From 91a5834dd701211f929fd25419dc34561ce3b4e7 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Fri, 14 Feb 2025 09:15:20 +0100 Subject: Initialize dev docs --- docs/dev/reference/saem.html | 731 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 731 insertions(+) create mode 100644 docs/dev/reference/saem.html (limited to 'docs/dev/reference/saem.html') diff --git a/docs/dev/reference/saem.html b/docs/dev/reference/saem.html new file mode 100644 index 00000000..25eb03c7 --- /dev/null +++ b/docs/dev/reference/saem.html @@ -0,0 +1,731 @@ + +Fit nonlinear mixed models with SAEM — saem • mkin + Skip to contents + + +
+
+
+ +

Fit nonlinear mixed models with SAEM

+ Source: R/saem.R +
saem.Rd
+
+ +
+

This function uses saemix::saemix() as a backend for fitting nonlinear mixed +effects models created from mmkin row objects using the Stochastic Approximation +Expectation Maximisation algorithm (SAEM).

+
+ +
+

Usage

+ 
saem(object, ...)
+
+# S3 method for class 'mmkin'
+saem(
+  object,
+  transformations = c("mkin", "saemix"),
+  error_model = "auto",
+  degparms_start = numeric(),
+  test_log_parms = TRUE,
+  conf.level = 0.6,
+  solution_type = "auto",
+  covariance.model = "auto",
+  omega.init = "auto",
+  covariates = NULL,
+  covariate_models = NULL,
+  no_random_effect = NULL,
+  error.init = c(1, 1),
+  nbiter.saemix = c(300, 100),
+  control = list(displayProgress = FALSE, print = FALSE, nbiter.saemix = nbiter.saemix,
+    save = FALSE, save.graphs = FALSE),
+  verbose = FALSE,
+  quiet = FALSE,
+  ...
+)
+
+# S3 method for class 'saem.mmkin'
+print(x, digits = max(3, getOption("digits") - 3), ...)
+
+saemix_model(
+  object,
+  solution_type = "auto",
+  transformations = c("mkin", "saemix"),
+  error_model = "auto",
+  degparms_start = numeric(),
+  covariance.model = "auto",
+  no_random_effect = NULL,
+  omega.init = "auto",
+  covariates = NULL,
+  covariate_models = NULL,
+  error.init = numeric(),
+  test_log_parms = FALSE,
+  conf.level = 0.6,
+  verbose = FALSE,
+  ...
+)
+
+saemix_data(object, covariates = NULL, verbose = FALSE, ...)
+
+ +
+

Arguments

+ + +
object
+

An mmkin row object containing several fits of the same +mkinmod model to different datasets

+ + +
...
+

Further parameters passed to saemix::saemixModel.

+ + +
transformations
+

Per default, all parameter transformations are done +in mkin. If this argument is set to 'saemix', parameter transformations +are done in 'saemix' for the supported cases, i.e. (as of version 1.1.2) +SFO, FOMC, DFOP and HS without fixing parent_0, and SFO or DFOP with +one SFO metabolite.

+ + +
error_model
+

Possibility to override the error model used in the mmkin object

+ + +
degparms_start
+

Parameter values given as a named numeric vector will +be used to override the starting values obtained from the 'mmkin' object.

+ + +
test_log_parms
+

If TRUE, an attempt is made to use more robust starting +values for population parameters fitted as log parameters in mkin (like +rate constants) by only considering rate constants that pass the t-test +when calculating mean degradation parameters using mean_degparms.

+ + +
conf.level
+

Possibility to adjust the required confidence level +for parameter that are tested if requested by 'test_log_parms'.

+ + +
solution_type
+

Possibility to specify the solution type in case the +automatic choice is not desired

+ + +
covariance.model
+

Will be passed to saemix::saemixModel(). Per +default, uncorrelated random effects are specified for all degradation +parameters.

+ + +
omega.init
+

Will be passed to saemix::saemixModel(). If using +mkin transformations and the default covariance model with optionally +excluded random effects, the variances of the degradation parameters +are estimated using mean_degparms, with testing of untransformed +log parameters for significant difference from zero. If not using +mkin transformations or a custom covariance model, the default +initialisation of saemix::saemixModel is used for omega.init.

+ + +
covariates
+

A data frame with covariate data for use in +'covariate_models', with dataset names as row names.

+ + +
covariate_models
+

A list containing linear model formulas with one explanatory +variable, i.e. of the type 'parameter ~ covariate'. Covariates must be available +in the 'covariates' data frame.

+ + +
no_random_effect
+

Character vector of degradation parameters for +which there should be no variability over the groups. Only used +if the covariance model is not explicitly specified.

+ + +
error.init
+

Will be passed to saemix::saemixModel().

+ + +
nbiter.saemix
+

Convenience option to increase the number of +iterations

+ + +
control
+

Passed to saemix::saemix.

+ + +
verbose
+

Should we print information about created objects of +type saemix::SaemixModel and saemix::SaemixData?

+ + +
quiet
+

Should we suppress the messages saemix prints at the beginning +and the end of the optimisation process?

+ + +
x
+

An saem.mmkin object to print

+ + +
digits
+

Number of digits to use for printing

+ +
+
+

Value

+

An S3 object of class 'saem.mmkin', containing the fitted +saemix::SaemixObject as a list component named 'so'. The +object also inherits from 'mixed.mmkin'.

+

An saemix::SaemixModel object.

+

An saemix::SaemixData object.

+
+
+

Details

+

An mmkin row object is essentially a list of mkinfit objects that have been +obtained by fitting the same model to a list of datasets using mkinfit.

+

Starting values for the fixed effects (population mean parameters, argument +psi0 of saemix::saemixModel() are the mean values of the parameters found +using mmkin.

+
+
+

See also

+

summary.saem.mmkin plot.mixed.mmkin

+
+ +
+

Examples

+ 
# \dontrun{
+ds <- lapply(experimental_data_for_UBA_2019[6:10],
+ function(x) subset(x$data[c("name", "time", "value")]))
+names(ds) <- paste("Dataset", 6:10)
+f_mmkin_parent_p0_fixed <- mmkin("FOMC", ds,
+  state.ini = c(parent = 100), fixed_initials = "parent", quiet = TRUE)
+f_saem_p0_fixed <- saem(f_mmkin_parent_p0_fixed)
+
+f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE)
+f_saem_sfo <- saem(f_mmkin_parent["SFO", ])
+f_saem_fomc <- saem(f_mmkin_parent["FOMC", ])
+f_saem_dfop <- saem(f_mmkin_parent["DFOP", ])
+anova(f_saem_sfo, f_saem_fomc, f_saem_dfop)
+#> Data: 90 observations of 1 variable(s) grouped in 5 datasets
+#> 
+#>             npar    AIC    BIC     Lik
+#> f_saem_sfo     5 624.33 622.38 -307.17
+#> f_saem_fomc    7 467.85 465.11 -226.92
+#> f_saem_dfop    9 493.76 490.24 -237.88
+anova(f_saem_sfo, f_saem_dfop, test = TRUE)
+#> Data: 90 observations of 1 variable(s) grouped in 5 datasets
+#> 
+#>             npar    AIC    BIC     Lik  Chisq Df Pr(>Chisq)    
+#> f_saem_sfo     5 624.33 622.38 -307.17                         
+#> f_saem_dfop    9 493.76 490.24 -237.88 138.57  4  < 2.2e-16 ***
+#> ---
+#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+illparms(f_saem_dfop)
+#> [1] "sd(g_qlogis)"
+f_saem_dfop_red <- update(f_saem_dfop, no_random_effect = "g_qlogis")
+anova(f_saem_dfop, f_saem_dfop_red, test = TRUE)
+#> Data: 90 observations of 1 variable(s) grouped in 5 datasets
+#> 
+#>                 npar    AIC    BIC     Lik Chisq Df Pr(>Chisq)
+#> f_saem_dfop_red    8 488.68 485.55 -236.34                    
+#> f_saem_dfop        9 493.76 490.24 -237.88     0  1          1
+
+anova(f_saem_sfo, f_saem_fomc, f_saem_dfop)
+#> Data: 90 observations of 1 variable(s) grouped in 5 datasets
+#> 
+#>             npar    AIC    BIC     Lik
+#> f_saem_sfo     5 624.33 622.38 -307.17
+#> f_saem_fomc    7 467.85 465.11 -226.92
+#> f_saem_dfop    9 493.76 490.24 -237.88
+# The returned saem.mmkin object contains an SaemixObject, therefore we can use
+# functions from saemix
+library(saemix)
+#> Loading required package: npde
+#> Package saemix, version 3.3, March 2024
+#>   please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr
+#> 
+#> Attaching package: ‘saemix’
+#> The following objects are masked from ‘package:npde’:
+#> 
+#>     kurtosis, skewness
+compare.saemix(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so)
+#> Likelihoods calculated by importance sampling
+#>        AIC      BIC
+#> 1 624.3316 622.3788
+#> 2 467.8472 465.1132
+#> 3 493.7592 490.2441
+plot(f_saem_fomc$so, plot.type = "convergence")
+
+plot(f_saem_fomc$so, plot.type = "individual.fit")
+#> Simulating data using nsim = 1000 simulated datasets
+#> Computing WRES and npde .
+
+plot(f_saem_fomc$so, plot.type = "npde")
+#> Simulating data using nsim = 1000 simulated datasets
+#> Computing WRES and npde .
+#> Please use npdeSaemix to obtain VPC and npde
+plot(f_saem_fomc$so, plot.type = "vpc")
+
+
+f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = "tc")
+f_saem_fomc_tc <- saem(f_mmkin_parent_tc["FOMC", ])
+anova(f_saem_fomc, f_saem_fomc_tc, test = TRUE)
+#> Data: 90 observations of 1 variable(s) grouped in 5 datasets
+#> 
+#>                npar    AIC    BIC     Lik Chisq Df Pr(>Chisq)
+#> f_saem_fomc       7 467.85 465.11 -226.92                    
+#> f_saem_fomc_tc    8 469.90 466.77 -226.95     0  1          1
+
+sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"),
+  A1 = mkinsub("SFO"))
+#> Temporary DLL for differentials generated and loaded
+fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"),
+  A1 = mkinsub("SFO"))
+#> Temporary DLL for differentials generated and loaded
+dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
+  A1 = mkinsub("SFO"))
+#> Temporary DLL for differentials generated and loaded
+# The following fit uses analytical solutions for SFO-SFO and DFOP-SFO,
+# and compiled ODEs for FOMC that are much slower
+f_mmkin <- mmkin(list(
+    "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo),
+  ds, quiet = TRUE)
+# saem fits of SFO-SFO and DFOP-SFO to these data take about five seconds
+# each on this system, as we use analytical solutions written for saemix.
+# When using the analytical solutions written for mkin this took around
+# four minutes
+f_saem_sfo_sfo <- saem(f_mmkin["SFO-SFO", ])
+f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ])
+# 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:
+#> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
+#>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
+#>            * parent
+#> d_A1/dt = + f_parent_to_A1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)
+#>            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
+#>            exp(-k2 * time))) * parent - k_A1 * A1
+#> 
+#> Data:
+#> 170 observations of 2 variable(s) grouped in 5 datasets
+#> 
+#> Likelihood computed by importance sampling
+#>     AIC   BIC logLik
+#>   839.2 834.1 -406.6
+#> 
+#> Fitted parameters:
+#>                    estimate    lower   upper
+#> parent_0           93.70402 91.04104 96.3670
+#> log_k_A1           -5.83760 -7.66452 -4.0107
+#> f_parent_qlogis    -0.95718 -1.35955 -0.5548
+#> log_k1             -2.35514 -3.39402 -1.3163
+#> log_k2             -3.79634 -5.64009 -1.9526
+#> g_qlogis           -0.02108 -0.66463  0.6225
+#> a.1                 1.88191  1.66491  2.0989
+#> SD.parent_0         2.81628  0.78922  4.8433
+#> SD.log_k_A1         1.78751  0.42105  3.1540
+#> SD.f_parent_qlogis  0.45016  0.16116  0.7391
+#> SD.log_k1           1.06923  0.31676  1.8217
+#> SD.log_k2           2.03768  0.70938  3.3660
+#> SD.g_qlogis         0.44024 -0.09262  0.9731
+plot(f_saem_dfop_sfo)
+
+summary(f_saem_dfop_sfo, data = TRUE)
+#> saemix version used for fitting:      3.3 
+#> mkin version used for pre-fitting:  1.2.10 
+#> R version used for fitting:         4.4.2 
+#> Date of fit:     Fri Feb 14 07:32:13 2025 
+#> Date of summary: Fri Feb 14 07:32:13 2025 
+#> 
+#> Equations:
+#> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
+#>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
+#>            * parent
+#> d_A1/dt = + f_parent_to_A1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)
+#>            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
+#>            exp(-k2 * time))) * parent - k_A1 * A1
+#> 
+#> Data:
+#> 170 observations of 2 variable(s) grouped in 5 datasets
+#> 
+#> Model predictions using solution type analytical 
+#> 
+#> Fitted in 3.605 s
+#> Using 300, 100 iterations and 10 chains
+#> 
+#> Variance model: Constant variance 
+#> 
+#> Starting values for degradation parameters:
+#>        parent_0        log_k_A1 f_parent_qlogis          log_k1          log_k2 
+#>         93.8102         -5.3734         -0.9711         -1.8799         -4.2708 
+#>        g_qlogis 
+#>          0.1356 
+#> 
+#> Fixed degradation parameter values:
+#> None
+#> 
+#> Starting values for random effects (square root of initial entries in omega):
+#>                 parent_0 log_k_A1 f_parent_qlogis log_k1 log_k2 g_qlogis
+#> parent_0           4.941    0.000          0.0000  0.000  0.000   0.0000
+#> log_k_A1           0.000    2.551          0.0000  0.000  0.000   0.0000
+#> f_parent_qlogis    0.000    0.000          0.7251  0.000  0.000   0.0000
+#> log_k1             0.000    0.000          0.0000  1.449  0.000   0.0000
+#> log_k2             0.000    0.000          0.0000  0.000  2.228   0.0000
+#> g_qlogis           0.000    0.000          0.0000  0.000  0.000   0.7814
+#> 
+#> Starting values for error model parameters:
+#> a.1 
+#>   1 
+#> 
+#> Results:
+#> 
+#> Likelihood computed by importance sampling
+#>     AIC   BIC logLik
+#>   839.2 834.1 -406.6
+#> 
+#> Optimised parameters:
+#>                        est.    lower   upper
+#> parent_0           93.70402 91.04104 96.3670
+#> log_k_A1           -5.83760 -7.66452 -4.0107
+#> f_parent_qlogis    -0.95718 -1.35955 -0.5548
+#> log_k1             -2.35514 -3.39402 -1.3163
+#> log_k2             -3.79634 -5.64009 -1.9526
+#> g_qlogis           -0.02108 -0.66463  0.6225
+#> a.1                 1.88191  1.66491  2.0989
+#> SD.parent_0         2.81628  0.78922  4.8433
+#> SD.log_k_A1         1.78751  0.42105  3.1540
+#> SD.f_parent_qlogis  0.45016  0.16116  0.7391
+#> SD.log_k1           1.06923  0.31676  1.8217
+#> SD.log_k2           2.03768  0.70938  3.3660
+#> SD.g_qlogis         0.44024 -0.09262  0.9731
+#> 
+#> Correlation: 
+#>                 parnt_0 lg_k_A1 f_prnt_ log_k1  log_k2 
+#> log_k_A1        -0.0147                                
+#> f_parent_qlogis -0.0269  0.0573                        
+#> log_k1           0.0263 -0.0011 -0.0040                
+#> log_k2           0.0020  0.0065 -0.0002 -0.0776        
+#> g_qlogis        -0.0248 -0.0180 -0.0004 -0.0903 -0.0603
+#> 
+#> Random effects:
+#>                      est.    lower  upper
+#> SD.parent_0        2.8163  0.78922 4.8433
+#> SD.log_k_A1        1.7875  0.42105 3.1540
+#> SD.f_parent_qlogis 0.4502  0.16116 0.7391
+#> SD.log_k1          1.0692  0.31676 1.8217
+#> SD.log_k2          2.0377  0.70938 3.3660
+#> SD.g_qlogis        0.4402 -0.09262 0.9731
+#> 
+#> Variance model:
+#>      est. lower upper
+#> a.1 1.882 1.665 2.099
+#> 
+#> Backtransformed parameters:
+#>                     est.     lower    upper
+#> parent_0       93.704015 9.104e+01 96.36699
+#> k_A1            0.002916 4.692e-04  0.01812
+#> f_parent_to_A1  0.277443 2.043e-01  0.36475
+#> k1              0.094880 3.357e-02  0.26813
+#> k2              0.022453 3.553e-03  0.14191
+#> g               0.494731 3.397e-01  0.65078
+#> 
+#> Resulting formation fractions:
+#>                 ff
+#> parent_A1   0.2774
+#> parent_sink 0.7226
+#> 
+#> Estimated disappearance times:
+#>         DT50   DT90 DT50back DT50_k1 DT50_k2
+#> parent  14.0  72.38    21.79   7.306   30.87
+#> A1     237.7 789.68       NA      NA      NA
+#> 
+#> Data:
+#>          ds   name time observed predicted residual   std standardized
+#>   Dataset 6 parent    0     97.2  95.70025  1.49975 1.882      0.79693
+#>   Dataset 6 parent    0     96.4  95.70025  0.69975 1.882      0.37183
+#>   Dataset 6 parent    3     71.1  71.44670 -0.34670 1.882     -0.18423
+#>   Dataset 6 parent    3     69.2  71.44670 -2.24670 1.882     -1.19384
+#>   Dataset 6 parent    6     58.1  56.59283  1.50717 1.882      0.80087
+#>   Dataset 6 parent    6     56.6  56.59283  0.00717 1.882      0.00381
+#>   Dataset 6 parent   10     44.4  44.56648 -0.16648 1.882     -0.08847
+#>   Dataset 6 parent   10     43.4  44.56648 -1.16648 1.882     -0.61984
+#>   Dataset 6 parent   20     33.3  29.76020  3.53980 1.882      1.88096
+#>   Dataset 6 parent   20     29.2  29.76020 -0.56020 1.882     -0.29767
+#>   Dataset 6 parent   34     17.6  19.39208 -1.79208 1.882     -0.95226
+#>   Dataset 6 parent   34     18.0  19.39208 -1.39208 1.882     -0.73971
+#>   Dataset 6 parent   55     10.5  10.55761 -0.05761 1.882     -0.03061
+#>   Dataset 6 parent   55      9.3  10.55761 -1.25761 1.882     -0.66826
+#>   Dataset 6 parent   90      4.5   3.84742  0.65258 1.882      0.34676
+#>   Dataset 6 parent   90      4.7   3.84742  0.85258 1.882      0.45304
+#>   Dataset 6 parent  112      3.0   2.03997  0.96003 1.882      0.51013
+#>   Dataset 6 parent  112      3.4   2.03997  1.36003 1.882      0.72268
+#>   Dataset 6 parent  132      2.3   1.14585  1.15415 1.882      0.61328
+#>   Dataset 6 parent  132      2.7   1.14585  1.55415 1.882      0.82583
+#>   Dataset 6     A1    3      4.3   4.86054 -0.56054 1.882     -0.29786
+#>   Dataset 6     A1    3      4.6   4.86054 -0.26054 1.882     -0.13844
+#>   Dataset 6     A1    6      7.0   7.74179 -0.74179 1.882     -0.39417
+#>   Dataset 6     A1    6      7.2   7.74179 -0.54179 1.882     -0.28789
+#>   Dataset 6     A1   10      8.2   9.94048 -1.74048 1.882     -0.92485
+#>   Dataset 6     A1   10      8.0   9.94048 -1.94048 1.882     -1.03112
+#>   Dataset 6     A1   20     11.0  12.19109 -1.19109 1.882     -0.63291
+#>   Dataset 6     A1   20     13.7  12.19109  1.50891 1.882      0.80180
+#>   Dataset 6     A1   34     11.5  13.10706 -1.60706 1.882     -0.85395
+#>   Dataset 6     A1   34     12.7  13.10706 -0.40706 1.882     -0.21630
+#>   Dataset 6     A1   55     14.9  13.06131  1.83869 1.882      0.97703
+#>   Dataset 6     A1   55     14.5  13.06131  1.43869 1.882      0.76448
+#>   Dataset 6     A1   90     12.1  11.54495  0.55505 1.882      0.29494
+#>   Dataset 6     A1   90     12.3  11.54495  0.75505 1.882      0.40122
+#>   Dataset 6     A1  112      9.9  10.31533 -0.41533 1.882     -0.22070
+#>   Dataset 6     A1  112     10.2  10.31533 -0.11533 1.882     -0.06128
+#>   Dataset 6     A1  132      8.8   9.20222 -0.40222 1.882     -0.21373
+#>   Dataset 6     A1  132      7.8   9.20222 -1.40222 1.882     -0.74510
+#>   Dataset 7 parent    0     93.6  90.82357  2.77643 1.882      1.47532
+#>   Dataset 7 parent    0     92.3  90.82357  1.47643 1.882      0.78453
+#>   Dataset 7 parent    3     87.0  84.73448  2.26552 1.882      1.20384
+#>   Dataset 7 parent    3     82.2  84.73448 -2.53448 1.882     -1.34675
+#>   Dataset 7 parent    7     74.0  77.65013 -3.65013 1.882     -1.93958
+#>   Dataset 7 parent    7     73.9  77.65013 -3.75013 1.882     -1.99272
+#>   Dataset 7 parent   14     64.2  67.60639 -3.40639 1.882     -1.81007
+#>   Dataset 7 parent   14     69.5  67.60639  1.89361 1.882      1.00621
+#>   Dataset 7 parent   30     54.0  52.53663  1.46337 1.882      0.77760
+#>   Dataset 7 parent   30     54.6  52.53663  2.06337 1.882      1.09642
+#>   Dataset 7 parent   60     41.1  39.42728  1.67272 1.882      0.88884
+#>   Dataset 7 parent   60     38.4  39.42728 -1.02728 1.882     -0.54587
+#>   Dataset 7 parent   90     32.5  33.76360 -1.26360 1.882     -0.67144
+#>   Dataset 7 parent   90     35.5  33.76360  1.73640 1.882      0.92268
+#>   Dataset 7 parent  120     28.1  30.39975 -2.29975 1.882     -1.22203
+#>   Dataset 7 parent  120     29.0  30.39975 -1.39975 1.882     -0.74379
+#>   Dataset 7 parent  180     26.5  25.62379  0.87621 1.882      0.46559
+#>   Dataset 7 parent  180     27.6  25.62379  1.97621 1.882      1.05010
+#>   Dataset 7     A1    3      3.9   2.70005  1.19995 1.882      0.63762
+#>   Dataset 7     A1    3      3.1   2.70005  0.39995 1.882      0.21252
+#>   Dataset 7     A1    7      6.9   5.83475  1.06525 1.882      0.56605
+#>   Dataset 7     A1    7      6.6   5.83475  0.76525 1.882      0.40663
+#>   Dataset 7     A1   14     10.4  10.26142  0.13858 1.882      0.07364
+#>   Dataset 7     A1   14      8.3  10.26142 -1.96142 1.882     -1.04225
+#>   Dataset 7     A1   30     14.4  16.82999 -2.42999 1.882     -1.29123
+#>   Dataset 7     A1   30     13.7  16.82999 -3.12999 1.882     -1.66319
+#>   Dataset 7     A1   60     22.1  22.32486 -0.22486 1.882     -0.11949
+#>   Dataset 7     A1   60     22.3  22.32486 -0.02486 1.882     -0.01321
+#>   Dataset 7     A1   90     27.5  24.45927  3.04073 1.882      1.61576
+#>   Dataset 7     A1   90     25.4  24.45927  0.94073 1.882      0.49988
+#>   Dataset 7     A1  120     28.0  25.54862  2.45138 1.882      1.30260
+#>   Dataset 7     A1  120     26.6  25.54862  1.05138 1.882      0.55868
+#>   Dataset 7     A1  180     25.8  26.82277 -1.02277 1.882     -0.54347
+#>   Dataset 7     A1  180     25.3  26.82277 -1.52277 1.882     -0.80916
+#>   Dataset 8 parent    0     91.9  91.16791  0.73209 1.882      0.38901
+#>   Dataset 8 parent    0     90.8  91.16791 -0.36791 1.882     -0.19550
+#>   Dataset 8 parent    1     64.9  67.58358 -2.68358 1.882     -1.42598
+#>   Dataset 8 parent    1     66.2  67.58358 -1.38358 1.882     -0.73520
+#>   Dataset 8 parent    3     43.5  41.62086  1.87914 1.882      0.99853
+#>   Dataset 8 parent    3     44.1  41.62086  2.47914 1.882      1.31735
+#>   Dataset 8 parent    8     18.3  19.60116 -1.30116 1.882     -0.69140
+#>   Dataset 8 parent    8     18.1  19.60116 -1.50116 1.882     -0.79768
+#>   Dataset 8 parent   14     10.2  10.63101 -0.43101 1.882     -0.22903
+#>   Dataset 8 parent   14     10.8  10.63101  0.16899 1.882      0.08980
+#>   Dataset 8 parent   27      4.9   3.12435  1.77565 1.882      0.94354
+#>   Dataset 8 parent   27      3.3   3.12435  0.17565 1.882      0.09334
+#>   Dataset 8 parent   48      1.6   0.43578  1.16422 1.882      0.61864
+#>   Dataset 8 parent   48      1.5   0.43578  1.06422 1.882      0.56550
+#>   Dataset 8 parent   70      1.1   0.05534  1.04466 1.882      0.55510
+#>   Dataset 8 parent   70      0.9   0.05534  0.84466 1.882      0.44883
+#>   Dataset 8     A1    1      9.6   7.63450  1.96550 1.882      1.04442
+#>   Dataset 8     A1    1      7.7   7.63450  0.06550 1.882      0.03481
+#>   Dataset 8     A1    3     15.0  15.52593 -0.52593 1.882     -0.27947
+#>   Dataset 8     A1    3     15.1  15.52593 -0.42593 1.882     -0.22633
+#>   Dataset 8     A1    8     21.2  20.32192  0.87808 1.882      0.46659
+#>   Dataset 8     A1    8     21.1  20.32192  0.77808 1.882      0.41345
+#>   Dataset 8     A1   14     19.7  20.09721 -0.39721 1.882     -0.21107
+#>   Dataset 8     A1   14     18.9  20.09721 -1.19721 1.882     -0.63617
+#>   Dataset 8     A1   27     17.5  16.37477  1.12523 1.882      0.59792
+#>   Dataset 8     A1   27     15.9  16.37477 -0.47477 1.882     -0.25228
+#>   Dataset 8     A1   48      9.5  10.13141 -0.63141 1.882     -0.33551
+#>   Dataset 8     A1   48      9.8  10.13141 -0.33141 1.882     -0.17610
+#>   Dataset 8     A1   70      6.2   5.81827  0.38173 1.882      0.20284
+#>   Dataset 8     A1   70      6.1   5.81827  0.28173 1.882      0.14970
+#>   Dataset 9 parent    0     99.8  97.48728  2.31272 1.882      1.22892
+#>   Dataset 9 parent    0     98.3  97.48728  0.81272 1.882      0.43186
+#>   Dataset 9 parent    1     77.1  79.29476 -2.19476 1.882     -1.16624
+#>   Dataset 9 parent    1     77.2  79.29476 -2.09476 1.882     -1.11310
+#>   Dataset 9 parent    3     59.0  55.67060  3.32940 1.882      1.76915
+#>   Dataset 9 parent    3     58.1  55.67060  2.42940 1.882      1.29092
+#>   Dataset 9 parent    8     27.4  31.57871 -4.17871 1.882     -2.22046
+#>   Dataset 9 parent    8     29.2  31.57871 -2.37871 1.882     -1.26398
+#>   Dataset 9 parent   14     19.1  22.51546 -3.41546 1.882     -1.81489
+#>   Dataset 9 parent   14     29.6  22.51546  7.08454 1.882      3.76454
+#>   Dataset 9 parent   27     10.1  14.09074 -3.99074 1.882     -2.12057
+#>   Dataset 9 parent   27     18.2  14.09074  4.10926 1.882      2.18355
+#>   Dataset 9 parent   48      4.5   6.95747 -2.45747 1.882     -1.30584
+#>   Dataset 9 parent   48      9.1   6.95747  2.14253 1.882      1.13848
+#>   Dataset 9 parent   70      2.3   3.32472 -1.02472 1.882     -0.54451
+#>   Dataset 9 parent   70      2.9   3.32472 -0.42472 1.882     -0.22569
+#>   Dataset 9 parent   91      2.0   1.64300  0.35700 1.882      0.18970
+#>   Dataset 9 parent   91      1.8   1.64300  0.15700 1.882      0.08343
+#>   Dataset 9 parent  120      2.0   0.62073  1.37927 1.882      0.73291
+#>   Dataset 9 parent  120      2.2   0.62073  1.57927 1.882      0.83918
+#>   Dataset 9     A1    1      4.2   3.64568  0.55432 1.882      0.29455
+#>   Dataset 9     A1    1      3.9   3.64568  0.25432 1.882      0.13514
+#>   Dataset 9     A1    3      7.4   8.30173 -0.90173 1.882     -0.47916
+#>   Dataset 9     A1    3      7.9   8.30173 -0.40173 1.882     -0.21347
+#>   Dataset 9     A1    8     14.5  12.71589  1.78411 1.882      0.94803
+#>   Dataset 9     A1    8     13.7  12.71589  0.98411 1.882      0.52293
+#>   Dataset 9     A1   14     14.2  13.90452  0.29548 1.882      0.15701
+#>   Dataset 9     A1   14     12.2  13.90452 -1.70452 1.882     -0.90574
+#>   Dataset 9     A1   27     13.7  14.15523 -0.45523 1.882     -0.24190
+#>   Dataset 9     A1   27     13.2  14.15523 -0.95523 1.882     -0.50759
+#>   Dataset 9     A1   48     13.6  13.31038  0.28962 1.882      0.15389
+#>   Dataset 9     A1   48     15.4  13.31038  2.08962 1.882      1.11037
+#>   Dataset 9     A1   70     10.4  11.85965 -1.45965 1.882     -0.77562
+#>   Dataset 9     A1   70     11.6  11.85965 -0.25965 1.882     -0.13797
+#>   Dataset 9     A1   91     10.0  10.36294 -0.36294 1.882     -0.19286
+#>   Dataset 9     A1   91      9.5  10.36294 -0.86294 1.882     -0.45855
+#>   Dataset 9     A1  120      9.1   8.43003  0.66997 1.882      0.35601
+#>   Dataset 9     A1  120      9.0   8.43003  0.56997 1.882      0.30287
+#>  Dataset 10 parent    0     96.1  93.95603  2.14397 1.882      1.13925
+#>  Dataset 10 parent    0     94.3  93.95603  0.34397 1.882      0.18278
+#>  Dataset 10 parent    8     73.9  77.70592 -3.80592 1.882     -2.02237
+#>  Dataset 10 parent    8     73.9  77.70592 -3.80592 1.882     -2.02237
+#>  Dataset 10 parent   14     69.4  70.04570 -0.64570 1.882     -0.34311
+#>  Dataset 10 parent   14     73.1  70.04570  3.05430 1.882      1.62298
+#>  Dataset 10 parent   21     65.6  64.01710  1.58290 1.882      0.84111
+#>  Dataset 10 parent   21     65.3  64.01710  1.28290 1.882      0.68170
+#>  Dataset 10 parent   41     55.9  54.98434  0.91566 1.882      0.48656
+#>  Dataset 10 parent   41     54.4  54.98434 -0.58434 1.882     -0.31050
+#>  Dataset 10 parent   63     47.0  49.87137 -2.87137 1.882     -1.52577
+#>  Dataset 10 parent   63     49.3  49.87137 -0.57137 1.882     -0.30361
+#>  Dataset 10 parent   91     44.7  45.06727 -0.36727 1.882     -0.19516
+#>  Dataset 10 parent   91     46.7  45.06727  1.63273 1.882      0.86759
+#>  Dataset 10 parent  120     42.1  40.76402  1.33598 1.882      0.70991
+#>  Dataset 10 parent  120     41.3  40.76402  0.53598 1.882      0.28481
+#>  Dataset 10     A1    8      3.3   4.14599 -0.84599 1.882     -0.44954
+#>  Dataset 10     A1    8      3.4   4.14599 -0.74599 1.882     -0.39640
+#>  Dataset 10     A1   14      3.9   6.08478 -2.18478 1.882     -1.16093
+#>  Dataset 10     A1   14      2.9   6.08478 -3.18478 1.882     -1.69231
+#>  Dataset 10     A1   21      6.4   7.59411 -1.19411 1.882     -0.63452
+#>  Dataset 10     A1   21      7.2   7.59411 -0.39411 1.882     -0.20942
+#>  Dataset 10     A1   41      9.1   9.78292 -0.68292 1.882     -0.36289
+#>  Dataset 10     A1   41      8.5   9.78292 -1.28292 1.882     -0.68171
+#>  Dataset 10     A1   63     11.7  10.93274  0.76726 1.882      0.40770
+#>  Dataset 10     A1   63     12.0  10.93274  1.06726 1.882      0.56711
+#>  Dataset 10     A1   91     13.3  11.93986  1.36014 1.882      0.72274
+#>  Dataset 10     A1   91     13.2  11.93986  1.26014 1.882      0.66961
+#>  Dataset 10     A1  120     14.3  12.79238  1.50762 1.882      0.80111
+#>  Dataset 10     A1  120     12.1  12.79238 -0.69238 1.882     -0.36791
+
+# The following takes about 6 minutes
+f_saem_dfop_sfo_deSolve <- saem(f_mmkin["DFOP-SFO", ], solution_type = "deSolve",
+  nbiter.saemix = c(200, 80))
+#> DINTDY-  T (=R1) illegal      
+#> In above message, R1 = 70
+#>  
+#>       T not in interval TCUR - HU (= R1) to TCUR (=R2)      
+#> In above message, R1 = 53.1042, R2 = 56.6326
+#>  
+#> DINTDY-  T (=R1) illegal      
+#> In above message, R1 = 91
+#>  
+#>       T not in interval TCUR - HU (= R1) to TCUR (=R2)      
+#> In above message, R1 = 53.1042, R2 = 56.6326
+#>  
+#> DLSODA-  Trouble in DINTDY.  ITASK = I1, TOUT = R1
+#> In above message, I1 = 1
+#>  
+#> In above message, R1 = 91
+#>  
+#> Error in deSolve::lsoda(y = odeini, times = outtimes, func = lsoda_func,  : 
+#>   illegal input detected before taking any integration steps - see written message
+
+#anova(
+#  f_saem_dfop_sfo,
+#  f_saem_dfop_sfo_deSolve))
+
+# If the model supports it, we can also use eigenvalue based solutions, which
+# take a similar amount of time
+#f_saem_sfo_sfo_eigen <- saem(f_mmkin["SFO-SFO", ], solution_type = "eigen",
+#  control = list(nbiter.saemix = c(200, 80), nbdisplay = 10))
+# }
+
+
+
+ + +
+ + + + + + + -- cgit v1.2.1