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

View source: R/func_plots.R

default.saemix.plotsR Documentation

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, ...)

Arguments

saemixObject

an object returned by the saemix function

...

optional arguments passed to the plots

Details

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

Value

Depending on the type argument, the following plots are produced:

  • 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

Author(s)

Emmanuelle Comets emmanuelle.comets@inserm.fr, 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

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")

# Reducing the number of iterations due to time constraints for CRAN
saemix.options<-list(seed=632545,save=FALSE,save.graphs=FALSE,nbiter.saemix=c(100,100))

saemix.fit<-saemix(saemix.model,saemix.data,saemix.options)

default.saemix.plots(saemix.fit)

# Not run (time constraints for CRAN)
# basic.gof(saemix.fit)

# Not run (time constraints for CRAN)
# advanced.gof(saemix.fit)

individual.fits(saemix.fit)



saemix documentation built on Aug. 5, 2022, 5:25 p.m.