saemix (version 0.96.1)

default.saemix.plots: Wrapper functions to produce certain sets of default plots

Description

These functions produce default sets of plots, corresponding to diagnostic or individual fits.

Usage

default.saemix.plots(saemixObject, ...)
basic.gof(saemixObject, ...)
advanced.gof(saemixObject, ...)
covariate.fits(saemixObject, which = "parameters", ...)
individual.fits(saemixObject, ...)

Arguments

saemixObject

an object returned by the saemix function

which

for covariate fits, whether they should be produced with the EBE estimates of the parameters (the default) or with random effects (which="randeff")

...

optional arguments passed to the plots

Value

• default.saemix.plots
  by default, the following plots are produced: a plot of the data, convergence plots, plot of the likelihood by importance sampling (if computed), plots of observations versus predictions, scatterplots and distribution of residuals, boxplot of the random effects, correlations between random effects, distribution of the parameters, VPC
• basic.gof
  basic goodness-of-fit plots: convergence plots, plot of the likelihood by importance sampling (if computed), plots of observations versus predictions
• advanced.gof
  advanced goodness-of-fit plots: scatterplots and distribution of residuals, VPC,...
• covariate.fits
  plots of all estimated parameters versus all covariates in the dataset
• individual.fits
  plots of individual predictions (line) overlayed on individual observations (dots) for all subjects in the dataset

Details

These functions are wrapper functions designed to produce default sets of plots to help the user assess their model fits.

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

saemix, saemix.plot.data, saemix.plot.setoptions, 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,
  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)

default.saemix.plots(saemix.fit)

basic.gof(saemix.fit)

advanced.gof(saemix.fit)

individual.fits(saemix.fit)