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pkbuild

Overview

Description

Fit several structural PK models and select the best one based on a corrected Bayesian Information Criterion for mixed effects models.

Models to compare can be defined by rate constants and/or clearances and can include or not nonlinear elimination models.

Usage

CODE
pkbuild <- function(data=NULL, project=NULL, stat=FALSE, param="clearance", new.dir=".", 
                    MM=FALSE, linearization=T, criterion="BICc", level=NULL, settings.stat=NULL) 

Arguments

data

a list with fields

  • dataFile: path of a formatted data file

  • headerTypes: a vector of strings

  • administration ("iv", "bolus", "infusion", "oral", "ev"): route of administration

project

a Monolix project

stat

(FALSE, TRUE): the statistical model is also built (using buildmlx) (default=FALSE)

param

("clearance", "rate", "both): parametrization (default="clearance")

new.dir

name of the directory where the created files are stored (default is the current working directory) )

MM

(FALSE, TRUE): tested models include or not Michaelis Menten elimination models (default=FALSE)

linearization

TRUE/FALSE whether the computation of the likelihood is based on a linearization of the model (default=FALSE)

criterion

penalization criterion to optimize c("AIC", "BIC", "BICc", gamma) (default="BICc")

level

an integer between 1 and 9 (used by setSettings)

settings.stat

list of settings used by buildmlx (only if stat=TRUE)

Examples

Reading a data file

Let us use the warfarin PK data in this example:

R
head(read.csv('data/warfarinPK.csv'))
R
##   id time   y amount   wt age sex
## 1  1    0   .    100 66.7  50   1
## 2  1   24 9.2      . 66.7  50   1
## 3  1   36 8.5      . 66.7  50   1
## 4  1   48 6.4      . 66.7  50   1
## 5  1   72 4.8      . 66.7  50   1
## 6  1   96 3.1      . 66.7  50   1

warfarin is administrated orally:

R
library(Rsmlx)
warfarinPK <- list(
  dataFile = "data/warfarinPK.csv",
  headerTypes = c("id", "time", "observation", "amount", "contcov", "contcov", "catcov"),
  administration = "oral")

By default, PK models parameterized with clearance(s) are fitted.

R
warf.pk1a <- pkbuild(data=warfarinPK,  new.dir="warfarinPK")
R
## 
## [1] "warfarinPK/pk_kaVCl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##   875.0286541   891.0286541   902.7545413   912.9728035     0.0467466 
## 
## [1] "warfarinPK/pk_Tk0VCl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##  796.97249564  812.97249564  824.69838286  834.91664503    0.04163542 
## 
## [1] "warfarinPK/pk_TlagkaVCl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##  661.92854178  681.92854178  696.58590081  708.84781541    0.09644021 
## 
## [1] "warfarinPK/pk_TlagTk0VCl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##   657.4535554   677.4535554   692.1109144   704.3728291     0.1275872 
## 
## [1] "warfarinPK/pk_TlagTk0V1V2QCl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##   641.5775866   669.5775866   690.0978893   706.4471087     0.2516801 
## 
## [1] "warfarinPK/pk_TlagkaV1V2QCl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##   640.9399316   668.9399316   689.4602342   705.8094537     0.2841378

By default, comparison of these models is based on a corrected Bayesian Information Criterion (BICc) for mixed effects models:

where N is the number of individuals and n the total number of observations. Then, is the number of parameters associated to the variability of the individual parameters and the number of parameters associated to the variability of the observations.

According to this criterion, the best PK model for this data is oral0_1cpt_TlagTk0VCl.txt.

R
print(warf.pk1a)
R
## $pop.ini
##      Tlag       Tk0         V        Cl 
## 0.9094114 2.9142311 7.9241581 0.1320154 
## 
## $project
## [1] "warfarinPK/pk_TlagTk0VCl.mlxtran"
## 
## $model
## [1] "lib:oral0_1cpt_TlagTk0VCl.txt"
## 
## $data
## $data$dataFile
## [1] "data/warfarinPK.csv"
## 
## $data$headerTypes
## [1] "id"          "time"        "observation" "amount"      "contcov"    
## [6] "contcov"     "catcov"     
## 
## $data$administration
## [1] "oral"
## 
## 
## $ofv
## [1] 657.4536
## 
## $bicc
## [1] 704.3728
## 
## $bic
## [1] 692.1109
## 
## $aic
## [1] 677.4536
## 
## $pop.est
##   Tlag_pop    Tk0_pop      V_pop     Cl_pop omega_Tlag  omega_Tk0    omega_V 
## 0.78774241 1.47936633 7.99749648 0.13202040 0.61408374 0.56316740 0.22380612 
##   omega_Cl          a          b 
## 0.29199097 0.33300067 0.07104876 
## 
## $res
##                               model      OFV      AIC      BIC     BICc
## 1     lib:oral0_1cpt_TlagTk0VCl.txt 657.4536 677.4536 692.1109 704.3728
## 2  lib:oral1_2cpt_TlagkaClV1QV2.txt 640.9399 668.9399 689.4602 705.8095
## 3 lib:oral0_2cpt_TlagTk0ClV1QV2.txt 641.5776 669.5776 690.0979 706.4471
## 4      lib:oral1_1cpt_TlagkaVCl.txt 661.9285 681.9285 696.5859 708.8478
## 5         lib:oral0_1cpt_Tk0VCl.txt 796.9725 812.9725 824.6984 834.9166
## 6          lib:oral1_1cpt_kaVCl.txt 875.0287 891.0287 902.7545 912.9728

Another critrion, such as AIC or BIC can be used instead of BICc. Computation of the likelihood can also be based on a linearization of the model (much faster than Importance Sampling):

R
warf.pk1b <- pkbuild(data=warfarinPK,  new.dir="warfarinPK", linearization=T, criterion="AIC")
R
## 
## [1] "warfarinPK/pk_kaVCl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##      OFV      AIC      BIC     BICc 
## 866.8906 882.8906 894.6165 904.8348 
## 
## [1] "warfarinPK/pk_Tk0VCl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##      OFV      AIC      BIC     BICc 
## 796.7175 812.7175 824.4434 834.6617 
## 
## [1] "warfarinPK/pk_TlagkaVCl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##      OFV      AIC      BIC     BICc 
## 658.7242 678.7242 693.3816 705.6435 
## 
## [1] "warfarinPK/pk_TlagTk0VCl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##      OFV      AIC      BIC     BICc 
## 656.8035 676.8035 691.4609 703.7228 
## 
## [1] "warfarinPK/pk_TlagTk0V1V2QCl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##      OFV      AIC      BIC     BICc 
## 647.8176 675.8176 696.3379 712.6871 
## 
## [1] "warfarinPK/pk_TlagkaV1V2QCl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##      OFV      AIC      BIC     BICc 
## 638.7796 666.7796 687.2999 703.6491 
## 
## [1] "warfarinPK/pk_TlagkaV1V2V3Q2Q3Cl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##      OFV      AIC      BIC     BICc 
## 639.3326 675.3326 701.7159 722.1524
R
print(warf.pk1b)
R
## $pop.ini
##        Tlag          ka          V1          V2           Q          Cl 
##  0.80651432  1.68633271  7.80325033 41.24319306  0.02736140  0.09860742 
## 
## $project
## [1] "warfarinPK/pk_TlagkaV1V2QCl.mlxtran"
## 
## $model
## [1] "lib:oral1_2cpt_TlagkaClV1QV2.txt"
## 
## $data
## $data$dataFile
## [1] "data/warfarinPK.csv"
## 
## $data$headerTypes
## [1] "id"          "time"        "observation" "amount"      "contcov"    
## [6] "contcov"     "catcov"     
## 
## $data$administration
## [1] "oral"
## 
## 
## $ofv
## [1] 638.7796
## 
## $bicc
## [1] 703.6491
## 
## $bic
## [1] 687.2999
## 
## $aic
## [1] 666.7796
## 
## $pop.est
##    Tlag_pop      ka_pop      Cl_pop      V1_pop       Q_pop      V2_pop 
##  0.86822311  1.23542836  0.06098586  7.63926798  0.07441126 69.16438571 
##  omega_Tlag    omega_ka    omega_Cl    omega_V1     omega_Q    omega_V2 
##  0.53251214  0.64444931  0.17806529  0.22095336  0.56811834  0.85552363 
##           a           b 
##  0.30210520  0.06647686 
## 
## $res
##                                   model      OFV      AIC      BIC     BICc
## 1      lib:oral1_2cpt_TlagkaClV1QV2.txt 638.7796 666.7796 687.2999 703.6491
## 2 lib:oral1_3cpt_TlagkaClV1Q2V2Q3V3.txt 639.3326 675.3326 701.7159 722.1524
## 3     lib:oral0_2cpt_TlagTk0ClV1QV2.txt 647.8176 675.8176 696.3379 712.6871
## 4         lib:oral0_1cpt_TlagTk0VCl.txt 656.8035 676.8035 691.4609 703.7228
## 5          lib:oral1_1cpt_TlagkaVCl.txt 658.7242 678.7242 693.3816 705.6435
## 6             lib:oral0_1cpt_Tk0VCl.txt 796.7175 812.7175 824.4434 834.6617
## 7              lib:oral1_1cpt_kaVCl.txt 866.8906 882.8906 894.6165 904.8348

Models parameterized with rate constants can be used instead of clearances:

R
warf.pk2 <- pkbuild(data=warfarinPK,  new.dir="warfarinPK", param="rate")
R
## 
## [1] "warfarinPK/pk_kaVk.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##  874.42902727  890.42902727  902.15491449  912.37317666    0.07974211 
## 
## [1] "warfarinPK/pk_Tk0Vk.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##  792.68073394  808.68073394  820.40662116  830.62488333    0.08323628 
## 
## [1] "warfarinPK/pk_TlagkaVk.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##   660.7850613   680.7850613   695.4424203   707.7043349     0.1203258 
## 
## [1] "warfarinPK/pk_TlagTk0Vk.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##   654.3904192   674.3904192   689.0477782   701.3096928     0.1886907 
## 
## [1] "warfarinPK/pk_TlagTk0Vk12k21k.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##   626.3613590   654.3613590   674.8816616   691.2308811     0.2238111 
## 
## [1] "warfarinPK/pk_TlagkaVk12k21k.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##     628.55224     656.55224     677.07254     693.42176       0.26789 
## 
## [1] "warfarinPK/pk_TlagTk0Vk12k21k13k31k.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##   632.3695506   668.3695506   694.7527969   715.1893212     0.5020241
R
print(warf.pk2$res)
R
##                                      model      OFV      AIC      BIC     BICc
## 1       lib:oral0_2cpt_TlagTk0Vkk12k21.txt 626.3614 654.3614 674.8817 691.2309
## 2        lib:oral1_2cpt_TlagkaVkk12k21.txt 628.5522 656.5522 677.0725 693.4218
## 3             lib:oral0_1cpt_TlagTk0Vk.txt 654.3904 674.3904 689.0478 701.3097
## 4              lib:oral1_1cpt_TlagkaVk.txt 660.7851 680.7851 695.4424 707.7043
## 5 lib:oral0_3cpt_TlagTk0Vkk12k21k13k31.txt 632.3696 668.3696 694.7528 715.1893
## 6                 lib:oral0_1cpt_Tk0Vk.txt 792.6807 808.6807 820.4066 830.6249
## 7                  lib:oral1_1cpt_kaVk.txt 874.4290 890.4290 902.1549 912.3732
R
print(warf.pk2$pop.est)
R
##    Tlag_pop     Tk0_pop       V_pop       k_pop     k12_pop     k21_pop 
## 0.787927547 1.684833934 7.412555798 0.015276566 0.007026264 0.035824779 
##  omega_Tlag   omega_Tk0     omega_V     omega_k   omega_k12   omega_k21 
## 0.503209926 0.519285248 0.193032922 0.083388055 0.809503136 2.526785135 
##           a           b 
## 0.283076376 0.071405473

or both:

R
warf.pk3 <- pkbuild(data=warfarinPK,  new.dir="warfarinPK", param="both")
R
## 
## [1] "warfarinPK/pk_kaVCl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##   875.0286541   891.0286541   902.7545413   912.9728035     0.0467466 
## 
## [1] "warfarinPK/pk_Tk0VCl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##  796.97249564  812.97249564  824.69838286  834.91664503    0.04163542 
## 
## [1] "warfarinPK/pk_TlagkaVCl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##  661.92854178  681.92854178  696.58590081  708.84781541    0.09644021 
## 
## [1] "warfarinPK/pk_TlagTk0VCl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##   657.4535554   677.4535554   692.1109144   704.3728291     0.1275872 
## 
## [1] "warfarinPK/pk_TlagTk0V1V2QCl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##   641.5775866   669.5775866   690.0978893   706.4471087     0.2516801 
## 
## [1] "warfarinPK/pk_TlagkaV1V2QCl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##   640.9399316   668.9399316   689.4602342   705.8094537     0.2841378 
## 
## [1] "warfarinPK/pk_kaVk.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##  874.42902727  890.42902727  902.15491449  912.37317666    0.07974211 
## 
## [1] "warfarinPK/pk_Tk0Vk.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##  792.68073394  808.68073394  820.40662116  830.62488333    0.08323628 
## 
## [1] "warfarinPK/pk_TlagkaVk.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##   660.7850613   680.7850613   695.4424203   707.7043349     0.1203258 
## 
## [1] "warfarinPK/pk_TlagTk0Vk.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##   654.3904192   674.3904192   689.0477782   701.3096928     0.1886907 
## 
## [1] "warfarinPK/pk_TlagTk0Vk12k21k.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##   626.3613590   654.3613590   674.8816616   691.2308811     0.2238111 
## 
## [1] "warfarinPK/pk_TlagkaVk12k21k.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##     628.55224     656.55224     677.07254     693.42176       0.26789 
## 
## [1] "warfarinPK/pk_TlagTk0Vk12k21k13k31k.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##   632.3695506   668.3695506   694.7527969   715.1893212     0.5020241
R
print(warf.pk3$res)
R
##                                       model      OFV      AIC      BIC     BICc
## 1        lib:oral0_2cpt_TlagTk0Vkk12k21.txt 626.3614 654.3614 674.8817 691.2309
## 2         lib:oral1_2cpt_TlagkaVkk12k21.txt 628.5522 656.5522 677.0725 693.4218
## 3              lib:oral0_1cpt_TlagTk0Vk.txt 654.3904 674.3904 689.0478 701.3097
## 4             lib:oral0_1cpt_TlagTk0VCl.txt 657.4536 677.4536 692.1109 704.3728
## 5          lib:oral1_2cpt_TlagkaClV1QV2.txt 640.9399 668.9399 689.4602 705.8095
## 6         lib:oral0_2cpt_TlagTk0ClV1QV2.txt 641.5776 669.5776 690.0979 706.4471
## 7               lib:oral1_1cpt_TlagkaVk.txt 660.7851 680.7851 695.4424 707.7043
## 8              lib:oral1_1cpt_TlagkaVCl.txt 661.9285 681.9285 696.5859 708.8478
## 9  lib:oral0_3cpt_TlagTk0Vkk12k21k13k31.txt 632.3696 668.3696 694.7528 715.1893
## 10                 lib:oral0_1cpt_Tk0Vk.txt 792.6807 808.6807 820.4066 830.6249
## 11                lib:oral0_1cpt_Tk0VCl.txt 796.9725 812.9725 824.6984 834.9166
## 12                  lib:oral1_1cpt_kaVk.txt 874.4290 890.4290 902.1549 912.3732
## 13                 lib:oral1_1cpt_kaVCl.txt 875.0287 891.0287 902.7545 912.9728

Using a Monolix project

If a Monolix project has been already created using the data file, it can be used for providing data information

R
warf.pk4  <- pkbuild(project="projects/warfarinPK.mlxtran", new.dir="warfarinPK")
R
## 
## [1] "warfarinPK/pk_kaVCl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##  850.88080099  866.88080099  878.60668821  888.82495038    0.05221115 
## 
## [1] "warfarinPK/pk_Tk0VCl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##  772.83071891  788.83071891  800.55660613  810.77486830    0.04437503 
## 
## [1] "warfarinPK/pk_TlagkaVCl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##   650.9798730   670.9798730   685.6372320   697.8991466     0.1419425 
## 
## [1] "warfarinPK/pk_TlagTk0VCl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##    648.838170    668.838170    683.495529    695.757444      0.126884 
## 
## [1] "warfarinPK/pk_TlagTk0V1V2QCl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##   630.7504153   658.7504153   679.2707179   695.6199374     0.4185244 
## 
## [1] "warfarinPK/pk_TlagkaV1V2QCl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##   626.3730062   654.3730062   674.8933088   691.2425283     0.3712965 
## 
## [1] "warfarinPK/pk_TlagkaV1V2V3Q2Q3Cl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##   625.4464458   661.4464458   687.8296920   708.2662164     0.4318463

Of course, the results are the same as those obtained using the data file

R
print(warf.pk4$res)
R
##                                   model      OFV      AIC      BIC     BICc
## 1      lib:oral1_2cpt_TlagkaClV1QV2.txt 626.3730 654.3730 674.8933 691.2425
## 2     lib:oral0_2cpt_TlagTk0ClV1QV2.txt 630.7504 658.7504 679.2707 695.6199
## 3         lib:oral0_1cpt_TlagTk0VCl.txt 648.8382 668.8382 683.4955 695.7574
## 4          lib:oral1_1cpt_TlagkaVCl.txt 650.9799 670.9799 685.6372 697.8991
## 5 lib:oral1_3cpt_TlagkaClV1Q2V2Q3V3.txt 625.4464 661.4464 687.8297 708.2662
## 6             lib:oral0_1cpt_Tk0VCl.txt 772.8307 788.8307 800.5566 810.7749
## 7              lib:oral1_1cpt_kaVCl.txt 850.8808 866.8808 878.6067 888.8250

Building the statistical model also

The “best” PK model is selected first. Then, the “best” statistical model is built using buildAll:

R
warf.pk5  <- pkbuild(data=warfarinPK, stat=TRUE, new.dir="warfarinPK", param="rate")
R
## 
## [1] "warfarinPK/pk_kaVk.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##  874.42902727  890.42902727  902.15491449  912.37317666    0.07974211 
## 
## [1] "warfarinPK/pk_Tk0Vk.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##  792.68073394  808.68073394  820.40662116  830.62488333    0.08323628 
## 
## [1] "warfarinPK/pk_TlagkaVk.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##   660.7850613   680.7850613   695.4424203   707.7043349     0.1203258 
## 
## [1] "warfarinPK/pk_TlagTk0Vk.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##   654.3904192   674.3904192   689.0477782   701.3096928     0.1886907 
## 
## [1] "warfarinPK/pk_TlagTk0Vk12k21k.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##   626.3613590   654.3613590   674.8816616   691.2308811     0.2238111 
## 
## [1] "warfarinPK/pk_TlagkaVk12k21k.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##     628.55224     656.55224     677.07254     693.42176       0.26789 
## 
## [1] "warfarinPK/pk_TlagTk0Vk12k21k13k31k.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##           OFV           AIC           BIC          BICc standardError 
##   632.3695506   668.3695506   694.7527969   715.1893212     0.5020241 
## 
## --------------------------------------------------
## 
## Building:
##    -  The covariate model
##    -  The correlation model
##    -  The residual error model
##  
## __________________________________________________
## - - - Initialization - - -
## 
## Covariate model:
##      sex age wt
## Tlag   0   0  0
## Tk0    0   0  0
## V      0   0  0
## k      0   0  0
## k12    0   0  0
## k21    0   0  0
## 
## Correlation model:
## [1] "NULL"
## 
## Residual error model:
##           y 
## "combined2" 
## Sampling of the conditional distribution using the initial model ... 
## 
## Estimated criteria (importanceSampling):
##    AIC    BIC   BICc   s.e. 
## 654.36 674.88 691.23   0.22 
## __________________________________________________
## - - - Iteration 1 - - -
## 
## Covariate model:
##      sex age wt
## Tlag   0   0  0
## Tk0    0   0  0
## V      1   0  1
## k      0   0  0
## k12    0   0  0
## k21    0   1  0
## 
## Correlation model:
## [1] "NULL"
## 
## Residual error model:
##           y 
## "combined2" 
## 
## Run scenario for model 1 ... 
## Estimation of the population parameters... 
## Sampling from the conditional distribution... 
## Estimation of the log-likelihood... 
## 
## Estimated criteria (importanceSampling):
##    AIC    BIC   BICc   s.e. 
## 634.70 659.62 675.97   0.25 
## __________________________________________________
## - - - Iteration 2 - - -
## 
## Covariate model:
##      sex age wt
## Tlag   0   0  0
## Tk0    0   0  0
## V      0   0  1
## k      0   0  0
## k12    0   0  0
## k21    0   1  0
## 
## Correlation model:
## [1] "NULL"
## 
## Residual error model:
##           y 
## "combined2" 
## 
## Run scenario for model 2 ... 
## Estimation of the population parameters... 
## Sampling from the conditional distribution... 
## Estimation of the log-likelihood... 
## 
## Estimated criteria (importanceSampling):
##    AIC    BIC   BICc   s.e. 
## 637.55 661.01 677.36   0.31 
## __________________________________________________
## - - - Iteration 3 - - -
## 
## No difference between two successive iterations
## __________________________________________________
## - - - Further tests - - -
## _______________________
## Add parameters/covariates relationships:
##    parameter covariate  p.value
## 4        Tk0       sex 0.000203
## 14       k21       sex 0.021590
## 
## Run scenario for model 4 ... 
## Estimation of the population parameters... 
## _______________________
## Remove parameters/covariates relationships:
##      coefficient  p.value
## 1 beta_Tk0_sex_1 0.160988
## 3   beta_V_sex_1 0.104232
## 5 beta_k21_sex_1 0.484202
## 4   beta_k21_age 0.109704
## 
## Run scenario for model 5 ... 
## Estimation of the population parameters... 
## Sampling from the conditional distribution... 
## Estimation of the log-likelihood... 
## 
## Estimated criteria (importanceSampling):
##    AIC    BIC   BICc   s.e. 
## 633.52 655.51 671.86   0.26 
## _______________________
## Add correlation:
##   randomEffect.1 randomEffect.2 correlation p.value p.wald_lin p.wald_SA
## 3       eta_Tlag          eta_k    0.144046 0.05364        NaN       NaN
##   in.model
## 3    FALSE
## [[1]]
## [1] "Tlag" "k"   
## 
## 
## Run scenario for model 6  ... 
## Estimation of the population parameters... 
## Sampling from the conditional distribution... 
## Estimation of the log-likelihood... 
## 
## Estimated criteria (importanceSampling):
##    AIC    BIC   BICc   s.e. 
## 633.67 657.12 673.47   0.31 
## __________________________________________________
## 
## Final statistical model:
## 
## Covariate model:
##      age sex wt
## Tlag   0   0  0
## Tk0    0   0  0
## V      0   0  1
## k      0   0  0
## k12    0   0  0
## k21    0   0  0
## 
## Correlation model:
## [1] "NULL"
## 
## Residual error model:
##           y 
## "combined2" 
## 
## Estimated criteria (importanceSampling):
##    AIC    BIC   BICc   s.e. 
## 633.52 655.51 671.86   0.26 
## 
## total time: 110.9s
## __________________________________________________
## 
## --------------------------------------------------
## 
## Building the variance model
## 
## __________________________________________________
## Iteration  1 
## 
## removing variability...
## 
## -----------------------
## Step  1 
## Parameters without variability:  
## Parameters with variability   : Tlag Tk0 V k k12 k21 
## 
## Criterion (linearization):  678.2 
## trying to remove omega_k    : 672.8 
## trying to remove omega_V    : 717.6 
## trying to remove omega_Tk0  : 710.9 
## trying to remove omega_Tlag : 709.0 
## trying to remove omega_k12  : 686.3 
## trying to remove omega_k21  : 681.3 
## 
## Criterion:  671.9
## fitting the model with no variability on  k : 665
## variability on k removed
## 
## -----------------------
## Step  2 
## Parameters without variability: k 
## Parameters with variability   : Tlag Tk0 V k12 k21 
## 
## Criterion (linearization):  671.2 
## trying to remove omega_k12  : 722.9 
## trying to remove omega_k21  : 728.4 
## 
## no more variability can be removed
## _______________________
## 
## adding variability...
## 
## -----------------------
## Step  1 
## Parameters without variability: k 
## Parameters with variability   : Tlag Tk0 V k12 k21 
## 
## Criterion (linearization):  671.2 
## 
## no more variability can be added
## 
## __________________________________________________
## 
## Final variance model: 
## 
## Parameters without variability: k 
## Parameters with variability   : Tlag Tk0 V k12 k21 
## 
## Fitting the final model using the original settings... 
## 
## Estimated criteria (importanceSampling):
##    AIC    BIC   BICc   s.e. 
## 628.00 648.52 664.87   0.18 
## 
## total time: 62.9s
## 
## 
## --------------------------------------------------
## 
## Building:
##    -  The covariate model
##    -  The correlation model
##    -  The residual error model
##  
## __________________________________________________
## - - - Initialization - - -
## 
## Covariate model:
##      sex age wt
## Tlag   0   0  0
## Tk0    0   0  0
## V      0   0  1
## k      0   0  0
## k12    0   0  0
## k21    0   0  0
## 
## Correlation model:
## [1] "NULL"
## 
## Residual error model:
##           y 
## "combined2" 
## 
## Estimated criteria (importanceSampling):
##    AIC    BIC   BICc   s.e. 
## 628.00 648.52 664.87   0.18 
## __________________________________________________
## - - - Iteration 1 - - -
## 
## No difference between two successive iterations
## __________________________________________________
## - - - Further tests - - -
## __________________________________________________
## 
## Final statistical model:
## 
## Covariate model:
##      age sex wt
## Tlag   0   0  0
## Tk0    0   0  0
## V      0   0  1
## k      0   0  0
## k12    0   0  0
## k21    0   0  0
## 
## Correlation model:
## [1] "NULL"
## 
## Residual error model:
##           y 
## "combined2" 
## 
## Estimated criteria (importanceSampling):
##    AIC    BIC   BICc   s.e. 
## 628.00 648.52 664.87   0.18 
## 
## total time: 4.2s
## __________________________________________________
## 
## --------------------------------------------------
## 
## Final complete model:
## 
## Variance model: 
## Parameters without variability: k 
## Parameters with variability   : Tlag Tk0 V k12 k21 
## 
## Covariate model:
##      age sex wt
## Tlag   0   0  0
## Tk0    0   0  0
## V      0   0  1
## k      0   0  0
## k12    0   0  0
## k21    0   0  0
## 
## Correlation model:
## [1] "NULL"
## 
## Residual error model:
##           y 
## "combined2" 
## 
## Estimated criteria (importanceSampling):
##    AIC    BIC   BICc   s.e. 
## 628.00 648.52 664.87   0.18 
## 
## total time: 179.7s
## --------------------------------------------------

It is possible to define the settings used by buildAll in a list:

R
warf.pk6  <- pkbuild(data=warfarinPK, stat=TRUE, new.dir="warfarinPK", linearization=T, 
                     settings.stat =list(covToTransform = c("wt"), 
                                         criterion ="AIC",
                                         model=c("covariate", "residualError"),
                                         linearization=T))
R
## 
## [1] "warfarinPK/pk_kaVCl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##      OFV      AIC      BIC     BICc 
## 866.8906 882.8906 894.6165 904.8348 
## 
## [1] "warfarinPK/pk_Tk0VCl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##      OFV      AIC      BIC     BICc 
## 796.7175 812.7175 824.4434 834.6617 
## 
## [1] "warfarinPK/pk_TlagkaVCl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##      OFV      AIC      BIC     BICc 
## 658.7242 678.7242 693.3816 705.6435 
## 
## [1] "warfarinPK/pk_TlagTk0VCl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##      OFV      AIC      BIC     BICc 
## 656.8035 676.8035 691.4609 703.7228 
## 
## [1] "warfarinPK/pk_TlagTk0V1V2QCl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##      OFV      AIC      BIC     BICc 
## 647.8176 675.8176 696.3379 712.6871 
## 
## [1] "warfarinPK/pk_TlagkaV1V2QCl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##      OFV      AIC      BIC     BICc 
## 638.7796 666.7796 687.2999 703.6491 
## 
## [1] "warfarinPK/pk_TlagkaV1V2V3Q2Q3Cl.mlxtran"
## Estimation of the population parameters...
## Estimation of the log-likelihood... 
##      OFV      AIC      BIC     BICc 
## 639.3326 675.3326 701.7159 722.1524 
## 
## --------------------------------------------------
## 
## Building:
##    -  The covariate model
##    -  The residual error model
##  
## __________________________________________________
## - - - Initialization - - -
## 
## Covariate model:
##      sex age wt
## Tlag   0   0  0
## ka     0   0  0
## Cl     0   0  0
## V1     0   0  0
## Q      0   0  0
## V2     0   0  0
## 
## Residual error model:
##           y 
## "combined2" 
## Sampling of the conditional distribution using the initial model ... 
## 
## Estimated criteria (linearization):
##    AIC    BIC   BICc 
## 666.78 687.30 703.65 
## 
## Estimation of the population parameters using the transformed covariates ... 
## Sampling of the conditional distribution using the the transformed covariates ... 
## __________________________________________________
## - - - Iteration 1 - - -
## 
## Covariate model:
##      sex age wt logtWt
## Tlag   0   0  0      0
## ka     1   0  0      0
## Cl     0   0  0      1
## V1     1   0  0      1
## Q      0   1  0      1
## V2     0   0  0      0
## 
## Residual error model:
##           y 
## "combined2" 
## 
## Run scenario for model 1 ... 
## Estimation of the population parameters... 
## Sampling from the conditional distribution... 
## Estimation of the log-likelihood... 
## 
## Estimated criteria (linearization):
##    AIC    BIC   BICc 
## 638.16 667.48 683.83 
## __________________________________________________
## - - - Iteration 2 - - -
## 
## Covariate model:
##      sex age wt logtWt
## Tlag   0   0  0      0
## ka     1   0  0      0
## Cl     0   0  0      1
## V1     0   0  0      1
## Q      0   1  0      1
## V2     0   0  0      0
## 
## Residual error model:
##           y 
## "combined2" 
## 
## Run scenario for model 2 ... 
## Estimation of the population parameters... 
## Sampling from the conditional distribution... 
## Estimation of the log-likelihood... 
## 
## Estimated criteria (linearization):
##    AIC    BIC   BICc 
## 651.06 678.91 695.26 
## __________________________________________________
## - - - Iteration 3 - - -
## 
## Covariate model:
##      sex age wt logtWt
## Tlag   0   0  0      0
## ka     1   0  0      0
## Cl     0   0  0      1
## V1     0   0  0      1
## Q      0   1  0      0
## V2     0   0  0      0
## 
## Residual error model:
##           y 
## "combined2" 
## 
## Run scenario for model 3 ... 
## Estimation of the population parameters... 
## Sampling from the conditional distribution... 
## Estimation of the log-likelihood... 
## 
## Estimated criteria (linearization):
##    AIC    BIC   BICc 
## 648.16 674.54 690.89 
## __________________________________________________
## - - - Iteration 4 - - -
## 
## No difference between two successive iterations
## __________________________________________________
## - - - Further tests - - -
## _______________________
## Add parameters/covariates relationships:
##   parameter covariate   p.value
## 5        ka    logtWt 2.318e-02
## 6        Cl       sex 1.817e-06
## 
## Run scenario for model 5 ... 
## Estimation of the population parameters... 
## _______________________
## Remove parameters/covariates relationships:
##     coefficient   p.value
## 4 beta_Cl_sex_1  0.513109
## 6 beta_V1_sex_1  0.361551
## 8 beta_Q_logtWt 0.0519279
## 
## Run scenario for model 6 ... 
## Estimation of the population parameters... 
## Estimation of the log-likelihood... 
## 
## Estimated criteria (linearization):
##    AIC    BIC   BICc 
## 641.41 669.26 685.61 
## _______________________
## Remove parameters/covariates relationships:
##     coefficient  p.value
## 1 beta_ka_sex_1  0.14197
## 4 beta_V1_sex_1  0.37607
## 6 beta_Q_logtWt 0.209595
## 
## Run scenario for model 7 ... 
## Estimation of the population parameters... 
## Estimation of the log-likelihood... 
## 
## Estimated criteria (linearization):
##    AIC    BIC   BICc 
## 635.35 660.27 676.61 
## __________________________________________________
## 
## Final statistical model:
## 
## Covariate model:
##      age logtWt sex wt
## Tlag   0      0   0  0
## ka     0      0   0  0
## Cl     0      1   0  0
## V1     0      1   0  0
## Q      1      0   0  0
## V2     0      0   0  0
## 
## Residual error model:
##           y 
## "combined2" 
## 
## Estimated criteria (linearization):
##    AIC    BIC   BICc 
## 635.35 660.27 676.61 
## 
## total time: 137.2s
## __________________________________________________
## 
## --------------------------------------------------
## 
## Final complete model:
## 
## Variance model: 
## Parameters without variability:  
## Parameters with variability   : Tlag ka Cl V1 Q V2 
## 
## Covariate model:
##      age logtWt sex wt
## Tlag   0      0   0  0
## ka     0      0   0  0
## Cl     0      1   0  0
## V1     0      1   0  0
## Q      1      0   0  0
## V2     0      0   0  0
## 
## Correlation model:
## [1] "NULL"
## 
## Residual error model:
##           y 
## "combined2" 
## 
## Estimated criteria (linearization):
##    AIC    BIC   BICc 
## 635.35 660.27 676.61 
## 
## total time: 144.7s
## --------------------------------------------------
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