Nothing
#' @export
#'
summary.multlcmm <- function(object,...)
{
x <- object
if (!inherits(x, "multlcmm")) stop("use only with \"multlcmm\" objects")
if(inherits(x, "externVar")){
cat("Secondary linear mixed model", "\n")
} else {
cat("General latent class mixed model", "\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$posfix)
cat("Statistical Model:", "\n")
cat(paste(" Dataset:", as.character(as.expression(x$call$data))),"\n")
cat(paste(" Number of subjects:", x$ns),"\n")
cat(paste(" Number of observations:", x$N[9]),"\n")
cat(paste(" Number of latent classes:", x$ng), "\n")
cat(paste(" Number of parameters:", length(x$best))," \n")
if(length(posfix)) cat(paste(" Number of estimated parameters:", length(x$best)-length(posfix))," \n")
ntrtot <- rep(NA,x$N[8])
numSPL <- 0
cat(" Link functions: ")
for (yk in 1:x$N[8])
{
if (x$linktype[yk]==0)
{
ntrtot[yk] <- 2
if (yk>1) cat(" ")
cat("Linear for",x$Ynames[yk]," \n")
}
if (x$linktype[yk]==1)
{
ntrtot[yk] <- 4
if (yk>1) cat(" ")
cat("Standardised Beta CdF for",x$Ynames[yk]," \n")
}
if (x$linktype[yk]==2)
{
numSPL <- numSPL+1
ntrtot[yk] <- x$nbnodes[numSPL]+2
if (yk>1) cat(" ")
cat("Quadratic I-splines with nodes", x$linknodes[1:x$nbnodes[numSPL],yk],"for",x$Ynames[yk], "\n")
}
if (x$linktype[yk]==3)
{
ntrtot[yk] <- x$nbmod[yk]-1
if (yk>1) cat(" ")
cat("Thresholds for",x$Ynames[yk], "\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$conv==12)
{
cat(" The program stopped abnormally. No results can be displayed.\n")
}
else
{
cat(" \n")
if(inherits(x, "externVar")) {
if(x$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$gconv[3],2), "\n")
}
} else {
cat(" Number of iterations: ", x$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("Maximum Likelihood Estimates:", "\n")
cat(" \n")
nprob <- x$N[1]
ncontr <- x$N[2]
nef <- x$N[3]
nvc <- x$N[4]
nw <- x$N[5]
nalea <- x$N[6]
ncor <- x$N[7]
ny <- x$N[8]
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$best) <- names_best
islong <- which(sapply(x$Xnames, nchar)>20)
if(length(islong))
{
x$Xnames[islong] <- sapply(x$Xnames[islong], gsub, pattern="\\(.*\\)", replacement="(...)")
}
}
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$V[indice])
se[which(is.na(se))] <- 1
wald <- x$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))
}
if(nw>0) coef[nef+nvc+1:nw] <- abs(coef[nef+nvc+1:nw])
if(ncor>0) coef[nef+nvc+nw+ncor] <- abs(coef[nef+nvc+nw+ncor])
coef[nef+nvc+nw+ncor+1:ny] <- abs(coef[nef+nvc+nw+ncor+1:ny])
if(nalea>0) coef[nef+nvc+nw+ncor+ny+1:ny] <- abs(coef[nef+nvc+nw+ncor+ny+1:ny])
ow <- options("warn")
options(warn=-1) # to avoid warnings with conv=3
if(x$conv!=2)
{
coefch <- format(as.numeric(sprintf("%.5f",coef)),nsmall=5,scientific=FALSE)
sech <- format(as.numeric(sprintf("%.5f",se)),nsmall=5,scientific=FALSE)
waldch <- format(as.numeric(sprintf("%.3f",wald)),nsmall=3,scientific=FALSE)
pwaldch <- format(as.numeric(sprintf("%.5f",pwald)),nsmall=5,scientific=FALSE)
sech[which(is.na(sqrt(x$V[indice])))] <- NA
waldch[which(is.na(sqrt(x$V[indice])))] <- NA
pwaldch[which(is.na(sqrt(x$V[indice])))] <- NA
}
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)
}
#browser()
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]-2,0)),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")
}
cat("Fixed effects in the longitudinal model:\n" )
tmp <- matrix(c(0,NA,NA,NA),nrow=1,ncol=4)
if ((nef-ncontr-nprob)>0)
{
tmp2 <- cbind(round(coef[(nprob+1):(nef-ncontr)],5),round(se[(nprob+1):(nef-ncontr)],5),round(wald[(nprob+1):(nef-ncontr)],3),round(pwald[(nprob+1):(nef-ncontr)],5))
tmp <- rbind(tmp,tmp2)
}
if (x$ng>1)
{
interc <- "intercept class1 (not estimated)"
}
else
{
interc <- "intercept (not estimated)"
}
if((nef-ncontr)>0) dimnames(tmp) <- list(c(interc,names(coef)[(nprob+1):(nef-ncontr)]), c("coef", "Se", "Wald", "p-value"))
else dimnames(tmp) <- list(interc, c("coef", "Se", "Wald", "p-value"))
cat("\n")
if(ncontr>0)
{
indice2 <- 1:NPM*(1:NPM+1)/2
nom.contr <- x$Xnames[as.logical(x$idcontr0)]
for (i in 1:sum(x$idcontr0))
{
##matrice de variance pour test et se du dernier coef
indtmp <- indice2[(nef-ncontr+(i-1)*(ny-1)+1):(nef-ncontr+i*(ny-1))]
indtmp <- cbind(indtmp-0:(length(indtmp)-1),indtmp)
indV <- NULL
for (j in 1:dim(indtmp)[1])
{
indV <- c(indV,seq(indtmp[j,1],indtmp[j,2]))
}
Vcontr <- matrix(0,ny-1,ny-1)
Vcontr[upper.tri(Vcontr,diag=TRUE)] <- x$V[indV]
Vcontr <- t(Vcontr)
Vcontr[upper.tri(Vcontr,diag=TRUE)] <- x$V[indV]
vect.gamma <- coef[(nef-ncontr+(i-1)*(ny-1)+1):(nef-ncontr+i*(ny-1))]
if(any(c((nef-ncontr+(i-1)*(ny-1)+1):(nef-ncontr+i*(ny-1))) %in% posfix))
{
wald.contr <- NA
p.wald.contr <- NA
}
else
{
wald.contr <- t(vect.gamma) %*% solve(Vcontr,vect.gamma)
p.wald.contr <- 1-pchisq(wald.contr,ny-1)
}
tmp2 <- cbind(round(vect.gamma,5),round(se[(nef-ncontr+(i-1)*(ny-1)+1):(nef-ncontr+i*(ny-1))],5),round(wald[(nef-ncontr+(i-1)*(ny-1)+1):(nef-ncontr+i*(ny-1))],3),round(pwald[(nef-ncontr+(i-1)*(ny-1)+1):(nef-ncontr+i*(ny-1))],5))
tmp2 <- rbind(rep(NA,4),tmp2)
if(x$conv %in% c(1,3))
{
pp <- -sum(na.omit(tmp2[,1]))/sqrt(sum(Vcontr))
tmp2 <- rbind(tmp2,c(round(-sum(na.omit(tmp2[,1])),5),round(sqrt(sum(Vcontr)),5),round(pp,3),round(1-pchisq(pp*pp,1),5)))
if(is.na(p.wald.contr)) rownames(tmp2) <- c(paste("Contrasts on ",nom.contr[i],sep=""),x$Ynames)
else
{
if(round(p.wald.contr,5)!=0) rownames(tmp2) <- c(paste("Contrasts on ",nom.contr[i]," (p=",round(p.wald.contr,5),")",sep=""),x$Ynames)
if(round(p.wald.contr,5)==0) rownames(tmp2) <- c(paste("Contrasts on ",nom.contr[i]," (p<0.00001)",sep=""),x$Ynames)
}
}
if(x$conv==2)
{
tmp2 <- rbind(tmp2,c(-sum(na.omit(tmp2[,1])),NA,NA,NA))
rownames(tmp2) <- c(paste("Contrasts on ",nom.contr[i],sep=""),x$Ynames)
}
rownames(tmp2)[nrow(tmp2)] <- paste(rownames(tmp2)[nrow(tmp2)],"**",sep="")
if(!is.finite(tmp2[nrow(tmp2),3])) tmp2[nrow(tmp2),2:4] <- NA
tmp <- rbind(tmp,tmp2)
}
}
tTable <- tmp
if(nef>0 & any(c((nprob+1):nef) %in% posfix))
{
col1 <- rep(NA,length(tmp[,1]))
col1[which(!is.na(tmp[,1]))] <- format(as.numeric(sprintf("%.5f",na.omit(tmp[,1]))),nsmall=5,scientific=FALSE)
col2 <- rep(NA,length(tmp[,2]))
col2[which(!is.na(tmp[,2]))] <- format(as.numeric(sprintf("%.5f",na.omit(tmp[,2]))),nsmall=5,scientific=FALSE)
col3 <- rep(NA,length(tmp[,3]))
col3[which(!is.na(tmp[,3]))] <- format(as.numeric(sprintf("%.3f",na.omit(tmp[,3]))),nsmall=3,scientific=FALSE)
col4 <- rep(NA,length(tmp[,4]))
col4[which(!is.na(tmp[,4]))] <- format(as.numeric(sprintf("%.5f",na.omit(tmp[,4]))),nsmall=5,scientific=FALSE)
pf <- sort(intersect(c((nprob+1):nef),posfix))
p <- rep(0,length(tmp[,1]))
p[which(rownames(tmp) %in% c(names(x$best)[(nprob+1):nef],x$Ynames[-length(x$Ynames)]))] <- c((nprob+1):nef)
col1[which(p %in% pf)] <- paste(col1[which(p %in% pf)],"*",sep="")
col2[which(p %in% pf)] <- NA
col3[which(p %in% pf)] <- NA
col4[which(p %in% pf)] <- NA
tmp <- cbind(col1,col2,col3,col4)
rownames(tmp) <- rownames(tTable)
maxch <- apply(tmp,2,maxchar)
maxch[1] <- maxch[1]-1
colnames(tmp) <- c(paste(paste(rep(" ",max(maxch[1]-4,0)),collapse=""),"coef",sep=""),
paste(paste(rep(" ",max(maxch[2]-2,0)),collapse=""),"Se",sep=""),
paste(paste(rep(" ",max(maxch[3]-4,0)),collapse=""),"Wald",sep=""),
paste(paste(rep(" ",max(maxch[4]-7,0)),collapse=""),"p-value",sep=""))
print(tmp,quote=FALSE,na.print="")
cat("\n")
}
else
{
prmatrix(round(tmp,5),na.print="")
cat("\n")
}
cat("\n")
cat("Variance-covariance matrix of the random-effects:\n" )
cat("(the variance of the first random effect is not estimated)\n")
if(x$idiag==1)
{
if (nvc>0)
{
Mat.cov <- diag(c(1,coef[(nef+1):(nef+nvc)]))
}
else
{
Mat.cov <- matrix(1,ncol=1)
}
Mat.cov[lower.tri(Mat.cov)] <- 0
Mat.cov[upper.tri(Mat.cov)] <- NA
}
if(x$idiag==0)
{
Mat.cov<-matrix(0,ncol=sum(x$idea0),nrow=sum(x$idea0))
if(nvc>0)
{
Mat.cov[upper.tri(Mat.cov,diag=TRUE)]<-c(1,coef[(nef+1):(nef+nvc)])
Mat.cov <-t(Mat.cov)
Mat.cov[upper.tri(Mat.cov)] <- NA
}
else Mat.cov[1,1] <- 1
}
colnames(Mat.cov) <-x$Xnames[x$idea0==1]
rownames(Mat.cov) <-x$Xnames[x$idea0==1]
if(any(posfix %in% c(nef+1:nvc)))
{
Mat.cov <- apply(Mat.cov,2,format,digits=5,nsmall=5)
Mat.cov[upper.tri(Mat.cov)] <- ""
pf <- sort(intersect(c(nef+1:nvc),posfix))
p <- matrix(0,sum(x$idea),sum(x$idea))
if(x$idiag==FALSE) p[upper.tri(p,diag=TRUE)] <- c(0,nef+1:nvc)
if(x$idiag==TRUE) diag(p) <- c(0,nef+1:nvc)
Mat.cov[which(t(p) %in% pf)] <- paste(Mat.cov[which(t(p) %in% pf)],"*",sep="")
print(Mat.cov,quote=FALSE)
}
else
{
prmatrix(round(Mat.cov,5),na.print="")
}
cat("\n")
std <- NULL
nom <- NULL
if(nw>=1)
{
nom <- paste("Proportional coefficient class",c(1:(x$ng-1)),sep="")
std <-cbind(coefch[nef+nvc+1:nw],sech[nef+nvc+1:nw])
}
if(ncor==2)
{
nom <- c(nom,"AR correlation parameter:","AR standard error:")
std <-rbind(std,c(coefch[nef+nvc+nw+1],sech[nef+nvc+nw+1]),c(coefch[nef+nvc+nw+2],sech[nef+nvc+nw+2]))
}
if(ncor==1)
{
nom <- c(nom,"BM standard error:")
std <-rbind(std,c(coefch[nef+nvc+nw+1],sech[nef+nvc+nw+1]))
}
if (!is.null(std))
{
rownames(std) <- nom
maxch <- apply(std,2,maxchar)
if(any(c(nef+nvc+1:(nw+ncor)) %in% posfix)) maxch[1] <- maxch[1]-1
colnames(std) <- c(paste(paste(rep(" ",max(maxch[1]-4,0)),collapse=""),"coef",sep=""),
paste(paste(rep(" ",max(maxch[2]-2,0)),collapse=""),"Se",sep=""))
print(std,quote=FALSE,na.print="")
cat("\n")
}
std.err <- NULL
nom <- NULL
std.err <- rbind(std.err,coefch[nef+nvc+nw+ncor+1:ny])
nom <- c(nom, "Residual standard error:")
if(nalea>0)
{
std.err <- rbind(std.err,coefch[nef+nvc+nw+ncor+ny+1:nalea])
nom <- c(nom, "Standard error of the random effect:")
}
rownames(std.err) <- nom
maxch <- apply(std.err,2,maxchar)
if(any(c(nef+nvc+nw+ncor+1:(ny+nalea)) %in% posfix))
{
if(nalea>0)
{
maxch[union(grep("*",std.err[1,]),grep("*",std.err[2,]))] <- maxch[union(grep("*",std.err[1,]),grep("*",std.err[2,]))]-1
}
else
{
maxch[grep("*",std.err[1,])] <- maxch[grep("*",std.err[1,])]-1
}
}
colnames(std.err) <- sapply(1:ny,function(k) paste(paste(rep(" ",max(0,maxch[k]-maxchar(x$Ynames[k]))),collapse=""),x$Ynames[k],sep=""))
print(std.err,quote=FALSE,na.print="")
cat("\n")
cat("Parameters of the link functions:\n" )
tmp <- cbind(coefch[(nef+nvc+nw+ncor+ny+nalea+1):NPM],
sech[(nef+nvc+nw+ncor+ny+nalea+1):NPM],
waldch[(nef+nvc+nw+ncor+ny+nalea+1):NPM],
pwaldch[(nef+nvc+nw+ncor+ny+nalea+1):NPM])
tmp.rownames <- NULL
for (yk in 1:ny)
{
tmp.rownames <- c(tmp.rownames, paste(rep(x$Ynames[yk],ntrtot[yk]),names(coef[(nef+nvc+nw+ncor+ny+nalea+sum(ntrtot[1:yk])-ntrtot[yk]+1):(nef+nvc+nw+ncor+ny+nalea+sum(ntrtot[1:yk]))]),sep="-"))
}
rownames(tmp) <- tmp.rownames
maxch <- apply(tmp,2,maxchar)
if(any(c((nef+nvc+nw+ncor+ny+nalea+1):NPM) %in% posfix)) maxch[1] <- maxch[1]-1
colnames(tmp) <- c(paste(paste(rep(" ",max(maxch[1]-4,0)),collapse=""),"coef",sep=""),
paste(paste(rep(" ",max(maxch[2]-2,0)),collapse=""),"Se",sep=""),
paste(paste(rep(" ",max(maxch[3]-4,0)),collapse=""),"Wald",sep=""),
paste(paste(rep(" ",max(maxch[4]-7,0)),collapse=""),"p-value",sep=""))
if(inherits(x, "externVar")) colnames(tmp)[2] = paste(paste(rep(" ",max(maxch[2]-5,0)),collapse=""),"Se***",sep="")
cat("\n")
print(tmp,quote=FALSE,na.print="")
cat("\n")
if(length(posfix))
{
cat(" * coefficient fixed by the user \n \n")
}
if(ncontr>0)
{
cat(" ** coefficient not estimated but obtained from the others as minus the sum of them \n \n")
}
if(inherits(x, "externVar")){
if(x$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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