PKtools.AIC {PKtools}R Documentation

PKtools.AIC

Description

PKtools.AIC calculates the AIC and AICc.

Usage

PKtools.AIC(loglike,n,K,...)

Arguments

loglike loglikelihood
n total number of samples
K number of fixed parameters including both mean and variance parameters
... additional arguments to be passed to lower level functions

Value

This function outputs the AIC and and the small sample AIC, AICc, as well as the objective function (-2 x loglikelihood) and K.

Author(s)

M.S. Blanchard <sblanchard@coh.org>

References

Burnham, K.P. and Anderson,D.R., (2002). Model Selection and Multimodel Inference: A Practical Information - Theoretic Approach (2nd edition). Springer: New York.

See Also

tex, HTMLtools

Examples

 
library(PKtools)
library(nlme) 
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("V","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=lV+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)

K = attr(logLik(MM$mm), "df")
n<-nrow(MM$pkdata) 
AIC.table<-data.frame(PKtools.AIC(loglike=logLik(MM$mm),n=n,K=K), row.names="")
AIC.table 


[Package PKtools version 1.4-0 Index]