saemix (version 0.96.1)

saemix-package: Stochastic Approximation Expectation Maximization (SAEM) algorithm for non-linear mixed effects models

Description

ll{ - Computing the maximum likelihood estimator of the population parameters, without any approximation of the model (linearization, quadrature approximation, . . . ), using the Stochastic Approximation Expectation Maximization (SAEM) algorithm - Estimation of the Fisher Information matrix - Estimation of the individual parameters - Estimation of the likelihood - Plot convergence graphs }

Details

ll{ Package: saemix Type: Package Version: 0.9 Date: 2010-09-19 License: GPL (>=) 1.2 LazyLoad: yes }

The SAEM package includes a number of undocumented functions, which are not meant to be used directly by the user. [object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

References

Kuhn, E., and Lavielle, M. Maximum likelihood estimation in nonlinear mixed effects models. Computational Statistics and Data Analysis 49, 4 (2005), 1020-1038. Monolix32_UsersGuide.pdf (http://software.monolix.org/sdoms/software/)

See Also

nlme,SaemixData,SaemixModel, SaemixObject,saemix

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")

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.model<-saemixModel(model=model1cpt,
  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(0,1,0,0,0,0),ncol=3,byrow=TRUE),fixed.estim=c(1,1,1),
  covariance.model=matrix(c(1,0,0,0,1,0,0,0,1),ncol=3,byrow=TRUE), 
  omega.init=matrix(c(1,0,0,0,1,0,0,0,1),ncol=3,byrow=TRUE), error.model="constant")

saemix.options<-list(seed=632545,save=FALSE,save.graphs=FALSE)

saemix.fit<-saemix(saemix.model,saemix.data,saemix.options)

print(saemix.fit)

plot(saemix.fit)