saemix-package: Stochastic Approximation Expectation Maximization (SAEM)...

In saemixdevelopment/saemix: Stochastic Approximation Expectation Maximization (SAEM) Algorithm

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

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

Author(s)

Emmanuelle Comets <[e-mail]>, Audrey Lavenu, Marc Lavielle.

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)

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

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

saemixdevelopment/saemix documentation built on May 27, 2020, 1:56 p.m.