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
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 fileheaderTypes
: a vector of stringsadministration
("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:
head(read.csv('data/warfarinPK.csv'))
## 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:
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.
warf.pk1a <- pkbuild(data=warfarinPK, new.dir="warfarinPK")
##
## [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
.
print(warf.pk1a)
## $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):
warf.pk1b <- pkbuild(data=warfarinPK, new.dir="warfarinPK", linearization=T, criterion="AIC")
##
## [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
print(warf.pk1b)
## $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:
warf.pk2 <- pkbuild(data=warfarinPK, new.dir="warfarinPK", param="rate")
##
## [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
print(warf.pk2$res)
## 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
print(warf.pk2$pop.est)
## 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:
warf.pk3 <- pkbuild(data=warfarinPK, new.dir="warfarinPK", param="both")
##
## [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
print(warf.pk3$res)
## 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
warf.pk4 <- pkbuild(project="projects/warfarinPK.mlxtran", new.dir="warfarinPK")
##
## [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
print(warf.pk4$res)
## 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
:
warf.pk5 <- pkbuild(data=warfarinPK, stat=TRUE, new.dir="warfarinPK", param="rate")
##
## [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:
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))
##
## [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
## --------------------------------------------------