#' @export
#'
summary.mpjlcmm <- function(object,...)
{
x <- object
cat("Multivariate 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$posfix)
nbevt <- x$nbevt
K <- x$K
cat("Statistical Model:", "\n")
cat(paste(" Dataset:", x$call$data),"\n")
cat(paste(" Number of subjects:", x$ns),"\n")
cat(paste(" Number of longitudinal models:", x$K),"\n")
cat(paste(" Number of observations:", paste(x$N[11+1:K],collapse=" ")),"\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$best)-length(posfix))," \n")
if(nbevt>0)
{
nprisq <- rep(NA,nbevt)
nrisq <- rep(NA,nbevt)
for(ke in 1:nbevt)
{
if(x$typrisq[ke]==1) nprisq[ke] <- x$nz[ke]-1
if(x$typrisq[ke]==2) nprisq[ke] <- 2
if(x$typrisq[ke]==3) nprisq[ke] <- x$nz[ke]+2
nrisq[ke] <- x$Nprm[1+ke]
cat(paste(" Event",ke,": \n"))
cat(paste(" Number of events: ", x$N[11+K+ke],"\n",sep=""))
if(x$ng>1)
{
if (x$hazardtype[ke]=="Specific") cat(" Class-specific hazards and \n")
if (x$hazardtype[ke]=="PH") cat(" Proportional hazards over latent classes and \n")
if (x$hazardtype[ke]=="Common") cat(" Common hazards over classes and \n")
}
if (x$typrisq[ke]==2)
{
cat(" Weibull baseline risk function \n")
}
if (x$typrisq[ke]==1)
{
cat(" Piecewise constant baseline risk function with nodes \n")
cat(" ",x$hazardnodes[1:x$nz[ke],ke]," \n")
}
if (x$typrisq[ke]==3)
{
cat(" M-splines constant baseline risk function with nodes \n")
cat(" ",x$hazardnodes[1:x$nz[ke],ke]," \n")
}
}
}
ny <- x$ny
Ynames <- x$Names$Yname
if(any(x$linktype!=-1))
{
cat(" Link functions: ")
for (yk in 1:sum(x$ny))
{
if (x$linktype[yk]==0)
{
if (yk>1) cat(" ")
cat("Linear for",Ynames[yk]," \n")
}
if (x$linktype[yk]==1)
{
if (yk>1) cat(" ")
cat("Standardised Beta CdF for",Ynames[yk]," \n")
}
if (x$linktype[yk]==2)
{
if (yk>1) cat(" ")
cat("Quadratic I-splines with nodes", x$linknodes[1:x$nbzitr[yk],yk]," for ",Ynames[yk], "\n")
}
}
}
cat(" \n")
cat("Iteration process:", "\n")
if(x$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")
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]
nrisqtot <- x$N[2]
nvarxevt <- x$N[3]
l <- 3
if(nbevt>1) l <- 2+nbevt
nef <- x$Nprm[l+1:K]
ncontr <- x$Nprm[l+K+1:K]
nvc <- x$Nprm[l+2*K+1:K]
nw <- x$Nprm[l+3*K+1:K]
ncor <- x$Nprm[l+4*K+1:K]
nerr <- x$Nprm[l+5*K+1:K]
nalea <- x$Nprm[l+6*K+1:K]
ntr <- x$Nprm[l+7*K+1:sum(x$ny)]
NPM <- length(x$best)
## shorten names if > 20 characters
names_best <- names(x$best)
if(any(sapply(names_best, nchar)>20))
{
islong <- which(sapply(names_best, nchar)>20)
split_names_best <- strsplit(names_best, split=":", fixed=TRUE)
short_names_best <- lapply(split_names_best, gsub, pattern="\\(.*\\)", replacement="(...)")
new_names <- lapply(short_names_best, paste, collapse=":")
names_best[islong] <- unlist(new_names)[islong]
if(nrisqtot>0) names_best[nprob+1:nrisqtot] <- names(x$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$best
sech <- rep(NA,length(coef))
waldch <- rep(NA,length(coef))
pwaldch <- rep(NA,length(coef))
}
ow <- options("warn")
options(warn=-1) # to avoid warnings with conv=3
if(x$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")
}
if(nbevt>0)
{
cat("\n")
cat("Parameters in the proportional hazard model:\n" )
cat("\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")
}
tTable <- vector("list",K)
sumny <- 0
sumnpm <- 0
sumnv <- 0
for (k in 1:K)
{
sumntr <- 0
cat("\n")
cat("Longitudinal model for",paste(Ynames[sumny+1:ny[k]],collapse="/"),":\n" )
cat("\n")
cat("Fixed effects in the longitudinal model:\n" )
if (x$ng>1)
{
interc <- "intercept class1"
}
else
{
interc <- "intercept"
}
if(x$contrainte[k]!=0)
{
tmp <- matrix(c(0,NA,NA,NA),nrow=1,ncol=4)
interc <- paste(interc, "(not estimated)")
}
else
{
interc <- NULL
tmp <- NULL
}
if(nef[k]>0)
{
tmp2 <- cbind(round(coef[nprob+nrisqtot+nvarxevt+sumnpm+1:nef[k]],5),
round(se[nprob+nrisqtot+nvarxevt+sumnpm+1:nef[k]],5),
round(wald[nprob+nrisqtot+nvarxevt+sumnpm+1:nef[k]],3),
round(pwald[nprob+nrisqtot+nvarxevt+sumnpm+1:nef[k]],5))
tmp <- rbind(tmp,tmp2)
dimnames(tmp) <- list(c(interc,names(coef)[nprob+nrisqtot+nvarxevt+sumnpm+1:nef[k]]), c("coef", "Se", "Wald", "p-value"))
}
else
{
dimnames(tmp) <- list(interc, c("coef", "Se", "Wald", "p-value"))
}
if(ncontr[k]>0)
{
indice2 <- 1:NPM*(1:NPM+1)/2
nom.contr <- x$Names$Xnames[sumnv+as.logical(x$idcontr[sumnv+1:x$nv[k]])]
for (i in 1:sum(x$idcontr[sumnv+1:x$nv[k]]))
{
##matrice de variance pour test et se du dernier coef
indtmp <- indice2[nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+((i-1)*(ny[k]-1)+1):(i*(ny[k]-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[k]-1,ny[k]-1)
Vcontr[upper.tri(Vcontr,diag=TRUE)] <- x$V[indV]
Vcontr <- t(Vcontr)
Vcontr[upper.tri(Vcontr)] <- Vcontr[lower.tri(Vcontr)]
vect.gamma <- coef[nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+((i-1)*(ny[k]-1)+1):(i*(ny[k]-1))]
if(any(c(nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+((i-1)*(ny[k]-1)+1):(i*(ny[k]-1))) %in% posfix))
{
wald.contr <- NA
p.wald.contr <- NA
}
else
{
wald.contr <- t(vect.gamma) %*% solve(Vcontr,vect.gamma)
p.wald.contr <- 1-pchisq(wald.contr,ny[k]-1)
}
tmp2 <- cbind(round(vect.gamma,5),
round(se[nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+((i-1)*(ny[k]-1)+1):(i*(ny[k]-1))],5),
round(wald[nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+((i-1)*(ny[k]-1)+1):(i*(ny[k]-1))],3),
round(pwald[nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+((i-1)*(ny[k]-1)+1):(i*(ny[k]-1))],5))
tmp2 <- rbind(rep(NA,4),tmp2)
if(x$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=""),Ynames[sumny+1:ny[k]])
else
{
if(round(p.wald.contr,5)!=0) rownames(tmp2) <- c(paste("Contrasts on ",nom.contr[i]," (p=",round(p.wald.contr,5),")",sep=""),Ynames[sumny+1:ny[k]])
if(round(p.wald.contr,5)==0) rownames(tmp2) <- c(paste("Contrasts on ",nom.contr[i]," (p<0.00001)",sep=""),Ynames[sumny+1:ny[k]])
}
}
if(x$conv==2)
{
tmp2 <- rbind(tmp2,c(-sum(na.omit(tmp2[,1])),NA,NA,NA))
rownames(tmp2) <- c(paste("Contrasts on ",nom.contr[i],sep=""),Ynames[sumny+1:ny[k]])
}
rownames(tmp2)[nrow(tmp2)] <- paste(rownames(tmp2)[nrow(tmp2)],"**",sep="")
if(!is.finite(tmp2[nrow(tmp2),3])) tmp2[nrow(tmp2),2:4] <- NA
tmp <- rbind(tmp,tmp2)
}
}
tTable[[k]] <- tmp
if(nef[k]>0 & any(c(nprob+nrisqtot+nvarxevt+sumnpm+1:(nef[k]+ncontr[k])) %in% posfix))
{
col1 <- rep(NA,length(tmp[,1]))
col1[which(!is.na(tmp[,1]))] <- format(as.numeric(sprintf("%.5f",na.omit(tmp[,1]))),nsmall=5,scientific=FALSE)
col2 <- rep(NA,length(tmp[,2]))
col2[which(!is.na(tmp[,2]))] <- format(as.numeric(sprintf("%.5f",na.omit(tmp[,2]))),nsmall=5,scientific=FALSE)
col3 <- rep(NA,length(tmp[,3]))
col3[which(!is.na(tmp[,3]))] <- format(as.numeric(sprintf("%.3f",na.omit(tmp[,3]))),nsmall=3,scientific=FALSE)
col4 <- rep(NA,length(tmp[,4]))
col4[which(!is.na(tmp[,4]))] <- format(as.numeric(sprintf("%.5f",na.omit(tmp[,4]))),nsmall=5,scientific=FALSE)
pf <- sort(intersect(c(nprob+nrisqtot+nvarxevt+sumnpm+1:(nef[k]+ncontr[k])),posfix))
p <- rep(0,length(tmp[,1]))
a0 <- 1:nef[k]
if(x$contrainte[k]!=0) a0 <- c(0,1:nef[k])
a1 <- rep(c(NA,1:(ny[k]-1),NA),sum(x$idcontr))
a2 <- rep(nef[k]+cumsum(c(0:(sum(x$idcontr)-1))),each=ny[k]+1)
a <- c(a0,a1+a2)
p[which(a>0)] <- c(nprob+nrisqtot+nvarxevt+sumnpm+1:(nef[k]+ncontr[k]))
#p[which(rownames(tmp) %in% c(x$Names$Xnames,Ynames[sumny+1:(ny[k]-1)]))] <- c(nprob+nrisqtot+nvarxevt+sumnpm+1:(nef[k]+ncontr[k]))
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[[k]])
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")
}
if(sum(x$idea[sumnv+1:x$nv[k]])>0) cat("Variance-covariance matrix of the random-effects:\n" )
if(x$contrainte[k]==2)
{
cat("(the variance of the first random effect is not estimated)\n")
if(nvc[k]==0)
{
Mat.cov <- matrix(1,nrow=1,ncol=1)
colnames(Mat.cov) <- x$Names$Xnames[sumnv+which(x$idea[sumnv+1:x$nv[k]]==1)]
rownames(Mat.cov) <- x$Names$Xnames[sumnv+which(x$idea[sumnv+1:x$nv[k]]==1)]
prmatrix(Mat.cov)
}
}
if(nvc[k]>0)
{
if(x$idiag[k]==1)
{
if(x$contrainte[k]==2)
{
Mat.cov <- diag(c(1,coef[nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+ncontr[k]+1:nvc[k]]))
}
else
{
Mat.cov <- diag(coef[nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+ncontr[k]+1:nvc[k]],nrow=nvc[k],ncol=nvc[k])
}
Mat.cov[lower.tri(Mat.cov)] <- 0
Mat.cov[upper.tri(Mat.cov)] <- NA
}
if(x$idiag[k]==0)
{
Mat.cov<-matrix(0,ncol=sum(x$idea[sumnv+1:x$nv[k]]),nrow=sum(x$idea[sumnv+1:x$nv[k]]))
if(x$contrainte[k]==2)
{
Mat.cov[upper.tri(Mat.cov,diag=TRUE)] <- c(1,coef[nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+ncontr[k]+1:nvc[k]])
}
else
{
Mat.cov[upper.tri(Mat.cov,diag=TRUE)] <- coef[nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+ncontr[k]+1:nvc[k]]
}
Mat.cov <-t(Mat.cov)
Mat.cov[upper.tri(Mat.cov)] <- NA
}
colnames(Mat.cov) <- x$Names$Xnames[sumnv+which(x$idea[sumnv+1:x$nv[k]]==1)]
rownames(Mat.cov) <- x$Names$Xnames[sumnv+which(x$idea[sumnv+1:x$nv[k]]==1)]
if(any(posfix %in% c(nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+ncontr[k]+1:nvc[k])))
{
Mat.cov <- apply(Mat.cov,2,format,digits=5,nsmall=5)
Mat.cov[upper.tri(Mat.cov)] <- ""
pf <- sort(intersect(c(nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+ncontr[k]+1:nvc[k]),posfix))
p <- matrix(0,sum(x$idea[sumnv+1:x$nv[k]]),sum(x$idea[sumnv+1:x$nv[k]]))
if(x$idiag[k]==FALSE)
{
if(x$contrainte[k]==2)
{
p[upper.tri(p,diag=TRUE)] <- c(0,nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+ncontr[k]+1:nvc[k])
}
else
{
p[upper.tri(p,diag=TRUE)] <- nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+ncontr[k]+1:nvc[k]
}
}
if(x$idiag[k]==TRUE)
{
if(x$contrainte[k]==2)
{
diag(p) <- c(0,nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+ncontr[k]+1:nvc[k])
}
else
{
diag(p) <- nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+ncontr[k]+1:nvc[k]
}
}
Mat.cov[which(t(p) %in% pf)] <- paste(Mat.cov[which(t(p) %in% pf)],"*",sep="")
print(Mat.cov,quote=FALSE)
}
else
{
prmatrix(round(Mat.cov,5),na.print="")
}
cat("\n")
}
std <- NULL
nom <- NULL
if(nw[k]>=1)
{
nom <- paste("Proportional coefficient class",c(1:(x$ng-1)),sep="")
std <- cbind(coefch[nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+ncontr[k]+nvc[k]+1:nw[k]],
sech[nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+ncontr[k]+nvc[k]+1:nw[k]])
}
if(ncor[k]==2)
{
nom <- c(nom,"AR correlation parameter:","AR standard error:")
std <- rbind(std,c(coefch[nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+ncontr[k]+nvc[k]+nw[k]+1],
sech[nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+ncontr[k]+nvc[k]+nw[k]+1]),
c(coefch[nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+ncontr[k]+nvc[k]+nw[k]+2],
sech[nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+ncontr[k]+nvc[k]+nw[k]+2]))
}
if(ncor[k]==1)
{
nom <- c(nom,"BM standard error:")
std <- rbind(std,c(coefch[nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+ncontr[k]+nvc[k]+nw[k]+1],
sech[nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+ncontr[k]+nvc[k]+nw[k]+1]))
}
if (!is.null(std))
{
rownames(std) <- nom
maxch <- apply(std,2,maxchar)
if(any(c(nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+ncontr[k]+nvc[k]+1:(nw[k]+ncor[k])) %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$contrainte[k]==1)
{
cat("Residual standard error: 1 (not estimated)\n")
}
else
{
std.err <- NULL
nom <- NULL
std.err <- rbind(std.err,coefch[nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+ncontr[k]+nvc[k]+nw[k]+ncor[k]+1:nerr[k]])
nom <- c(nom, "Residual standard error:")
if(nalea[k]>0)
{
std.err <- rbind(std.err,coefch[nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+ncontr[k]+nvc[k]+nw[k]+ncor[k]+nerr[k]+1:nalea[k]])
nom <- c(nom, "Standard error of the random effect:")
}
rownames(std.err) <- nom
maxch <- apply(std.err,2,maxchar)
if(any(c(nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+ncontr[k]+nvc[k]+nw[k]+ncor[k]+1:(nerr[k]+nalea[k])) %in% posfix))
{
if(nalea[k]>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[k],function(k) paste(paste(rep(" ",max(0,maxch[k]-maxchar(Ynames[sumny+k]))),collapse=""),Ynames[sumny+k],sep=""))
print(std.err,quote=FALSE,na.print="")
}
cat("\n")
if(any(ntr[sumny+1:ny[k]]>0))
{
cat("Parameters of the link functions:\n" )
tmp <- cbind(coefch[nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+ncontr[k]+nvc[k]+nw[k]+ncor[k]+nerr[k]+nalea[k]+1:sum(ntr[sumny+1:ny[k]])],
sech[nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+ncontr[k]+nvc[k]+nw[k]+ncor[k]+nerr[k]+nalea[k]+1:sum(ntr[sumny+1:ny[k]])],
waldch[nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+ncontr[k]+nvc[k]+nw[k]+ncor[k]+nerr[k]+nalea[k]+1:sum(ntr[sumny+1:ny[k]])],
pwaldch[nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+ncontr[k]+nvc[k]+nw[k]+ncor[k]+nerr[k]+nalea[k]+1:sum(ntr[sumny+1:ny[k]])])
tmp.rownames <- NULL
for (yk in 1:ny[k])
{
tmp.rownames <- c(tmp.rownames, paste(rep(Ynames[sumny+yk],ntr[sumny+yk]), names(coef[(nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+ncontr[k]+nvc[k]+nw[k]+ncor[k]+nerr[k]+nalea[k]+sum(ntr[sumny+1:yk])-ntr[sumny+yk]+1):(nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+ncontr[k]+nvc[k]+nw[k]+ncor[k]+nerr[k]+nalea[k]+sum(ntr[sumny+1:yk]))]),sep="-"))
}
rownames(tmp) <- tmp.rownames
maxch <- apply(tmp,2,maxchar)
if(any(c(nprob+nrisqtot+nvarxevt+sumnpm+nef[k]+ncontr[k]+nvc[k]+nw[k]+ncor[k]+nerr[k]+nalea[k]+1:sum(ntr[sumny+1:ny[k]])) %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")
}
sumnpm <- sumnpm + nef[k] + ncontr[k] + nvc[k] + nw[k] + ncor[k] + nerr[k] + nalea[k] + sum(ntr[sumny+1:ny[k]])
sumny <- sumny + x$ny[k]
sumnv <- sumnv + x$nv[k]
}
if(length(posfix))
{
cat(" * coefficient fixed by the user \n \n")
}
if(any(ncontr>0))
{
cat(" ** coefficient not estimated but obtained from the others as minus the sum of them \n \n")
}
return(invisible(tTable))
}
}
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