step.saemix: Stepwise procedure for joint selection of covariates and...

In saemix: Stochastic Approximation Expectation Maximization (SAEM) Algorithm

Stepwise procedure for joint selection of covariates and random effects

Description

Joint selection of covariates and random effects in a nonlinear mixed effects model by a stepwise-type algorithm based on two different versions of BIC for covariate selection and random effects selection respectively. Selection is made among the covariates as such specified in the SaemixData object. Only uncorrelated random effects structures are considered.

Usage

step.saemix(
  saemixObject,
  trace = TRUE,
  direction = "forward",
  covariate.init = NULL
)

Arguments

saemixObject	An object returned by the saemix function
trace	If TRUE, a table summarizing the steps of the algorithm is printed. Default "TRUE"
direction	The mode of stepwise search, can be one of "both", "backward", or "forward", with a default of "forward".
covariate.init	A matrix specifying the initial covariate structure to be considered in the algorithm. covariate.init is only used if the direction argument is "both".

Value

An object of the SaemixObject class storing the covariate model and the covariance structure of random effects of the final model.

Author(s)

Maud Delattre

References

M Delattre, M Lavielle, MA Poursat (2014) A note on BIC in mixed effects models. Electronic Journal of Statistics 8(1) p. 456-475 M Delattre, MA Poursat (2017) BIC strategies for model choice in a population approach. (arXiv:1612.02405)

Examples

data(theo.saemix)

saemix.data<-saemixData(name.data=theo.saemix,header=TRUE,sep=" ",na=NA,
  name.group=c("Id"),name.predictors=c("Dose","Time"),
  name.response=c("Concentration"),name.covariates=c("Weight","Sex"),
  units=list(x="hr",y="mg/L",covariates=c("kg","-")), name.X="Time")

# Definition of models to be compared
model1cpt<-function(psi,id,xidep) { 
   dose<-xidep[,1]
   tim<-xidep[,2]  
   ka<-psi[id,1]
   V<-psi[id,2]
   CL<-psi[id,3]
   k<-CL/V
   ypred<-dose*ka/(V*(ka-k))*(exp(-k*tim)-exp(-ka*tim))
   return(ypred)
}

saemix.model1<-saemixModel(model=model1cpt,modeltype="structural", 
     description="One-compartment model with first-order absorption",
     psi0=matrix(c(1.,20,0.5,0.1,0,-0.01),ncol=3,byrow=TRUE, 
     dimnames=list(NULL, c("ka","V","CL"))), transform.par=c(1,1,1),
     covariate.model=matrix(c(0,0,1,0,0,0),ncol=3,byrow=TRUE))
                          
saemix.options<-list(seed=632545,save=FALSE,save.graphs=FALSE)
saemix.fit1<-saemix(saemix.model1,saemix.data,saemix.options)     

## Not run: 
res.forward <- step.saemix(saemix.fit1, direction = "forward")
res.backward <- step.saemix(saemix.fit1, direction = "backward")
covariate.init <- matrix(c(1,0,0,0,1,0),ncol=3,nrow=2)
res.stepwise <- step.saemix(saemix.fit1, direction="both")

## End(Not run)

saemix documentation built on May 29, 2024, 10:43 a.m.