC), + nlmixr_saem = sapply(list(f_parent_nlmixr_saem_sfo_const$nm, f_parent_nlmixr_saem_sfo_tc$nm, + f_parent_nlmixr_saem_dfop_const$nm, f_parent_nlmixr_saem_dfop_tc$nm), AIC) +) +kable(AIC_all) +``` # References -- cgit v1.2.1