compare.saemix: Model comparison with information criteria (AIC, BIC).

In saemix: Stochastic Approximation Expectation Maximization (SAEM) Algorithm

Model comparison with information criteria (AIC, BIC).

Description

A specific penalty is used for BIC (BIC.cov) when the compared models have in common the structural model and the covariance structure for the random effects (see Delattre et al., 2014).

Usage

compare.saemix(..., method = c("is", "lin", "gq"))

Arguments

...	Two or more objects returned by the saemix function
method	The method used for computing the likelihood : "is" (Importance Sampling), "lin" (Linearisation) or "gq" (Gaussian quadrature). The default is to use importance sampling "is". If the requested likelihood is not available in all model objects, the method stops with a warning.

Details

Note that the comparison between two or more models will only be valid if they are fitted to the same dataset.

Value

A matrix of information criteria is returned, with at least two columns containing respectively AIC and BIC values for each of the compared models. When the models have in common the structural model and the covariance structure for the random effects, the matrix includes an additional column with BIC.cov values that are more appropriate when the comparison only concerns the covariates.

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

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)
}
# Model with one covariate
saemix.model1<-saemixModel(model=model1cpt,modeltype="structural",
  description="One-compartment model, clearance dependent on weight",
  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))
# Model with two covariates
saemix.model2<-saemixModel(model=model1cpt,modeltype="structural",
   description="One-compartment model, clearance dependent on weight, volume dependent on sex",
   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(0,0,1,0,1,0),ncol=3,byrow=TRUE))
# Model with three covariates
saemix.model3<-saemixModel(model=model1cpt,modeltype="structural",
  description="One-cpt model, clearance, absorption dependent on weight, volume dependent on sex",
  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(1,0,1,0,1,0),ncol=3,byrow=TRUE))


# Running the main algorithm to estimate the population parameters
saemix.options<-list(seed=632545,save=FALSE,save.graphs=FALSE)
saemix.fit1<-saemix(saemix.model1,saemix.data,saemix.options)
saemix.fit2<-saemix(saemix.model2,saemix.data,saemix.options)
saemix.fit3<-saemix(saemix.model3,saemix.data,saemix.options)

compare.saemix(saemix.fit1, saemix.fit2, saemix.fit3)
compare.saemix(saemix.fit1, saemix.fit2, saemix.fit3, method = "lin")

# We need to explicitly run Gaussian Quadrature if we want to use it in
# the comparisons
saemix.fit1 <- llgq.saemix(saemix.fit1)
saemix.fit2 <- llgq.saemix(saemix.fit2)
saemix.fit3 <- llgq.saemix(saemix.fit3)
compare.saemix(saemix.fit1, saemix.fit2, saemix.fit3, method = "gq")

saemix documentation built on July 9, 2023, 7:43 p.m.