Nothing
#' @export
#'
summary.Jointlcmm <- function(object,...)
{
x <- object
if (!inherits(x, "Jointlcmm")) stop("use only with \"Jointlcmm\" objects")
cat("Joint latent class model for quantitative outcome and competing risks", "\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")
nbevt <- length(x$hazard[[1]])
nprisq <- rep(NA,nbevt)
nrisq <- rep(NA,nbevt)
typrisq <- x$hazard[[1]]
hazardtype <- x$hazard[[2]]
nz <- x$hazard[[4]]
for(ke in 1:nbevt)
{
if(typrisq[ke]==1) nprisq[ke] <- nz[ke]-1
if(typrisq[ke]==2) nprisq[ke] <- 2
if(typrisq[ke]==3) nprisq[ke] <- nz[ke]+2
if(hazardtype[ke]=="Common") nrisq[ke] <- nprisq[ke]
if(hazardtype[ke]=="PH") nrisq[ke] <- nprisq[ke]+x$ng-1
if(hazardtype[ke]=="Specific") nrisq[ke] <- nprisq[ke]*x$ng
cat(paste(" Event ",ke,": \n",sep=""))
cat(paste(" Number of events: ", x$N[9+ke],"\n",sep=""))
if(x$ng>1)
{
if (hazardtype[ke]=="Specific") cat(" Class-specific hazards and \n")
if (hazardtype[ke]=="PH") cat(" Proportional hazards over latent classes and \n")
if (hazardtype[ke]=="Common") cat(" Common hazards over classes and \n")
}
if (typrisq[ke]==2)
{
cat(" Weibull baseline risk function \n")
}
if (typrisq[ke]==1)
{
cat(" Piecewise constant baseline risk function with nodes \n")
cat(" ",x$hazard[[3]][1:nz[ke],ke]," \n")
}
if (typrisq[ke]==3)
{
cat(" M-splines constant baseline risk function with nodes \n")
cat(" ",x$hazard[[3]][1:nz[ke],ke]," \n")
}
}
ntrtot <- x$N[8]
numSPL <- 0
if(x$linktype!=-1)
{
cat(paste(" Link function for ",x$Names$Yname,": ",sep=""))
if (x$linktype==0)
{
cat("Linear \n")
}
if (x$linktype==1)
{
cat("Standardised Beta CdF \n")
}
if (x$linktype==2)
{
cat("Quadratic I-splines with nodes ", x$linknodes ,"\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$conv==12)
{
cat(" The program stopped abnormally. No results can be displayed.\n")
}
else
{
cat(" \n")
cat(" Number of iterations: ", x$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")
if(!is.na(x$scoretest[1])&(length(x$hazard[[1]])==1)){
cat(paste(" Score test statistic for CI assumption: ", round(x$scoretest[1],3)," (p-value=",round((1-pchisq(x$scoretest[1],sum(x$idea))),4),")" ,sep=""))
}
if(!is.na(x$scoretest[1])&(length(x$hazard[[1]])>1)){
cat(paste(" Score test statistic for global CI assumption: ", round(x$scoretest[1],3)," (p-value=",round((1-pchisq(x$scoretest[1],sum(x$idea))),4),")" ,sep=""),"\n")
}
if(!is.na(x$scoretest[1])&(length(x$hazard[[1]])>1)){
cat(" Score test statistic for event-specific CI assumption: \n")
for (ke in 1:length(x$hazard[[1]])){
if(!is.na(x$scoretest[1+ke])){
cat(paste(" event ",ke,":", round(x$scoretest[1+ke],3)," (p-value=",round((1-pchisq(x$scoretest[1+ke],sum(x$idea))),4),")" ,sep=""),"\n")
}
else{
cat(paste(" event ",ke,": problem in the computation", "\n"))
}
}
}
cat(" \n")
cat(" \n")
cat("Maximum Likelihood Estimates:", "\n")
cat(" \n")
nprob <- x$N[1]
nrisqtot <- x$N[2]
nvarxevt <- x$N[3]
nef <- x$N[4]
nvc <- x$N[5]
nw <- x$N[6]
ncor <- x$N[7]
ntrtot <- x$N[8]
NPM <- length(x$best)
#nvdepsurv <- length(x$Name$TimeDepVar.name)
## shorten names if > 20 characters
names_best <- names(x$best)
if(any(sapply(names_best, nchar)>20))
{
islong <- which(sapply(names_best, nchar)>20)
split_names_best <- strsplit(names_best, split=":", fixed=TRUE)
short_names_best <- lapply(split_names_best, gsub, pattern="\\(.*\\)", replacement="(...)")
new_names <- lapply(short_names_best, paste, collapse=":")
names_best[islong] <- unlist(new_names)[islong]
names_best[nprob+1:nrisqtot] <- names(x$best)[nprob+1:nrisqtot]
names(x$best) <- names_best
islong <- which(sapply(x$Names$Xnames, nchar)>20)
if(length(islong))
{
x$Names$Xnames[islong] <- sapply(x$Names$Xnames[islong], gsub, pattern="\\(.*\\)", replacement="(...)")
}
}
se <- rep(NA,NPM)
if (x$conv==1 | x$conv==3)
{
##recuperation des indices de V
id <- 1:NPM
indice <- id*(id+1)/2
se <-sqrt(x$V[indice])
wald <- x$best/se
pwald <- 1-pchisq(wald**2,1)
coef <- x$best
}
else
{
se <- NA
wald <- NA
pwald <- NA
coef <- x$best
sech <- rep(NA,length(coef))
waldch <- rep(NA,length(coef))
pwaldch <- rep(NA,length(coef))
}
if(nw>0) coef[nprob+nrisqtot+nvarxevt+nef+nvc+1:nw] <- abs(coef[nprob+nrisqtot+nvarxevt+nef+nvc+1:nw])
if(ncor>0) coef[nprob+nrisqtot+nvarxevt+nef+nvc+nw+ncor] <- abs(coef[nprob+nrisqtot+nvarxevt+nef+nvc+nw+ncor])
if(ntrtot==1) coef[nprob+nrisqtot+nvarxevt+nef+nvc+nw+ncor+1] <- abs(coef[nprob+nrisqtot+nvarxevt+nef+nvc+nw+ncor+1])
ow <- options("warn")
options(warn=-1) # to avoid warnings with conv=3
if(x$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]-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("Parameters in the proportional hazard model:\n" )
tmp <- cbind(coefch[nprob+1:(nrisqtot+nvarxevt)],
sech[nprob+1:(nrisqtot+nvarxevt)],
waldch[nprob+1:(nrisqtot+nvarxevt)],
pwaldch[nprob+1:(nrisqtot+nvarxevt)])
maxch <- apply(tmp,2,maxchar)
if(any(c(nprob+1:(nrisqtot+nvarxevt)) %in% posfix)) maxch[1] <- maxch[1]-1
dimnames(tmp) <- list(names(coef)[nprob+1:(nrisqtot+nvarxevt)],
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" )
if(x$linktype!=-1)
{
tmp <- matrix(c(paste(c(rep(" ",maxchar(coefch[nprob+nrisqtot+nvarxevt+1:nef])-ifelse(any(c(nprob+nrisqtot+nvarxevt+1:nef) %in% posfix),2,1)),0),collapse=""),"","",""),nrow=1,ncol=4)
tTable <- matrix(c(0,NA,NA,NA),nrow=1,ncol=4)
}
if(x$linktype==-1)
{
tmp <- NULL
tTable <-NULL
}
if (nef>0)
{
tmp2 <- cbind(coefch[nprob+nrisqtot+nvarxevt+1:nef],
sech[nprob+nrisqtot+nvarxevt+1:nef],
waldch[nprob+nrisqtot+nvarxevt+1:nef],
pwaldch[nprob+nrisqtot+nvarxevt+1:nef])
tmp <- rbind(tmp,tmp2)
tTable <- rbind(tTable,cbind(round(coef[nprob+nrisqtot+nvarxevt+1:nef],5),
round(se[nprob+nrisqtot+nvarxevt+1:nef],5),
round(wald[nprob+nrisqtot+nvarxevt+1:nef],3),
round(pwald[nprob+nrisqtot+nvarxevt+1:nef],5)))
}
interc <- "intercept"
if (x$ng>1)
{
interc <- paste(interc,"class1")
}
if(x$linktype!=-1) interc <- paste(interc,"(not estimated)")
if(x$linktype==-1) interc <- NULL
if(nef>0)
{
maxch <- apply(tmp,2,maxchar)
if(any(c(nprob+nrisqtot+nvarxevt+1:nef) %in% posfix)) maxch[1] <- maxch[1]-1
dimnames(tmp) <- list(c(interc,names(coef)[nprob+nrisqtot+nvarxevt+1:nef]),
c(paste(paste(rep(" ",max(maxch[1]-4,0)),collapse=""),"coef",sep=""),
paste(paste(rep(" ",max(maxch[2]-2,0)),collapse=""),"Se",sep=""),
paste(paste(rep(" ",max(maxch[3]-4,0)),collapse=""),"Wald",sep=""),
paste(paste(rep(" ",max(maxch[4]-7,0)),collapse=""),"p-value",sep="")))
}
else
{
dimnames(tmp) <- list(interc, c("coef", "Se", "Wald", "p-value"))
}
rownames(tTable) <- rownames(tmp)
colnames(tTable) <- c("coef", "Se", "Wald", "p-value")
cat("\n")
print(tmp,quote=FALSE,na.print="")
cat("\n")
if(nvc>0)
{
cat("\n")
cat("Variance-covariance matrix of the random-effects:\n" )
if(x$idiag==1)
{
Mat.cov <- diag(coef[nprob+nrisqtot+nvarxevt+nef+1:nvc])
Mat.cov[lower.tri(Mat.cov)] <- 0
Mat.cov[upper.tri(Mat.cov)] <- NA
if(nvc==1) Mat.cov <- matrix(coef[nprob+nrisqtot+nvarxevt+nef+1:nvc],1,1)
}
if(x$idiag==0)
{
Mat.cov<-matrix(0,ncol=sum(x$idea),nrow=sum(x$idea))
Mat.cov[upper.tri(Mat.cov,diag=TRUE)] <- coef[nprob+nrisqtot+nvarxevt+nef+1:nvc]
Mat.cov <-t(Mat.cov)
Mat.cov[upper.tri(Mat.cov)] <- NA
}
colnames(Mat.cov) <-x$Names$Xnames[x$idea==1]
rownames(Mat.cov) <-x$Names$Xnames[x$idea==1]
if(any(posfix %in% c(nprob+nrisqtot+nvarxevt+nef+1:nvc)))
{
Mat.cov <- apply(Mat.cov,2,format,digits=5,nsmall=5)
Mat.cov[upper.tri(Mat.cov)] <- ""
pf <- sort(intersect(c(nprob+nrisqtot+nvarxevt+nef+1:nvc),posfix))
p <- matrix(0,sum(x$idea),sum(x$idea))
if(x$idiag==FALSE) p[upper.tri(p,diag=TRUE)] <- c(nprob+nrisqtot+nvarxevt+nef+1:nvc)
if(x$idiag==TRUE & nvc>1) diag(p) <- c(nprob+nrisqtot+nvarxevt+nef+1:nvc)
if(x$idiag==TRUE & nvc==1) p <- matrix(c(nprob+nrisqtot+nvarxevt+nef+1),1,1)
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$ng-1)),sep="")
std <-cbind(coefch[nprob+nrisqtot+nvarxevt+nef+nvc+1:nw],
sech[nprob+nrisqtot+nvarxevt+nef+nvc+1:nw])
}
if(ncor==2)
{
nom <- c(nom,"AR correlation parameter:","AR standard error:")
std <-rbind(std,c(coefch[nprob+nrisqtot+nvarxevt+nef+nvc+nw+1],
sech[nprob+nrisqtot+nvarxevt+nef+nvc+nw+1]),
c(coefch[nprob+nrisqtot+nvarxevt+nef+nvc+nw+2],
sech[nprob+nrisqtot+nvarxevt+nef+nvc+nw+2]))
}
if(ncor==1)
{
nom <- c(nom,"BM standard error:")
std <-rbind(std,c(coefch[nprob+nrisqtot+nvarxevt+nef+nvc+nw+1],
sech[nprob+nrisqtot+nvarxevt+nef+nvc+nw+1]))
}
if (!is.null(std))
{
rownames(std) <- nom
maxch <- apply(std,2,maxchar)
if(any(c(nprob+nrisqtot+nvarxevt+nef+nvc+1:(nw+ncor)) %in% posfix)) maxch[1] <- maxch[1]-1
colnames(std) <- c(paste(paste(rep(" ",max(maxch[1]-4,0)),collapse=""),"coef",sep=""),
paste(paste(rep(" ",max(maxch[2]-2,0)),collapse=""),"Se",sep=""))
print(std,quote=FALSE,na.print="")
cat("\n")
}
if(x$linktype==-1)
{
tmp <- cbind(coefch[NPM],sech[NPM])
rownames(tmp) <- "Residual standard error"
maxch <- apply(tmp,2,maxchar)
if(c(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=""))
print(tmp,quote=FALSE,na.print="")
cat("\n")
}
else
{
cat("Residual standard error (not estimated) = 1\n")
cat("\n")
cat("Parameters of the link function:\n" )
tmp <- cbind(coefch[(nprob+nrisqtot+nvarxevt+nef+nvc+nw+ncor+1):NPM],
sech[(nprob+nrisqtot+nvarxevt+nef+nvc+nw+ncor+1):NPM],
waldch[(nprob+nrisqtot+nvarxevt+nef+nvc+nw+ncor+1):NPM],
pwaldch[(nprob+nrisqtot+nvarxevt+nef+nvc+nw+ncor+1):NPM])
rownames(tmp) <- names(x$best[(nprob+nrisqtot+nvarxevt+nef+nvc+nw+ncor+1):NPM])
maxch <- apply(tmp,2, maxchar)
if(any(c((nprob+nrisqtot+nvarxevt+nef+nvc+nw+ncor+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=""))
cat("\n")
print(tmp,quote=FALSE,na.print="")
cat("\n")
}
if(length(posfix))
{
cat(" * coefficient fixed by the user \n \n")
}
return(invisible(tTable))
}
}
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