saemix (version 0.96.1)

saemix.plot.select: Plots of the results obtained by SAEM

Description

Several plots (selectable by the type argument) are currently available: convergence plot, individual plots, predictions versus observations, distribution plots, residual plots, VPC.

Usage

saemix.plot.select(saemixObject, data = FALSE, convergence = FALSE,
  likelihood = FALSE, individual.fit = FALSE, population.fit = FALSE, 
  both.fit = FALSE, observations.vs.predictions = FALSE,
  residuals.scatter = FALSE, residuals.distribution = FALSE,
  random.effects = FALSE, correlations = FALSE, 
  parameters.vs.covariates = FALSE, randeff.vs.covariates = FALSE, 
  marginal.distribution = FALSE, vpc = FALSE, npde = FALSE, ...)

Arguments

saemixObject

an object returned by the saemix function

data

if TRUE, produce a plot of the data. Defaults to FALSE

convergence

if TRUE, produce a convergence plot. Defaults to FALSE

likelihood

if TRUE, produce a plot of the estimation of the LL by importance sampling. Defaults to FALSE

individual.fit

if TRUE, produce individual fits with individual estimates. Defaults to FALSE

population.fit

if TRUE, produce individual fits with population estimates. Defaults to FALSE

both.fit

if TRUE, produce individual fits with both individual and population estimates. Defaults to FALSE

observations.vs.predictions

if TRUE, produce a plot of observations versus predictions. Defaults to FALSE

residuals.scatter

if TRUE, produce scatterplots of residuals versus predictor and predictions. Defaults to FALSE

residuals.distribution

if TRUE, produce plots of the distribution of residuals. Defaults to FALSE

random.effects

if TRUE, produce boxplots of the random effects. Defaults to FALSE

correlations

if TRUE, produce a matrix plot showing the correlation between random effects. Defaults to FALSE

parameters.vs.covariates

if TRUE, produce plots of the relationships between parameters and covariates, using the Empirical Bayes Estimates of individual parameters. Defaults to FALSE

randeff.vs.covariates

if TRUE, produce plots of the relationships between random effects and covariates, using the Empirical Bayes Estimates of individual random effects. Defaults to FALSE

marginal.distribution

if TRUE, produce plots of the marginal distribution of the random effects. Defaults to FALSE

vpc

if TRUE, produce Visual Predictive Check plots. Defaults to FALSE

npde

if TRUE, produce plots of the npde. Defaults to FALSE

...

optional arguments passed to the plots

Value

• None

Details

This function plots different graphs related to the algorithm (convergence plots, likelihood estimation) as well as diagnostic graphs. A description is provided in the PDF documentation. [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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

SaemixObject,saemix, default.saemix.plots, saemix.plot.setoptions, saemix.plot.data, saemix.plot.convergence, saemix.plot.llis, saemix.plot.randeff, saemix.plot.obsvspred, saemix.plot.fits, saemix.plot.parcov, saemix.plot.randeffcov, saemix.plot.distpsi, saemix.plot.scatterresiduals, saemix.plot.distribresiduals, saemix.plot.vpc

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)

saemix.plot.select(saemix.fit,data=TRUE,main="Spaghetti plot of data")

# Putting several graphs on the same plot
par(mfrow=c(2,2))
saemix.plot.select(saemix.fit,data=TRUE,vpc=TRUE,
  observations.vs.predictions=TRUE, new=FALSE)