saemix (version 2.3)

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

Description

- 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	- Computing the maximum likelihood estimator of the population parameters, without any approximation

Details

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.

default

setdefault

computational functions

cutoff,cutoff.max, cutoff.eps, cutoff.res, compute.Uy, compute.Uy.nocov, conditional.distribution, gqg.mlx

distributions

normcdf, norminv

error model

error

sampling

trnd.mlx, tpdf.mlx, gammarnd.mlx

parameter transformations

transpsi, transphi, dtransphi

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

# NOT RUN {
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)

# Not run (strict time constraints for CRAN)
# saemix.fit<-saemix(saemix.model,saemix.data,saemix.options)

# print(saemix.fit)
# plot(saemix.fit)
# }