| 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)