# R/dropAIC.fun.r In NHPoisson: Modelling and Validation of Non Homogeneous Poisson Processes

#### Documented in dropAIC.fun

dropAIC.fun <-function(mlePP,modSim=FALSE,...)
{
covariates<-mlePP@covariates
ncov<-dim(covariates)[2]
AICcurrent<-AIC(mlePP,...)
AICnew<-NULL
tind<-mlePP@tind
if (ncov>(tind==FALSE)) #if there is an intercept ncov can be 1 but otherwise must be at least 2
{
for (j in c(1:ncov))
{
aux<-update(mlePP, covariates=covariates[,-j], start=as.list(mlePP@coef[-(j+(tind==TRUE))]),modCI=FALSE,modSim=TRUE, dplot=FALSE)
AICnew[j]<-AIC(aux,...)
}

namcovariates<-dimnames(mlePP@covariates)[[2]]
if (is.null(namcovariates)) namcovariates<- paste('Covariate',c(1:ncov))
AICnew<-matrix(AICnew,ncol=1,dimnames=list(namcovariates,'AIC'))

posminAIC<-which.min(AICnew)

if (modSim==FALSE)
{
cat(fill=T)
cat(' Initial model ', round(AICcurrent,3), fill=TRUE)
cat(' Initial model deleting covariate ',fill=TRUE)
print(round(AICnew,3))
cat(fill=T)
cat('The best covariate to drop is ', namcovariates[posminAIC], fill=TRUE)

}
}
else stop('No test can be carried out since there is only one covariate and no intercept')

return(list(AICdrop=AICnew,posminAIC=posminAIC,namecov=namcovariates[posminAIC], AICcurrent=AICcurrent))

}


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NHPoisson documentation built on Feb. 19, 2020, 5:07 p.m.