Nothing
"summary.glmmNPML" <-
function(object,digits=max(3,getOption('digits')-3), ...){
np <- length(object$coefficients)
if (is.na(object$coef[length(object$coef)])){np<-np-1} ## Sep 2014
if (np > 0){
m <- seq(1,np)[substr(attr(object$coefficients,'names'),1,4)=='MASS']
mass.points <- object$coefficients[m]
cat('\nCall: ',deparse(object$call),'\n\n')
cat('Coefficients')
cat(":\n")
df.r <- object$lastglm$df.residual
glm.dispersion <- if (any(object$family$family == c("poisson", "binomial")))
1
else if (df.r > 0) {
sum(object$lastglm$weights * object$lastglm$residuals^2, na.rm=TRUE)/df.r
}
else Inf
lastglmsumm <- summary.glm(object$lastglm, dispersion=glm.dispersion)
fitcoef <- matrix(lastglmsumm$coeff[,1:3], ncol=3,dimnames= list(dimnames(lastglmsumm$coef)[[1]], c(dimnames(lastglmsumm$coeff)[[2]][1:2], "t value") )) #03-08-06
print(fitcoef)
} else {
cat('\nCall: ',deparse(object$call),'\n\n')
cat("No coefficients. \n")
}
p <- object$masses
#names(p) <- paste('MASS',seq(1,ncol(object$post.prob)),sep='') # omitted from 0.42 on
dispersion <- 1 # now calculate dispersion in the sense of 'dispersion parameter':
cat('\nMixture proportions:\n')
print.default(format(p,digits),print.gap=2,quote=FALSE)
if (object$family$family=='gaussian'){
dispersion <- (object$sdev$sdev)^2
if (object$Misc$lambda<=1/length(object$masses)){
cat('\nComponent distribution - MLE of sigma:\t ',format(signif(object$sdev$sdev,digits)),'\n')
} else {
cat('\nMLE of component standard deviations:\n'); s<- object$sdev$sdevk; names(s)<- names(p); print.default(format(s,digits),print.gap=2,quote=FALSE); cat ('\n')
}
} else if (object$family$family=='Gamma'){
dispersion <- 1/object$shape$shape
if (object$Misc$lambda<=1/length(object$masses)){cat('\nComponent distribution - MLE of shape parameter:\t ',format(signif(object$shape$shape,digits)),'\n')
} else {
cat('\n MLE of component shape parameters:\n'); s<- object$shape$shapek; names(s)<- names(p); print.default(format(s,digits),print.gap=2,quote=FALSE); cat ('\n')
}
} else cat('\n')
cat('Random effect distribution - standard deviation:\t ', format(object$rsdev),'\n\n') # 03/09/07
cat('-2 log L:\t ',format(round(object$disparity,digits=1)))
if (!is.null(object$post.prob)) cat(' Convergence at iteration ',round(object$EMiter,0))
cat('\n')
if (np >0){
invisible(c("coefficients"=list(fitcoef), object[-1],list(dispersion=dispersion), list(lastglmsumm=lastglmsumm)))
} else {
invisible(c("coefficients"=list(fitcoef), object[-1],list(dispersion=dispersion)) )
}
}
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