diagtrplot {PKtools} | R Documentation |
diagtrplot creates a trellis plot of the observed concentrations and predicted values vs time by subject.
diagtrplot(x, level, xvarlab, yvarlab, pages,...)
x |
variable identifying the clustering variable |
level |
level of mixed model ("p"-population, "i"-individual) |
xvarlab |
label for x variable |
yvarlab |
label for y variable |
pages |
number of pages to print, 1 prints first page |
... |
additional arguments to be passed to lower level functions |
diagtrplot produces a trellis plot of observed concentrations and predicted values vs time by subject.
M.S. Blanchard<sblanchard@coh.org>
trplot
, diagplot
, residplot
, obvsprplot
, tex
, HTMLtools
library(nlme) library(PKtools) data(Theoph) Theoph<-Theoph[Theoph$Time!=0,] id<-as.numeric(as.character(Theoph$Subject)) dose<-Theoph$Dose time<-Theoph$Time conc<-round(sqrt(Theoph$conc),4) Theo<-data.frame(cbind(id,dose,time,conc)) names(Theo)<-c("id","dose","time","conc") wt.v<-Theoph$Wt data<-list(pkvar=Theo, cov=wt.v) nameData<-list(covnames=c("wt"), yvarlab="Sqrt(Theop. Conc.) (mg/L)", xvarlab="Time since dose (hrs)", reparams=c("Cl"), params=c("Ka","V", "Cl"), tparams=c("log(Ka)","log(V)","log(CL)")) model.def<-list(fixed.model=lKa+lV+lCl~1,random.model=lCl~1, start.lst=c(lKa=.3,lV=-.6,lCl=-3), form=conc~sonecpmt(dose, time, lV, lKa, lCl), control=nlmeControl(returnObject=FALSE)) MM<-RunNLME(inputStructure=model.def,data=data, nameData=nameData) diagtrplot(x=MM,level="p", xvarlab=nameData$xvarlab, yvarlab=nameData$xvarlab, pages=1)