saemix (version 0.96.1)

saemix.plots: General plot function from SAEM

Description

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

Usage

plot(x,y, ...)

Arguments

x

an object returned by the saemix function

y

empty

...

optional arguments passed to the plots

Value

• None

Details

This is the generic plot function for an SaemixObject object, which implements different graphs related to the algorithm (convergence plots, likelihood estimation) as well as diagnostic graphs. A description is provided in the PDF documentation. Arguments such as main, xlab, etc... that can be given to the generic plot function may be used, and will be interpreted according to the type of plot that is to be drawn.

A special argument plot.type can be set to determine the type of plot; it can be one of: [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] In addition, the following values for plot.type produce a series of plots: [object Object],[object Object],Each plot can be customised by modifying options, either through a list of options set by the saemix.plot.setoptions function, or on the fly by passing an option in the call to the plot (see examples).

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, saemix.plot.setoptions, saemix.plot.select, saemix.plot.data

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)

# Set of default plots
plot(saemix.fit)

# Data
plot(saemix.fit,plot.type="data")

# Convergence
plot(saemix.fit,plot.type="convergence")

# Individual plot for subject 1, smoothed
plot(saemix.fit,plot.type="individual.fit",ilist=1,smooth=TRUE)

# Individual plot for subject 1 to 12, with ask set to TRUE 
# (the system will pause before a new graph is produced)
plot(saemix.fit,plot.type="individual.fit",ilist=c(1:12),ask=TRUE)

# Diagnostic plot: observations versus population predictions
par(mfrow=c(1,1))
plot(saemix.fit,plot.type="observations.vs.predictions",level=0,new=FALSE)

# LL by Importance Sampling
plot(saemix.fit,plot.type="likelihood")

# Scatter plot of residuals
# Data will be simulated to compute weighted residuals and npde
# the results shall be silently added to the object saemix.fit
plot(saemix.fit,plot.type="residuals.scatter")

# Boxplot of random effects
plot(saemix.fit,plot.type="random.effects")

# Relationships between parameters and covariates
plot(saemix.fit,plot.type="parameters.vs.covariates")

# Relationships between parameters and covariates, on the same page
par(mfrow=c(3,2))
plot(saemix.fit,plot.type="parameters.vs.covariates",new=FALSE)

# VPC
plot(saemix.fit,plot.type="vpc")