Nothing
"predict.glmmNPML" <-
predict.glmmNPML<-function(object, newdata, type="link", ...){
if(missing(newdata)){ #Emp. Bayes Pred. (Aitkin, 96)
if (type=="link") {
return(round(object$ebp,digits=4))
} else {
#rebp <- object$fitted.values
#rebp<- switch(object$family$link, "log"= exp(ebp),
# "logit"=exp(ebp)/(1+exp(ebp)),
# "identity"=ebp,
# "inverse"=1/ebp,
# "probit"= pnorm(ebp,0,1))
return(round(object$fitted.values, digits=4))
}
} else {
m<-length(object$mass.points)
k<-length(object$masses)
Terms<- delete.response(terms(object$formula))
if (k==1){ # GLM
X<-model.matrix(Terms, model.frame(Terms,newdata))
dimnames(X)[[1]]<-dimnames(model.frame(Terms,newdata))[[1]]
pred<- as.vector(X%*%matrix(object$coef[1:dim(X)[2]]))
} else if (k==m){ #Overdispersion model
X<-model.matrix(Terms, model.frame(Terms,newdata))[,-1,drop=FALSE]
dimnames(X)[[1]]<-dimnames(model.frame(Terms,newdata))[[1]]
if (dim(X)[2]!=0){
pred<- as.vector(X%*%matrix(object$coef[1:dim(X)[2]])+ sum(object$masses*object$mass.points))
} else {
pred<- rep(0, dim(X)[1])+ sum(object$masses*object$mass.points)
}
} else { #Random coefficient models
X<-model.matrix(Terms, model.frame(Terms,newdata))[,-1,drop=FALSE]
dimnames(X)[[1]]<-dimnames(model.frame(Terms,newdata))[[1]]
object$mass.points<- ifelse(is.na(object$mass.points),0,object$mass.points)
#r<- names(newdata) %in% gsub('~','',object$random)[2]
r<- names(newdata) %in% object$Misc$mform #28/02/06
if(is.factor(newdata[,r])){newdata[,r]<-as.numeric(newdata[,r])-1} #28/02/06
if (dim(X)[2]!=0){
pred<- as.vector(X%*%matrix(object$coef[1:dim(X)[2]])+ sum(object$masses*object$mass.points[1:k])+ newdata[,r]*sum(object$masses[1:(k-1)]*object$mass.points[(k+1):m])) #24-07-06
} else {
pred<- rep(0, dim(X)[1])+ sum(object$masses*object$mass.points[1:k])+ newdata[,r]*sum(object$masses[1:k]*object$mass.points[(k+1):m])
}
}
if (type=="link"){
rpred<-as.vector(pred)
} else {
rpred <- object$family$linkinv(pred)
}
names(rpred)<-dimnames(X)[[1]]
return(round(rpred,digits=4))
}
}
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