From 6476f5f49b373cd4cf05f2e73389df83e437d597 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Thu, 13 Feb 2025 16:30:31 +0100 Subject: Axis legend formatting, update vignettes --- docs/dev/reference/summary.saem.mmkin.html | 677 ----------------------------- 1 file changed, 677 deletions(-) delete mode 100644 docs/dev/reference/summary.saem.mmkin.html (limited to 'docs/dev/reference/summary.saem.mmkin.html') diff --git a/docs/dev/reference/summary.saem.mmkin.html b/docs/dev/reference/summary.saem.mmkin.html deleted file mode 100644 index e434ad8d..00000000 --- a/docs/dev/reference/summary.saem.mmkin.html +++ /dev/null @@ -1,677 +0,0 @@ - -Summary method for class "saem.mmkin" — summary.saem.mmkin • mkin - - -
-
- - - -
-
-
-

Summary method for class "saem.mmkin"

- Source: R/summary.saem.mmkin.R - -
- -
-

Lists model equations, initial parameter values, optimised parameters -for fixed effects (population), random effects (deviations from the -population mean) and residual error model, as well as the resulting -endpoints such as formation fractions and DT50 values. Optionally -(default is FALSE), the data are listed in full.

-
- -
- 
# S3 method for saem.mmkin
-summary(
-  object,
-  data = FALSE,
-  verbose = FALSE,
-  covariates = NULL,
-  covariate_quantile = 0.5,
-  distimes = TRUE,
-  ...
-)
-
-# S3 method for summary.saem.mmkin
-print(x, digits = max(3, getOption("digits") - 3), verbose = x$verbose, ...)
-
- -
-

Arguments

-
object
-

an object of class saem.mmkin

- - -
data
-

logical, indicating whether the full data should be included in -the summary.

- - -
verbose
-

Should the summary be verbose?

- - -
covariates
-

Numeric vector with covariate values for all variables in -any covariate models in the object. If given, it overrides 'covariate_quantile'.

- - -
covariate_quantile
-

This argument only has an effect if the fitted -object has covariate models. If so, the default is to show endpoints -for the median of the covariate values (50th percentile).

- - -
distimes
-

logical, indicating whether DT50 and DT90 values should be -included.

- - -
...
-

optional arguments passed to methods like print.

- - -
x
-

an object of class summary.saem.mmkin

- - -
digits
-

Number of digits to use for printing

- -
-
-

Value

- - -

The summary function returns a list based on the saemix::SaemixObject

- - -

obtained in the fit, with at least the following additional components

-
saemixversion, mkinversion, Rversion
-

The saemix, mkin and R versions used

- -
date.fit, date.summary
-

The dates where the fit and the summary were -produced

- -
diffs
-

The differential equations used in the degradation model

- -
use_of_ff
-

Was maximum or minimum use made of formation fractions

- -
data
-

The data

- -
confint_trans
-

Transformed parameters as used in the optimisation, with confidence intervals

- -
confint_back
-

Backtransformed parameters, with confidence intervals if available

- -
confint_errmod
-

Error model parameters with confidence intervals

- -
ff
-

The estimated formation fractions derived from the fitted -model.

- -
distimes
-

The DT50 and DT90 values for each observed variable.

- -
SFORB
-

If applicable, eigenvalues of SFORB components of the model.

- -

The print method is called for its side effect, i.e. printing the summary.

-
-
-

Author

-

Johannes Ranke for the mkin specific parts -saemix authors for the parts inherited from saemix.

-
- -
-

Examples

- 
# Generate five datasets following DFOP-SFO kinetics
-sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
-dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "m1"),
- m1 = mkinsub("SFO"), quiet = TRUE)
-set.seed(1234)
-k1_in <- rlnorm(5, log(0.1), 0.3)
-k2_in <- rlnorm(5, log(0.02), 0.3)
-g_in <- plogis(rnorm(5, qlogis(0.5), 0.3))
-f_parent_to_m1_in <- plogis(rnorm(5, qlogis(0.3), 0.3))
-k_m1_in <- rlnorm(5, log(0.02), 0.3)
-
-pred_dfop_sfo <- function(k1, k2, g, f_parent_to_m1, k_m1) {
-  mkinpredict(dfop_sfo,
-    c(k1 = k1, k2 = k2, g = g, f_parent_to_m1 = f_parent_to_m1, k_m1 = k_m1),
-    c(parent = 100, m1 = 0),
-    sampling_times)
-}
-
-ds_mean_dfop_sfo <- lapply(1:5, function(i) {
-  mkinpredict(dfop_sfo,
-    c(k1 = k1_in[i], k2 = k2_in[i], g = g_in[i],
-      f_parent_to_m1 = f_parent_to_m1_in[i], k_m1 = k_m1_in[i]),
-    c(parent = 100, m1 = 0),
-    sampling_times)
-})
-names(ds_mean_dfop_sfo) <- paste("ds", 1:5)
-
-ds_syn_dfop_sfo <- lapply(ds_mean_dfop_sfo, function(ds) {
-  add_err(ds,
-    sdfunc = function(value) sqrt(1^2 + value^2 * 0.07^2),
-    n = 1)[[1]]
-})
-
-# \dontrun{
-# Evaluate using mmkin and saem
-f_mmkin_dfop_sfo <- mmkin(list(dfop_sfo), ds_syn_dfop_sfo,
-  quiet = TRUE, error_model = "tc", cores = 5)
-f_saem_dfop_sfo <- saem(f_mmkin_dfop_sfo)
-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_m1/dt = + f_parent_to_m1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)
-#>            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
-#>            exp(-k2 * time))) * parent - k_m1 * m1
-#> 
-#> Data:
-#> 171 observations of 2 variable(s) grouped in 5 datasets
-#> 
-#> Likelihood computed by importance sampling
-#>     AIC   BIC logLik
-#>   810.8 805.4 -391.4
-#> 
-#> Fitted parameters:
-#>                     estimate     lower     upper
-#> parent_0           100.86947  97.81542 103.92353
-#> log_k_m1            -4.06947  -4.16944  -3.96950
-#> f_parent_qlogis     -0.93256  -1.34200  -0.52312
-#> log_k1              -2.37017  -2.72660  -2.01375
-#> log_k2              -4.06264  -4.21344  -3.91184
-#> g_qlogis            -0.02174  -0.45898   0.41549
-#> a.1                  0.87598   0.67275   1.07922
-#> b.1                  0.07949   0.06389   0.09509
-#> SD.parent_0          0.19170 -30.36286  30.74626
-#> SD.log_k_m1          0.01883  -0.28736   0.32502
-#> SD.f_parent_qlogis   0.44300   0.16391   0.72209
-#> SD.log_k1            0.35320   0.09661   0.60978
-#> SD.log_k2            0.13707   0.02359   0.25056
-#> SD.g_qlogis          0.37478   0.04490   0.70467
-illparms(f_saem_dfop_sfo)
-#> [1] "sd(parent_0)" "sd(log_k_m1)"
-f_saem_dfop_sfo_2 <- update(f_saem_dfop_sfo,
-  no_random_effect = c("parent_0", "log_k_m1"))
-illparms(f_saem_dfop_sfo_2)
-intervals(f_saem_dfop_sfo_2)
-#> Approximate 95% confidence intervals
-#> 
-#>  Fixed effects:
-#>                      lower         est.        upper
-#> parent_0       98.36731429 101.42508066 104.48284703
-#> k_m1            0.01513234   0.01670094   0.01843214
-#> f_parent_to_m1  0.20221431   0.27608850   0.36461630
-#> k1              0.06915073   0.09759718   0.13774560
-#> k2              0.01487068   0.01740389   0.02036863
-#> g               0.37365671   0.48384821   0.59563299
-#> 
-#>  Random effects:
-#>                          lower      est.     upper
-#> sd(f_parent_qlogis) 0.16439770 0.4427585 0.7211193
-#> sd(log_k1)          0.08304243 0.3345213 0.5860002
-#> sd(log_k2)          0.03146410 0.1490210 0.2665779
-#> sd(g_qlogis)        0.06216385 0.4023430 0.7425221
-#> 
-#>  
-#>          lower       est.      upper
-#> a.1 0.67696663 0.87777355 1.07858048
-#> b.1 0.06363957 0.07878001 0.09392044
-summary(f_saem_dfop_sfo_2, data = TRUE)
-#> saemix version used for fitting:      3.2 
-#> mkin version used for pre-fitting:  1.2.3 
-#> R version used for fitting:         4.2.3 
-#> Date of fit:     Sun Apr 16 08:34:58 2023 
-#> Date of summary: Sun Apr 16 08:34:58 2023 
-#> 
-#> 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_m1/dt = + f_parent_to_m1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)
-#>            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
-#>            exp(-k2 * time))) * parent - k_m1 * m1
-#> 
-#> Data:
-#> 171 observations of 2 variable(s) grouped in 5 datasets
-#> 
-#> Model predictions using solution type analytical 
-#> 
-#> Fitted in 9.384 s
-#> Using 300, 100 iterations and 10 chains
-#> 
-#> Variance model: Two-component variance function 
-#> 
-#> Starting values for degradation parameters:
-#>        parent_0        log_k_m1 f_parent_qlogis          log_k1          log_k2 
-#>       101.65645        -4.05368        -0.94311        -2.35943        -4.07006 
-#>        g_qlogis 
-#>        -0.01132 
-#> 
-#> Fixed degradation parameter values:
-#> None
-#> 
-#> Starting values for random effects (square root of initial entries in omega):
-#>                 parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2 g_qlogis
-#> parent_0           6.742   0.0000          0.0000 0.0000 0.0000    0.000
-#> log_k_m1           0.000   0.2236          0.0000 0.0000 0.0000    0.000
-#> f_parent_qlogis    0.000   0.0000          0.5572 0.0000 0.0000    0.000
-#> log_k1             0.000   0.0000          0.0000 0.8031 0.0000    0.000
-#> log_k2             0.000   0.0000          0.0000 0.0000 0.2931    0.000
-#> g_qlogis           0.000   0.0000          0.0000 0.0000 0.0000    0.807
-#> 
-#> Starting values for error model parameters:
-#> a.1 b.1 
-#>   1   1 
-#> 
-#> Results:
-#> 
-#> Likelihood computed by importance sampling
-#>   AIC   BIC logLik
-#>   807 802.3 -391.5
-#> 
-#> Optimised parameters:
-#>                         est.    lower     upper
-#> parent_0           101.42508 98.36731 104.48285
-#> log_k_m1            -4.09229 -4.19092  -3.99366
-#> f_parent_qlogis     -0.96395 -1.37251  -0.55538
-#> log_k1              -2.32691 -2.67147  -1.98235
-#> log_k2              -4.05106 -4.20836  -3.89376
-#> g_qlogis            -0.06463 -0.51656   0.38730
-#> a.1                  0.87777  0.67697   1.07858
-#> b.1                  0.07878  0.06364   0.09392
-#> SD.f_parent_qlogis   0.44276  0.16440   0.72112
-#> SD.log_k1            0.33452  0.08304   0.58600
-#> SD.log_k2            0.14902  0.03146   0.26658
-#> SD.g_qlogis          0.40234  0.06216   0.74252
-#> 
-#> Correlation: 
-#>                 parnt_0 lg_k_m1 f_prnt_ log_k1  log_k2 
-#> log_k_m1        -0.4693                                
-#> f_parent_qlogis -0.2378  0.2595                        
-#> log_k1           0.1720 -0.1593 -0.0669                
-#> log_k2           0.0179  0.0594  0.0035  0.1995        
-#> g_qlogis         0.1073 -0.1060 -0.0322 -0.2299 -0.3168
-#> 
-#> Random effects:
-#>                      est.   lower  upper
-#> SD.f_parent_qlogis 0.4428 0.16440 0.7211
-#> SD.log_k1          0.3345 0.08304 0.5860
-#> SD.log_k2          0.1490 0.03146 0.2666
-#> SD.g_qlogis        0.4023 0.06216 0.7425
-#> 
-#> Variance model:
-#>        est.   lower   upper
-#> a.1 0.87777 0.67697 1.07858
-#> b.1 0.07878 0.06364 0.09392
-#> 
-#> Backtransformed parameters:
-#>                    est.    lower     upper
-#> parent_0       101.4251 98.36731 104.48285
-#> k_m1             0.0167  0.01513   0.01843
-#> f_parent_to_m1   0.2761  0.20221   0.36462
-#> k1               0.0976  0.06915   0.13775
-#> k2               0.0174  0.01487   0.02037
-#> g                0.4838  0.37366   0.59563
-#> 
-#> Resulting formation fractions:
-#>                 ff
-#> parent_m1   0.2761
-#> parent_sink 0.7239
-#> 
-#> Estimated disappearance times:
-#>         DT50   DT90 DT50back DT50_k1 DT50_k2
-#> parent 15.54  94.33     28.4   7.102   39.83
-#> m1     41.50 137.87       NA      NA      NA
-#> 
-#> Data:
-#>    ds   name time observed predicted  residual    std standardized
-#>  ds 1 parent    0     89.8 1.014e+02 -11.62508 8.0383     -1.44620
-#>  ds 1 parent    0    104.1 1.014e+02   2.67492 8.0383      0.33277
-#>  ds 1 parent    1     88.7 9.650e+01  -7.80311 7.6530     -1.01961
-#>  ds 1 parent    1     95.5 9.650e+01  -1.00311 7.6530     -0.13107
-#>  ds 1 parent    3     81.8 8.753e+01  -5.72638 6.9510     -0.82382
-#>  ds 1 parent    3     94.5 8.753e+01   6.97362 6.9510      1.00326
-#>  ds 1 parent    7     71.5 7.254e+01  -1.04133 5.7818     -0.18010
-#>  ds 1 parent    7     70.3 7.254e+01  -2.24133 5.7818     -0.38765
-#>  ds 1 parent   14     54.2 5.349e+01   0.71029 4.3044      0.16502
-#>  ds 1 parent   14     49.6 5.349e+01  -3.88971 4.3044     -0.90366
-#>  ds 1 parent   28     31.5 3.167e+01  -0.16616 2.6446     -0.06283
-#>  ds 1 parent   28     28.8 3.167e+01  -2.86616 2.6446     -1.08379
-#>  ds 1 parent   60     12.1 1.279e+01  -0.69287 1.3365     -0.51843
-#>  ds 1 parent   60     13.6 1.279e+01   0.80713 1.3365      0.60392
-#>  ds 1 parent   90      6.2 6.397e+00  -0.19718 1.0122     -0.19481
-#>  ds 1 parent   90      8.3 6.397e+00   1.90282 1.0122      1.87996
-#>  ds 1 parent  120      2.2 3.323e+00  -1.12320 0.9160     -1.22623
-#>  ds 1 parent  120      2.4 3.323e+00  -0.92320 0.9160     -1.00788
-#>  ds 1     m1    1      0.3 1.179e+00  -0.87919 0.8827     -0.99605
-#>  ds 1     m1    1      0.2 1.179e+00  -0.97919 0.8827     -1.10935
-#>  ds 1     m1    3      2.2 3.273e+00  -1.07272 0.9149     -1.17256
-#>  ds 1     m1    3      3.0 3.273e+00  -0.27272 0.9149     -0.29811
-#>  ds 1     m1    7      6.5 6.559e+00  -0.05872 1.0186     -0.05765
-#>  ds 1     m1    7      5.0 6.559e+00  -1.55872 1.0186     -1.53032
-#>  ds 1     m1   14     10.2 1.016e+01   0.03787 1.1880      0.03188
-#>  ds 1     m1   14      9.5 1.016e+01  -0.66213 1.1880     -0.55734
-#>  ds 1     m1   28     12.2 1.268e+01  -0.47913 1.3297     -0.36032
-#>  ds 1     m1   28     13.4 1.268e+01   0.72087 1.3297      0.54211
-#>  ds 1     m1   60     11.8 1.078e+01   1.02493 1.2211      0.83936
-#>  ds 1     m1   60     13.2 1.078e+01   2.42493 1.2211      1.98588
-#>  ds 1     m1   90      6.6 7.705e+00  -1.10464 1.0672     -1.03509
-#>  ds 1     m1   90      9.3 7.705e+00   1.59536 1.0672      1.49491
-#>  ds 1     m1  120      3.5 5.236e+00  -1.73617 0.9699     -1.79010
-#>  ds 1     m1  120      5.4 5.236e+00   0.16383 0.9699      0.16892
-#>  ds 2 parent    0    118.0 1.014e+02  16.57492 8.0383      2.06198
-#>  ds 2 parent    0     99.8 1.014e+02  -1.62508 8.0383     -0.20217
-#>  ds 2 parent    1     90.2 9.599e+01  -5.79045 7.6129     -0.76061
-#>  ds 2 parent    1     94.6 9.599e+01  -1.39045 7.6129     -0.18264
-#>  ds 2 parent    3     96.1 8.652e+01   9.57931 6.8724      1.39388
-#>  ds 2 parent    3     78.4 8.652e+01  -8.12069 6.8724     -1.18164
-#>  ds 2 parent    7     77.9 7.197e+01   5.93429 5.7370      1.03439
-#>  ds 2 parent    7     77.7 7.197e+01   5.73429 5.7370      0.99953
-#>  ds 2 parent   14     56.0 5.555e+01   0.44657 4.4637      0.10005
-#>  ds 2 parent   14     54.7 5.555e+01  -0.85343 4.4637     -0.19120
-#>  ds 2 parent   28     36.6 3.853e+01  -1.93170 3.1599     -0.61132
-#>  ds 2 parent   28     36.8 3.853e+01  -1.73170 3.1599     -0.54803
-#>  ds 2 parent   60     22.1 2.110e+01   1.00360 1.8795      0.53396
-#>  ds 2 parent   60     24.7 2.110e+01   3.60360 1.8795      1.91728
-#>  ds 2 parent   90     12.4 1.250e+01  -0.09712 1.3190     -0.07363
-#>  ds 2 parent   90     10.8 1.250e+01  -1.69712 1.3190     -1.28667
-#>  ds 2 parent  120      6.8 7.419e+00  -0.61913 1.0546     -0.58709
-#>  ds 2 parent  120      7.9 7.419e+00   0.48087 1.0546      0.45599
-#>  ds 2     m1    1      1.3 1.422e+00  -0.12194 0.8849     -0.13781
-#>  ds 2     m1    3      3.7 3.831e+00  -0.13149 0.9282     -0.14166
-#>  ds 2     m1    3      4.7 3.831e+00   0.86851 0.9282      0.93567
-#>  ds 2     m1    7      8.1 7.292e+00   0.80812 1.0490      0.77034
-#>  ds 2     m1    7      7.9 7.292e+00   0.60812 1.0490      0.57969
-#>  ds 2     m1   14     10.1 1.055e+01  -0.45332 1.2090     -0.37495
-#>  ds 2     m1   14     10.3 1.055e+01  -0.25332 1.2090     -0.20953
-#>  ds 2     m1   28     10.7 1.230e+01  -1.59960 1.3074     -1.22347
-#>  ds 2     m1   28     12.2 1.230e+01  -0.09960 1.3074     -0.07618
-#>  ds 2     m1   60     10.7 1.065e+01   0.05342 1.2141      0.04400
-#>  ds 2     m1   60     12.5 1.065e+01   1.85342 1.2141      1.52661
-#>  ds 2     m1   90      9.1 8.196e+00   0.90368 1.0897      0.82930
-#>  ds 2     m1   90      7.4 8.196e+00  -0.79632 1.0897     -0.73078
-#>  ds 2     m1  120      6.1 5.997e+00   0.10252 0.9969      0.10284
-#>  ds 2     m1  120      4.5 5.997e+00  -1.49748 0.9969     -1.50220
-#>  ds 3 parent    0    106.2 1.014e+02   4.77492 8.0383      0.59402
-#>  ds 3 parent    0    106.9 1.014e+02   5.47492 8.0383      0.68110
-#>  ds 3 parent    1    107.4 9.390e+01  13.49935 7.4494      1.81214
-#>  ds 3 parent    1     96.1 9.390e+01   2.19935 7.4494      0.29524
-#>  ds 3 parent    3     79.4 8.152e+01  -2.12307 6.4821     -0.32753
-#>  ds 3 parent    3     82.6 8.152e+01   1.07693 6.4821      0.16614
-#>  ds 3 parent    7     63.9 6.446e+01  -0.55834 5.1533     -0.10834
-#>  ds 3 parent    7     62.4 6.446e+01  -2.05834 5.1533     -0.39942
-#>  ds 3 parent   14     51.0 4.826e+01   2.74073 3.9019      0.70241
-#>  ds 3 parent   14     47.1 4.826e+01  -1.15927 3.9019     -0.29711
-#>  ds 3 parent   28     36.1 3.424e+01   1.86399 2.8364      0.65718
-#>  ds 3 parent   28     36.6 3.424e+01   2.36399 2.8364      0.83346
-#>  ds 3 parent   60     20.1 1.968e+01   0.42172 1.7815      0.23672
-#>  ds 3 parent   60     19.8 1.968e+01   0.12172 1.7815      0.06833
-#>  ds 3 parent   90     11.3 1.195e+01  -0.64633 1.2869     -0.50222
-#>  ds 3 parent   90     10.7 1.195e+01  -1.24633 1.2869     -0.96844
-#>  ds 3 parent  120      8.2 7.255e+00   0.94532 1.0474      0.90251
-#>  ds 3 parent  120      7.3 7.255e+00   0.04532 1.0474      0.04327
-#>  ds 3     m1    0      0.8 2.956e-11   0.80000 0.8778      0.91140
-#>  ds 3     m1    1      1.8 1.758e+00   0.04187 0.8886      0.04712
-#>  ds 3     m1    1      2.3 1.758e+00   0.54187 0.8886      0.60978
-#>  ds 3     m1    3      4.2 4.567e+00  -0.36697 0.9486     -0.38683
-#>  ds 3     m1    3      4.1 4.567e+00  -0.46697 0.9486     -0.49224
-#>  ds 3     m1    7      6.8 8.151e+00  -1.35124 1.0876     -1.24242
-#>  ds 3     m1    7     10.1 8.151e+00   1.94876 1.0876      1.79182
-#>  ds 3     m1   14     11.4 1.083e+01   0.57098 1.2240      0.46647
-#>  ds 3     m1   14     12.8 1.083e+01   1.97098 1.2240      1.61022
-#>  ds 3     m1   28     11.5 1.147e+01   0.03175 1.2597      0.02520
-#>  ds 3     m1   28     10.6 1.147e+01  -0.86825 1.2597     -0.68928
-#>  ds 3     m1   60      7.5 9.298e+00  -1.79834 1.1433     -1.57298
-#>  ds 3     m1   60      8.6 9.298e+00  -0.69834 1.1433     -0.61083
-#>  ds 3     m1   90      7.3 7.038e+00   0.26249 1.0382      0.25283
-#>  ds 3     m1   90      8.1 7.038e+00   1.06249 1.0382      1.02340
-#>  ds 3     m1  120      5.3 5.116e+00   0.18417 0.9659      0.19068
-#>  ds 3     m1  120      3.8 5.116e+00  -1.31583 0.9659     -1.36232
-#>  ds 4 parent    0    104.7 1.014e+02   3.27492 8.0383      0.40741
-#>  ds 4 parent    0     88.3 1.014e+02 -13.12508 8.0383     -1.63281
-#>  ds 4 parent    1     94.2 9.781e+01  -3.61183 7.7555     -0.46572
-#>  ds 4 parent    1     94.6 9.781e+01  -3.21183 7.7555     -0.41414
-#>  ds 4 parent    3     78.1 9.110e+01 -13.00467 7.2307     -1.79853
-#>  ds 4 parent    3     96.5 9.110e+01   5.39533 7.2307      0.74617
-#>  ds 4 parent    7     76.2 7.951e+01  -3.30511 6.3246     -0.52258
-#>  ds 4 parent    7     77.8 7.951e+01  -1.70511 6.3246     -0.26960
-#>  ds 4 parent   14     70.8 6.376e+01   7.03783 5.0993      1.38016
-#>  ds 4 parent   14     67.3 6.376e+01   3.53783 5.0993      0.69379
-#>  ds 4 parent   28     43.1 4.340e+01  -0.30456 3.5303     -0.08627
-#>  ds 4 parent   28     45.1 4.340e+01   1.69544 3.5303      0.48026
-#>  ds 4 parent   60     21.3 2.142e+01  -0.12077 1.9022     -0.06349
-#>  ds 4 parent   60     23.5 2.142e+01   2.07923 1.9022      1.09308
-#>  ds 4 parent   90     11.8 1.207e+01  -0.26813 1.2940     -0.20721
-#>  ds 4 parent   90     12.1 1.207e+01   0.03187 1.2940      0.02463
-#>  ds 4 parent  120      7.0 6.954e+00   0.04554 1.0347      0.04402
-#>  ds 4 parent  120      6.2 6.954e+00  -0.75446 1.0347     -0.72914
-#>  ds 4     m1    0      1.6 1.990e-13   1.60000 0.8778      1.82279
-#>  ds 4     m1    1      0.9 7.305e-01   0.16949 0.8797      0.19267
-#>  ds 4     m1    3      3.7 2.051e+00   1.64896 0.8925      1.84753
-#>  ds 4     m1    3      2.0 2.051e+00  -0.05104 0.8925     -0.05719
-#>  ds 4     m1    7      3.6 4.204e+00  -0.60375 0.9382     -0.64354
-#>  ds 4     m1    7      3.8 4.204e+00  -0.40375 0.9382     -0.43036
-#>  ds 4     m1   14      7.1 6.760e+00   0.34021 1.0267      0.33137
-#>  ds 4     m1   14      6.6 6.760e+00  -0.15979 1.0267     -0.15563
-#>  ds 4     m1   28      9.5 9.011e+00   0.48856 1.1289      0.43277
-#>  ds 4     m1   28      9.3 9.011e+00   0.28856 1.1289      0.25561
-#>  ds 4     m1   60      8.3 8.611e+00  -0.31077 1.1093     -0.28014
-#>  ds 4     m1   60      9.0 8.611e+00   0.38923 1.1093      0.35086
-#>  ds 4     m1   90      6.6 6.678e+00  -0.07753 1.0233     -0.07576
-#>  ds 4     m1   90      7.7 6.678e+00   1.02247 1.0233      0.99915
-#>  ds 4     m1  120      3.7 4.847e+00  -1.14679 0.9572     -1.19804
-#>  ds 4     m1  120      3.5 4.847e+00  -1.34679 0.9572     -1.40698
-#>  ds 5 parent    0    110.4 1.014e+02   8.97492 8.0383      1.11651
-#>  ds 5 parent    0    112.1 1.014e+02  10.67492 8.0383      1.32800
-#>  ds 5 parent    1     93.5 9.466e+01  -1.16118 7.5089     -0.15464
-#>  ds 5 parent    1     91.0 9.466e+01  -3.66118 7.5089     -0.48758
-#>  ds 5 parent    3     71.0 8.302e+01 -12.01844 6.5988     -1.82130
-#>  ds 5 parent    3     89.7 8.302e+01   6.68156 6.5988      1.01254
-#>  ds 5 parent    7     60.4 6.563e+01  -5.22574 5.2440     -0.99652
-#>  ds 5 parent    7     59.1 6.563e+01  -6.52574 5.2440     -1.24442
-#>  ds 5 parent   14     56.5 4.727e+01   9.22621 3.8263      2.41128
-#>  ds 5 parent   14     47.0 4.727e+01  -0.27379 3.8263     -0.07156
-#>  ds 5 parent   28     30.2 3.103e+01  -0.83405 2.5977     -0.32108
-#>  ds 5 parent   28     23.9 3.103e+01  -7.13405 2.5977     -2.74634
-#>  ds 5 parent   60     17.0 1.800e+01  -0.99696 1.6675     -0.59787
-#>  ds 5 parent   60     18.7 1.800e+01   0.70304 1.6675      0.42161
-#>  ds 5 parent   90     11.3 1.167e+01  -0.36809 1.2710     -0.28961
-#>  ds 5 parent   90     11.9 1.167e+01   0.23191 1.2710      0.18246
-#>  ds 5 parent  120      9.0 7.595e+00   1.40496 1.0623      1.32256
-#>  ds 5 parent  120      8.1 7.595e+00   0.50496 1.0623      0.47535
-#>  ds 5     m1    0      0.7 0.000e+00   0.70000 0.8778      0.79747
-#>  ds 5     m1    1      3.0 3.158e+00  -0.15799 0.9123     -0.17317
-#>  ds 5     m1    1      2.6 3.158e+00  -0.55799 0.9123     -0.61160
-#>  ds 5     m1    3      5.1 8.443e+00  -3.34286 1.1013     -3.03535
-#>  ds 5     m1    3      7.5 8.443e+00  -0.94286 1.1013     -0.85613
-#>  ds 5     m1    7     16.5 1.580e+01   0.69781 1.5232      0.45811
-#>  ds 5     m1    7     19.0 1.580e+01   3.19781 1.5232      2.09935
-#>  ds 5     m1   14     22.9 2.216e+01   0.73604 1.9543      0.37663
-#>  ds 5     m1   14     23.2 2.216e+01   1.03604 1.9543      0.53014
-#>  ds 5     m1   28     22.2 2.423e+01  -2.03128 2.1011     -0.96678
-#>  ds 5     m1   28     24.4 2.423e+01   0.16872 2.1011      0.08030
-#>  ds 5     m1   60     15.5 1.876e+01  -3.25610 1.7187     -1.89455
-#>  ds 5     m1   60     19.8 1.876e+01   1.04390 1.7187      0.60739
-#>  ds 5     m1   90     14.9 1.366e+01   1.23585 1.3890      0.88976
-#>  ds 5     m1   90     14.2 1.366e+01   0.53585 1.3890      0.38579
-#>  ds 5     m1  120     10.9 9.761e+00   1.13911 1.1670      0.97613
-#>  ds 5     m1  120     10.4 9.761e+00   0.63911 1.1670      0.54767
-# Add a correlation between random effects of g and k2
-cov_model_3 <- f_saem_dfop_sfo_2$so@model@covariance.model
-cov_model_3["log_k2", "g_qlogis"] <- 1
-cov_model_3["g_qlogis", "log_k2"] <- 1
-f_saem_dfop_sfo_3 <- update(f_saem_dfop_sfo,
-  covariance.model = cov_model_3)
-intervals(f_saem_dfop_sfo_3)
-#> Approximate 95% confidence intervals
-#> 
-#>  Fixed effects:
-#>                      lower         est.        upper
-#> parent_0       98.39888363 101.48951337 104.58014311
-#> k_m1            0.01508704   0.01665986   0.01839665
-#> f_parent_to_m1  0.20141557   0.27540583   0.36418131
-#> k1              0.07708759   0.10430866   0.14114200
-#> k2              0.01476621   0.01786384   0.02161129
-#> g               0.33679867   0.45083525   0.57028162
-#> 
-#>  Random effects:
-#>                             lower       est.      upper
-#> sd(f_parent_qlogis)    0.38085375  0.4441841  0.5075145
-#> sd(log_k1)             0.04774819  0.2660384  0.4843286
-#> sd(log_k2)            -0.63842736  0.1977024  1.0338321
-#> sd(g_qlogis)           0.22711289  0.4502227  0.6733326
-#> corr(log_k2,g_qlogis) -0.83271473 -0.6176939 -0.4026730
-#> 
-#>  
-#>          lower       est.      upper
-#> a.1 0.67347568 0.87437392 1.07527216
-#> b.1 0.06393032 0.07912417 0.09431802
-# The correlation does not improve the fit judged by AIC and BIC, although
-# the likelihood is higher with the additional parameter
-anova(f_saem_dfop_sfo, f_saem_dfop_sfo_2, f_saem_dfop_sfo_3)
-#> Data: 171 observations of 2 variable(s) grouped in 5 datasets
-#> 
-#>                   npar    AIC    BIC     Lik
-#> f_saem_dfop_sfo_2   12 806.96 802.27 -391.48
-#> f_saem_dfop_sfo_3   13 807.99 802.91 -391.00
-#> f_saem_dfop_sfo     14 810.83 805.36 -391.42
-# }
-
-
-
-
- -
- - -
- - - - - - - - -- cgit v1.2.1