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#' @export
#'
summary.externX = function(object, ...){
x = object
if(!inherits(x, "externX")) stop("use only with\"externX\" objects")
cat("Secondary multinomial model for external class predictor", "\n")
cat(" fitted by maximum likelihood method", "\n")
cl <- x$call
cl$B <- NULL
if(is.data.frame(cl$data))
{
cl$data <- NULL
x$call$data <- NULL
}
cat(" \n")
dput(cl)
cat(" \n")
posfix <- eval(cl$posfix)
cat("Statistical Model:", "\n")
cat(paste(" Dataset:", x$call$data),"\n")
cat(paste(" Number of subjects:", x$ns),"\n")
cat(paste(" Number of latent classes:", x$ng), "\n")
cat(paste(" Number of parameters:", length(x$best))," \n")
cat(" \n")
cat("Iteration process:", "\n")
if(x$conv==1) cat(" Convergence criteria satisfied")
if(x$conv==2) cat(" Maximum number of iteration reached without convergence")
if(x$conv==3) cat(" Convergence with restrained Hessian matrix")
if(x$conv==4|x$conv==12)
{
cat(" The program stopped abnormally. No results can be displayed.\n")
}
else
{
cat(" \n")
if(x$varest == "paramBoot") {
cat(" Proportion of convergence on bootstrap iterations (%)=", x$Mconv, "\n")
} else {
cat(" Number of iterations: ", x$niter, "\n")
cat(" Convergence criteria: parameters=", signif(x$gconv[1],2), "\n")
cat(" : likelihood=", signif(x$gconv[2],2), "\n")
cat(" : second derivatives=", signif(x$gconv[3],2), "\n")
}
cat(" \n")
cat("Goodness-of-fit statistics:", "\n")
cat(paste(" maximum log-likelihood:", round(x$loglik,2))," \n")
cat(paste(" AIC:", round(x$AIC,2))," \n")
cat(paste(" BIC:", round(x$BIC,2))," \n")
cat(" \n")
cat(" \n")
cat(" \n")
cat("Maximum Likelihood Estimates:", "\n")
cat(" \n")
nprob <- x$N[1]
NPM <- length(x$best)
## shorten names if > 20 characters
names_best <- names(x$best)
if(any(sapply(names_best, nchar)>20))
{
islong <- which(sapply(names_best, nchar)>20)
split_names_best <- strsplit(names_best, split=":", fixed=TRUE)
short_names_best <- lapply(split_names_best, gsub, pattern="\\(.*\\)", replacement="(...)")
new_names <- lapply(short_names_best, paste, collapse=":")
names_best[islong] <- unlist(new_names)[islong]
names(x$best) <- names_best
}
se <- rep(NA,NPM)
if (x$conv==1 | x$conv==3)
{
##recuperation des indices de V
id <- 1:NPM
indice <- id*(id+1)/2
se <- sqrt(x$V[indice])
wald <- x$best/se
pwald <- 1-pchisq(wald**2,1)
coef <- x$best
}
else
{
se <- NA
wald <- NA
pwald <- NA
coef <- x$best
sech <- rep(NA,length(coef))
waldch <- rep(NA,length(coef))
pwaldch <- rep(NA,length(coef))
}
ow <- options("warn")
options(warn=-1) # to avoid warnings with conv=3
if(x$conv!=2)
{
coefch <- format(as.numeric(sprintf("%.5f",coef)),nsmall=5,scientific=FALSE)
sech <- format(as.numeric(sprintf("%.5f",se)),nsmall=5,scientific=FALSE)
waldch <- format(as.numeric(sprintf("%.3f",wald)),nsmall=3,scientific=FALSE)
pwaldch <- format(as.numeric(sprintf("%.5f",pwald)),nsmall=5,scientific=FALSE)
}
else
{
coefch <- format(as.numeric(sprintf("%.5f",coef)),nsmall=5,scientific=FALSE)
}
options(ow)
if(length(posfix))
{
coefch[posfix] <- paste(coefch[posfix],"*",sep="")
sech[posfix] <- ""
waldch[posfix] <- ""
pwaldch[posfix] <- ""
}
## fct pr determiner la longueur max d'une chaine de caracteres
## (avec gestion des NA)
maxchar <- function(x)
{
xx <- na.omit(x)
if(length(xx))
{
res <- max(nchar(xx))
}
else
{
res <- 2
}
return(res)
}
if(nprob>0)
{
cat("Fixed effects in the class-membership model:\n" )
cat("(the class of reference is the last class) \n")
tmp <- cbind(coefch[1:nprob],sech[1:nprob],waldch[1:nprob],pwaldch[1:nprob])
maxch <- apply(tmp,2,maxchar)
if(any(c(1:nprob) %in% posfix)) maxch[1] <- maxch[1]-1
dimnames(tmp) <- list(names(coef)[1:nprob],
c(paste(paste(rep(" ",max(maxch[1]-4,0)),collapse=""),"coef",sep=""),
paste(paste(rep(" ",max(maxch[2]-4,0)),collapse=""),"Se**",sep=""),
paste(paste(rep(" ",max(maxch[3]-4,0)),collapse=""),"Wald",sep=""),
paste(paste(rep(" ",max(maxch[4]-7,0)),collapse=""),"p-value",sep="")))
cat("\n")
print(tmp,quote=FALSE,na.print="")
cat("\n")
}
tTable = tmp
if(length(posfix))
{
cat(" * coefficient fixed by the user \n")
}
if(x$varest == "none") cat(" ** total variance estimated witout correction for primary model uncertainty", "\n \n")
if(x$varest == "Hessian") cat(" ** total variance estimated through the Hessian of the joint likelihood", "\n \n")
if(x$varest == "paramBoot") cat(" ** total variance estimated through parametric bootstrap", "\n \n")
return(invisible(tTable))
}
}
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