Overview

Description

buildAll builds the complete statistical model by iteratively calling functions buildmlx and buildVar.

Usage

buildAll <- function(project=NULL, final.project=NULL, model="all", prior=NULL, weight=NULL, coef.w1=0.5, cv.min=0.001, fError.min=1e-3,
                     paramToUse="all", covToTest="all", covToTransform="none", center.covariate=FALSE, 
                     criterion="BICc", linearization=FALSE, ll=T, test=T, direction=NULL, steps=1000,
                     max.iter=20, explor.iter=2, seq.cov=FALSE, seq.corr=TRUE, seq.cov.iter=0, 
                     p.max=0.1, p.min=c(0.075, 0.05, 0.1), print=TRUE, nb.model=1,
                     fix.param1=NULL, fix.param0=NULL, remove=T, add=T, delta=c(30,10,5), 
                     omega.set=NULL, pop.set1=NULL, pop.set2=NULL)

Arguments

project: a string: the initial Monolix project
final.project: a string: the final Monolix project (default adds "_buildAll" to the original project)
model: components of the model to optimize c("residualError", "covariate", "correlation"), (default="all")
prior: list of prior probabilities for each component of the model (default=NULL)
weight: list of penalty weights for each component of the model (default=NULL)
coef.w1: multiplicative weight coefficient used for the first iteration only (default=0.5)
cv.min: value of the coefficient of variation below which an individual parameter is considered fixed (default=0.001)
fError.min: minimum fraction of residual variance for combined error model (default = 1e-3)
paramToUse: list of parameters possibly function of covariates (default="all")
covToTest: components of the covariate model that can be modified (default="all")
covToTransform: list of (continuous) covariates to be log-transformed (default="none")
center.covariate: TRUE/FALSE center the covariates of the final model (default=FALSE)
criterion: penalization criterion to optimize c("AIC", "BIC", "BICc", gamma)
linearization: TRUE/FALSE whether the computation of the likelihood is based on a linearization of the model (default=FALSE, deprecated)
ll: TRUE/FALSE compute the observe likelihood and the criterion to optimize at each iteration
test: TRUE/FALSE perform additional statistical tests for building the model (default=TRUE)
direction: method for covariate search c("full", "both", "backward", "forward"), (default="full" or "both")
steps: maximum number of iteration for stepAIC (default=1000)
max.iter: maximum number of iterations (default=20)
explor.iter: number of iterations during the exploratory phase (default=2)
seq.cov: TRUE/FALSE whether the covariate model is built before the correlation model
seq.corr: TRUE/FALSE whether the correlation model is built iteratively (default=TRUE)
seq.cov.iter: number of iterations before building the correlation model (only when seq.cov=F, default=0)
p.max: maximum p-value used for removing non significant relationships between covariates and individual parameters (default=0.1)
p.min: minimum p-values used for testing the components of a new model (default=c(0.075, 0.05, 0.1))
print: TRUE/FALSE display the results (default=TRUE)
nb.model: number of models to display at each iteration (default=1)
fix.param1: parameters with variability that cannot be removed (default=NULL)
fix.param0: parameters without variability that cannot be added (default=NULL)
remove: try to remove random effects (default=T)
add: try to add random effects (default=T)
delta: maximum difference in criteria for testing a new model (default=c(30,10,5))
omega.set: settings to define how a variance varies during iterations of SAEM
pop.set1: Monolix settings 1
pop.set2: Monolix settings 2

Example

Using version <= 4.0.2 of Rsmlx requires to load explicitly lixoftConnectors. This package is automatically loaded with Rsmlx 4.0.3.

library(lixoftConnectors)
initializeLixoftConnectors(software="monolix")
library(Rsmlx)

We select a Monolix project

project <- "projects/simulatedPK2.mlxtran"

Example using the defaults settings of buildmlx and buildVar:

buildAll.res1 <- buildAll(project)

Selected model:

loadProject(buildAll.res1$project)
getIndividualParameterModel()

## $name
## [1] "ka" "Cl" "V1" "Q"  "V2"
## 
## $distribution
##          ka          Cl          V1           Q          V2 
## "logNormal" "logNormal" "logNormal" "logNormal" "logNormal" 
## 
## $limits
## named list()
## 
## $formula
## [1] "log(ka) = log(ka_pop) + eta_ka\nlog(Cl) = log(Cl_pop) + beta_Cl_X03*X03 + eta_Cl\nlog(V1) = log(V1_pop) + beta_V1_X01*X01 + beta_V1_X02*X02 + eta_V1\nlog(Q) = log(Q_pop) + beta_Q_X01*X01\nlog(V2) = log(V2_pop)\nCorrelations\n\tID : {Cl, V1}\n"
## 
## $variability
## $variability$id
##    ka    Cl    V1     Q    V2 
##  TRUE  TRUE  TRUE FALSE FALSE 
## 
## 
## $covariateModel
## $covariateModel$ka
##   X01   X02   X03   X04   X05   X06   X07   X08   X09   X10 
## FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 
## 
## $covariateModel$Cl
##   X01   X02   X03   X04   X05   X06   X07   X08   X09   X10 
## FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 
## 
## $covariateModel$V1
##   X01   X02   X03   X04   X05   X06   X07   X08   X09   X10 
##  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 
## 
## $covariateModel$Q
##   X01   X02   X03   X04   X05   X06   X07   X08   X09   X10 
##  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 
## 
## $covariateModel$V2
##   X01   X02   X03   X04   X05   X06   X07   X08   X09   X10 
## FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 
## 
## 
## $correlationBlocks
## $correlationBlocks$id
## $correlationBlocks$id[[1]]
## [1] "Cl" "V1"

Example using several settings of buildmlx and buildVar:

buildAll.res2 <- buildAll(project, final.project="projects/buildAll2.mlxtran", 
                          paramToUse=c("Cl", "V1", "Q"), covToTest=c("X01", "X02", "X03", "X04"),
                          fix.param0="Q", fix.param1=c("ka", "Cl"))

Selected model:

loadProject(buildAll.res2$project)
getIndividualParameterModel()

## $name
## [1] "ka" "Cl" "V1" "Q"  "V2"
## 
## $distribution
##          ka          Cl          V1           Q          V2 
## "logNormal" "logNormal" "logNormal" "logNormal" "logNormal" 
## 
## $limits
## named list()
## 
## $formula
## [1] "log(ka) = log(ka_pop) + eta_ka\nlog(Cl) = log(Cl_pop) + beta_Cl_X03*X03 + eta_Cl\nlog(V1) = log(V1_pop) + beta_V1_X01*X01 + beta_V1_X02*X02 + eta_V1\nlog(Q) = log(Q_pop) + beta_Q_X01*X01\nlog(V2) = log(V2_pop)\nCorrelations\n\tID : {Cl, V1}\n"
## 
## $variability
## $variability$id
##    ka    Cl    V1     Q    V2 
##  TRUE  TRUE  TRUE FALSE FALSE 
## 
## 
## $covariateModel
## $covariateModel$ka
##   X01   X02   X03   X04   X05   X06   X07   X08   X09   X10 
## FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 
## 
## $covariateModel$Cl
##   X01   X02   X03   X04   X05   X06   X07   X08   X09   X10 
## FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 
## 
## $covariateModel$V1
##   X01   X02   X03   X04   X05   X06   X07   X08   X09   X10 
##  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 
## 
## $covariateModel$Q
##   X01   X02   X03   X04   X05   X06   X07   X08   X09   X10 
##  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 
## 
## $covariateModel$V2
##   X01   X02   X03   X04   X05   X06   X07   X08   X09   X10 
## FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 
## 
## 
## $correlationBlocks
## $correlationBlocks$id
## $correlationBlocks$id[[1]]
## [1] "Cl" "V1"

Following what is done with buildmlxand buildVar, prior information about the statistical model can be introduced either by defining prior probabilities or introducing a weighting for the penalty term.

In this example, relationships between covariates and parameters are strongly penalized (weight=5) while correlations are lightly penalized (weight=0.1).

Different weights are used for the variances: variability of Q is privileged while that of V1 is strongly penalized.

buildAll.res3 <- buildAll(project, weight=list(covariate=5, correlation=0.3, variance=c(Q=0.05, V1=20)))

print(buildAll.res3$variability.model)

## NULL

loadProject(buildAll.res3$project)
getIndividualParameterModel()

## $name
## [1] "ka" "Cl" "V1" "Q"  "V2"
## 
## $distribution
##          ka          Cl          V1           Q          V2 
## "logNormal" "logNormal" "logNormal" "logNormal" "logNormal" 
## 
## $limits
## named list()
## 
## $formula
## [1] "log(ka) = log(ka_pop) + eta_ka\nlog(Cl) = log(Cl_pop) + beta_Cl_X03*X03 + eta_Cl\nlog(V1) = log(V1_pop) + beta_V1_X01*X01 + beta_V1_X02*X02 + eta_V1\nlog(Q) = log(Q_pop) + beta_Q_X01*X01\nlog(V2) = log(V2_pop)\nCorrelations\n\tID : {Cl, V1}\n"
## 
## $variability
## $variability$id
##    ka    Cl    V1     Q    V2 
##  TRUE  TRUE  TRUE FALSE FALSE 
## 
## 
## $covariateModel
## $covariateModel$ka
##   X01   X02   X03   X04   X05   X06   X07   X08   X09   X10 
## FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 
## 
## $covariateModel$Cl
##   X01   X02   X03   X04   X05   X06   X07   X08   X09   X10 
## FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 
## 
## $covariateModel$V1
##   X01   X02   X03   X04   X05   X06   X07   X08   X09   X10 
##  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 
## 
## $covariateModel$Q
##   X01   X02   X03   X04   X05   X06   X07   X08   X09   X10 
##  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 
## 
## $covariateModel$V2
##   X01   X02   X03   X04   X05   X06   X07   X08   X09   X10 
## FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 
## 
## 
## $correlationBlocks
## $correlationBlocks$id
## $correlationBlocks$id[[1]]
## [1] "Cl" "V1"

Automatic complete statistical model building

Marc Lavielle
June 14th, 2022

Overview

Description

Usage

Arguments

Example

Automatic complete statistical model building

Marc Lavielle June 14th, 2022

Overview

Description

Usage

Arguments

Example

Marc Lavielle
June 14th, 2022