npdeSaemix: Create an npdeObject from an saemixObject

In saemix: Stochastic Approximation Expectation Maximization (SAEM) Algorithm

Create an npdeObject from an saemixObject

Description

This function uses the npde library to compute normalised prediction distribution errors (npde) and normalised prediction discrepancies (npd). The simulations can also be used to plot VPCs. As of version 3.0, the plot functions for diagnostics involving VPC, npde or npd are deprecated and the user will be redirected to the current proposed method (creating an NpdeObject and using the plot functions from the library npde).

Usage

npdeSaemix(saemixObject, nsim = 1000)

Arguments

saemixObject	a fitted object resulting from a call to saemix()
nsim	the number of simulations used to compute npde (1000 by default, we suggest increasing it for large datasets)

Details

Since version 3.0, the saemix package depends on the npde package, which computes the npd/npde and produces graphs. See the documentation for npde for details on the computation methods See the PDF documentation and the bookdown https://iame-researchcenter.github.io/npde_bookdown/ for details on the different plots available.

Value

An object of class NpdeObject

Author(s)

Emmanuelle Comets [e-mail]

References

E Comets, K Brendel, F Mentre (2008). Computing normalised prediction distribution errors to evaluate nonlinear mixed-effect models: the npde add-on package for R. Computer Methods and Programs in Biomedicine, 90:154-66.

K Brendel, E Comets, C Laffont, C Laveille, F Mentre (2006). Metrics for external model evaluation with an application to the population pharmacokinetics of gliclazide. Pharmaceutical Research, 23:2036–49

PDF documentation for npde 3.0: https://github.com/ecomets/npde30/blob/main/userguide_npde_3.1.pdf

See Also

npde.graphs, gof.test

NpdeObject npde.plot.select autonpde

npde.plot.scatterplot npde.plot.dist

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(algorithm=c(1,0,0),seed=632545,save=FALSE,save.graphs=FALSE, 
displayProgress=FALSE)
# Not run
# Works interactively but not in the contained environment of CRAN (it looks for a datafile 
# instesad of finding the dataset in the environment)
# saemix.fit<-saemix(saemix.model,saemix.data,saemix.options)
# npde.obj<-npdeSaemix(saemix.fit)
# plot(npde.obj)
# plot(npde.obj, plot.type="vpc")
# plot(npde.obj, plot.type="covariates")
# plot(npde.obj, plot.type="cov.x.scatter")
# plot(npde.obj, plot.type="cov.ecdf")

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