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

In saemix: Stochastic Approximation Expectation Maximization (SAEM) Algorithm

Stochastic Approximation Expectation Maximization (SAEM) algorithm

Description

SAEM algorithm perform parameter estimation for nonlinear mixed effects models without any approximation of the model (linearization, quadrature approximation, . . . )

Usage

saemix(model, data, control = list())

Arguments

model	an object of class SaemixModel, created by a call to the function saemixModel
data	an object of class SaemixData, created by a call to the function saemixData
control	a list of options, see saemixControl

Details

The SAEM algorithm is a stochastic approximation version of the standard EM algorithm proposed by Kuhn and Lavielle (see reference). Details of the algorithm can be found in the pdf file accompanying the package.

Value

An object of class SaemixObject containing the results of the fit of the data by the non-linear mixed effect model. A summary of the results is printed out to the terminal, and, provided the appropriate options have not been changed, numerical and graphical outputs are saved in a directory.

Author(s)

Emmanuelle Comets [e-mail]

Audrey Lavenu

Marc Lavielle

References

E Comets, A Lavenu, M Lavielle M (2017). Parameter estimation in nonlinear mixed effect models using saemix, an R implementation of the SAEM algorithm. Journal of Statistical Software, 80(3):1-41.

E Kuhn, M Lavielle (2005). Maximum likelihood estimation in nonlinear mixed effects models. Computational Statistics and Data Analysis, 49(4):1020-1038.

E Comets, A Lavenu, M Lavielle (2011). SAEMIX, an R version of the SAEM algorithm. 20th meeting of the Population Approach Group in Europe, Athens, Greece, Abstr 2173.

See Also

SaemixData,SaemixModel, SaemixObject, saemixControl, plot.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,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(0,1,0,0,0,0),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")


# Not run (strict time constraints for CRAN)
 saemix.fit<-saemix(saemix.model,saemix.data,list(seed=632545,directory="newtheo",
 save=FALSE,save.graphs=FALSE, print=FALSE))
 
# Prints a summary of the results
print(saemix.fit)

# Outputs the estimates of individual parameters
psi(saemix.fit)

# Shows some diagnostic plots to evaluate the fit
plot(saemix.fit)

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