residplot {PKtools} | R Documentation |
resid creates individual residual vs predicted plots at the population (marginal) and individual (conditinal) levels of the mixed model the can be used with the method identify to identify outliers.
residplot(x,...)
x |
object of class, NONMEM, PKNLME, or WinBUGS |
... |
additional arguments to be passed to lower level functions |
The method identify can be used with objects of class NONMEM, PKNLME, and WinBUGS by including the following code.
plots of residual versus predicted values for both the population (marginal) and individual (conditional) levels.
M.S. Blanchard <sblanchard@coh.org>
identify
, obvsprplot
, diagplot
if (.Platform$OS.type =="windows"){ library(PKtools) library(nlme) curwd=getwd() if (file.exists("C:/nmv/run")){ setwd("C:/nmv/run") data(Theoph) Theoph<-Theoph[Theoph$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 nameData<-list(covnames=c("wt"), yvarlab="Sqrt(Theop. Conc.) Sqrt(mg/L)", xvarlab="Time since dose (hrs)", reparams=c("Ka", "V", "Cl"), params=c("Ka", "V", "Cl"), tparams=c("log(Ka)", "log(V)", "log(Cl)"), varnames=c("D[1,1]","D[1,2]","D[2,2]","D[1,3]","D[2,3]","D[3,3]")) data<-list(pkvar=Theo, cov=wt.v) NM<-RunNM(inputStructure="control.model5", data=data, nameData=nameData) residplot(NM, "p") setwd(curwd) } else{ "You do not have NONMEM." } }