Nothing
"frailtyGAFT" <- function (formula, data, na.action,
mcmc=list(nburn=3000, nsave=2000, nskip=0, ndisplay=500),
prior=NULL, state=NULL, Proximity=NULL, Coordinates=NULL,
DIST=NULL, scale.designX=TRUE){
#########################################################################################
# call parameters
#########################################################################################
Call <- match.call(); # save a copy of the call
indx <- match(c("formula", "data", "na.action", "truncation_time", "subject.num"),
names(Call), nomatch=0)
if (indx[1] ==0) stop("A formula argument is required");
temp <- Call[c(1,indx)] # only keep the arguments we wanted
temp[[1L]] <- quote(stats::model.frame)
special <- c("baseline", "frailtyprior", "truncation_time", "subject.num", "bspline")
temp$formula <- if (missing(data))
terms(formula, special)
else terms(formula, special, data = data)
if (is.R())
m <- eval(temp, parent.frame())
else m <- eval(temp, sys.parent())
Terms <- attr(m, 'terms')
if(any(names(m)=="(truncation_time)")){
truncation_time = m[,"(truncation_time)"]
}else{
truncation_time = NULL
}
if(any(names(m)=="(subject.num)")){
subject.num = m[,"(subject.num)"]
}else{
subject.num = NULL
}
Y <- model.extract(m, "response")
if (!inherits(Y, "Surv")) stop("Response must be a survival object")
baseline0 <- attr(Terms, "specials")$baseline
frailtyprior0<- attr(Terms, "specials")$frailtyprior
bspline0<- attr(Terms, "specials")$bspline
if (length(frailtyprior0)) {
temp <- survival::untangle.specials(Terms, 'frailtyprior', 1)
dropfrail <- c(temp$terms)
frail.terms <- m[[temp$vars]]
}else{
dropfrail <- NULL
frail.terms <- NULL;
}
if (length(baseline0)) {
temp <- survival::untangle.specials(Terms, 'baseline', 1)
dropXtf <- c(temp$terms)
Xtf <- m[[temp$vars]]
}else{
dropXtf <- NULL
Xtf <- NULL
}
if (length(bspline0)) {
temp <- survival::untangle.specials(Terms, 'bspline', 1)
#dropx <- c(dropx, temp$terms);
X.bs = NULL;
n.bs = rep(0, length(temp$vars));
for(ii in 1:length(temp$vars)){
X.bs = cbind(X.bs, m[[temp$vars[ii]]]);
n.bs[ii] = ncol(m[[temp$vars[ii]]]);
}
}else{
X.bs <- NULL;
n.bs <- NULL;
}
dropx <- c(dropfrail, dropXtf)
if (length(dropx)) {
newTerms <- Terms[-dropx]
# R (version 2.7.1) adds intercept=T anytime you drop something
if (is.R()) attr(newTerms, 'intercept') <- attr(Terms, 'intercept')
} else newTerms <- Terms
X <- model.matrix(newTerms, m);
if (is.R()) {
assign <- lapply(survival::attrassign(X, newTerms)[-1], function(x) x-1)
xlevels <- .getXlevels(newTerms, m)
contr.save <- attr(X, 'contrasts')
}else {
assign <- lapply(attr(X, 'assign')[-1], function(x) x -1)
xvars <- as.character(attr(newTerms, 'variables'))
xvars <- xvars[-attr(newTerms, 'response')]
if (length(xvars) >0) {
xlevels <- lapply(m[xvars], levels)
xlevels <- xlevels[!unlist(lapply(xlevels, is.null))]
if(length(xlevels) == 0)
xlevels <- NULL
} else xlevels <- NULL
contr.save <- attr(X, 'contrasts')
}
# drop the intercept after the fact, and also drop baseline if necessary
adrop <- 0 #levels of "assign" to be dropped; 0= intercept
Xatt <- attributes(X)
xdrop <- Xatt$assign %in% adrop #columns to drop (always the intercept)
X <- X[, !xdrop, drop=FALSE]
attr(X, "assign") <- Xatt$assign[!xdrop]
n <- nrow(X)
p <- ncol(X)
pce = p+1;
if(p==0){
X.scaled <- NULL;
X1 = cbind(rep(1,n), X.scaled); colnames(X1)[1]="intercept";
}else{
if(scale.designX){
X.scaled <- scale(X);
}else{
X.scaled <- scale(X, center=rep(0,p), scale=rep(1,p));
}
X.center = attributes(X.scaled)$`scaled:center`;
X.scale = attributes(X.scaled)$`scaled:scale`;
X1 = cbind(rep(1,n), X.scaled); colnames(X1)[1]="intercept";
}
if(is.null(Xtf)){
Xtf.scaled <- NULL;
Xtf1 = cbind(rep(1,n), Xtf.scaled); colnames(Xtf1)[1]="intercept";
}else{
if(scale.designX){
Xtf.scaled <- scale(Xtf);
}else{
Xtf.scaled <- scale(Xtf, center=rep(0,ncol(Xtf)), scale=rep(1,ncol(Xtf)));
}
Xtf.center = attributes(Xtf.scaled)$`scaled:center`;
Xtf.scale = attributes(Xtf.scaled)$`scaled:scale`;
Xtf1 = cbind(rep(1,n), Xtf.scaled); colnames(Xtf1)[1]="intercept";
}
ptf = ncol(Xtf1);
#########################################################################################
# data structure
#########################################################################################
t1 = Y[,1]; t2 = Y[,1];
type <- attr(Y, "type")
if (type== 'counting') stop ("Invalid survival type")
exactsurv <- Y[,ncol(Y)] ==1
if (any(exactsurv)) {
t1[exactsurv]=Y[exactsurv,1];
t2[exactsurv]=Y[exactsurv,1];
}
if (type=='interval') {
intsurv <- Y[,3]==3;
if (any(intsurv)){
t1[intsurv]=Y[intsurv,1];
t2[intsurv]=Y[intsurv,2];
}
}
delta = Y[,ncol(Y)];
if (!all(is.finite(Y))) {
stop("Invalid survival times for this distribution")
} else {
if (type=='left') delta <- 2- delta;
}
if(is.null(truncation_time)) truncation_time=rep(0, n);
frail.prior = colnames(frail.terms)[1];
ID = frail.terms[,1];
#### Distance type:
if(is.null(DIST)){
DIST <- function(x, y) fields::rdist(x, y)
}
##############################################
### Currently it only supports AFT ###########
##############################################
#########################################################################################
# initial MLE analysis and mcmc parameters
#########################################################################################
## initial fit
fit0 <- survival::survreg(formula = Y~X1-1, dist="lognormal");
betace = fit0$coefficients;
betaShat0 = 100*fit0$var[1:pce,1:pce];
sigma2 = (fit0$scale)^2;
sig2hat <- (fit0$scale)^2;
sig2var <- 100*fit0$var[pce+1, pce+1]*4*sig2hat^2;
sig2a0 = 2+sig2hat^2/sig2var;
sig2b0 = sig2hat*(sig2a0-1);
y <- state$logt;
if(is.null(y)){
y <- rep(0, n);
for(i in 1:n){
if(delta[i]==0) y[i] = log(t1[i]+1);
if(delta[i]==1) y[i] = log(t1[i]);
if(delta[i]==2) y[i] = log(t2[i]/2);
if(delta[i]==3) y[i] = log(mean(c(t1[i], t2[i])));
}
}
#########################################################################################
# check frailty
#########################################################################################
if(!is.null(frail.prior)) {
if(is.null(ID)) stop("please specify ID");
orderindex = order(ID);
if(!(sum(orderindex==(1:n))==n)) stop("please sort the data by ID");
blocki = c(0, cumsum(as.vector(table(ID))));
if(frail.prior=="car") {
if(is.null(Proximity)) stop("please specify prxoimity matrix");
W = Proximity;
D = rowSums(W);
if (any(D==0)) stop("it seems that some region does not have any neighbers, which is not allowed, pelase check")
}else if(frail.prior=="iid"){
W = matrix(0, length(blocki)-1, length(blocki)-1);
D = rep(0, length(blocki)-1);
}else if(frail.prior=="grf"){
if(is.null(Coordinates)) stop("please specify Coordinates for each ID");
if(nrow(Coordinates)!=(length(blocki)-1)) stop("the number of coordinates should be equal to the number of ID")
Dmm = DIST(Coordinates, Coordinates);
if(min(Dmm[row(Dmm)!=col(Dmm)])<=0) stop("each ID should have different Coordinates");
}else{
stop("This function only supports non-frailty, car frailty, iid and grf frailty models.")
}
}else{
ID = NULL;
blocki = c(0, n);
W = matrix(1, length(blocki)-1, length(blocki)-1);
D = rep(1, length(blocki)-1);
v <- rep(0, length(blocki)-1);
}
phi = state$phi; if(is.null(phi)) phi=1;
phia0_prior = 2;
nu = prior$nu; if(is.null(nu)) nu=1;
if(!is.null(frail.prior)){
if(frail.prior=="grf"){
maxdis = max(Dmm);
#phi_min = (-log(0.001))^(1/nu)/maxdis; phib0_prior = -log(.95)/phi_min; phi = 1/phib0_prior;
#cc = sqrt((-log(0.001))^(1/nu)/maxdis); phi = maxdis*cc;
phi = (-log(0.001))^(1/nu)/maxdis;
phia0_prior = 2;
if(!is.null(state$phi)){
phi = state$phi;
}
if (phi<=0) stop("phi in state arguement should be greater than 0.")
}
}
phi0=phi;
if(is.null(state$frail)) {
v <- rep(0, length(blocki)-1);
} else {
v <- state$frail; if(length(v)!=(length(blocki)-1)) stop("check the length of frail");
}
#########################################################################################
# priors
#########################################################################################
alpha=state$alpha; if(is.null(alpha)) alpha=5;
tau2 = state$tau2; if(is.null(tau2)) tau2=0.5;
nburn <- mcmc$nburn;
nsave <- mcmc$nsave;
nskip <- mcmc$nskip;
ndisplay <- mcmc$ndisplay;
maxL <- prior$maxL; if(is.null(maxL)) maxL<-4;
ntprob <- 2^(maxL+1)-2;
ntlr <- 2^maxL-1;
betatf <- matrix(0,nrow=ptf,ncol=ntlr);
a0=prior$a0; if(is.null(a0)) a0=5;
b0=prior$b0; if(is.null(b0)) b0=1;
if(a0<=0){
a0=-1;alpha=state$alpha;
if(is.null(alpha)) stop("please specify state$alpha if prior$a0 is not positive");
}
m0 <- prior$m0; if(is.null(m0)) m0 <- rep(0, pce);
S0 <- prior$S0; if(is.null(S0)) S0 <- diag(1e5, pce);
S0inv <- solve(S0);
siga0=prior$siga0; if(is.null(siga0)) siga0=sig2a0;
sigb0=prior$sigb0; if(is.null(sigb0)) sigb0=sig2b0;
gprior <- prior$gprior; if(is.null(gprior)) gprior <- 2*n*solve(t(Xtf1)%*%Xtf1);
taua0 = prior$taua0; if(is.null(taua0)) taua0=1;
taub0 = prior$taub0; if(is.null(taub0)) taub0=1;
phia0 = prior$phia0; if(is.null(phia0)) phia0=phia0_prior;
phib0 = prior$phib0; if(is.null(phib0)) phib0=(phia0-1)/phi0;
mm = prior$mm; if (is.null(mm)) mm = 10;
win = prior$win; if (is.null(win)) win = 1;
mcmc = list(nburn=nburn, nsave=nsave, nskip=nskip, ndisplay=ndisplay)
if(!is.null(frail.prior)){
prior = list(maxL=maxL, a0=a0, b0=b0, siga0=siga0, sigb0=sigb0, m0=m0, S0=S0, mm=mm, win=win);
if((frail.prior=="iid")|(frail.prior=="car")){
prior$taua0=taua0; prior$taub0=taub0;
}else if (frail.prior=="grf"){
prior$nu=nu;
prior$taua0=taua0; prior$taub0=taub0; #prior$silla0=silla0; prior$sillb0=sillb0;
prior$phia0=phia0; prior$phib0=phib0;
}
}else{
prior = list(maxL=maxL, a0=a0, b0=b0, siga0=siga0, sigb0=sigb0, m0=m0, S0=S0, mm=mm, win=win)
}
#########################################################################################
# calling the c++ code and # output
#########################################################################################
if(!is.null(frail.prior)){
model.name <- "Generalized accelerated failure time frailty model:";
if((frail.prior=="iid")|(frail.prior=="car")){
foo <- .Call("frailtyLDTFP", nburn_ = nburn, nsave_ = nsave, nskip_ = nskip, ndisplay_ = ndisplay,
tobs_ = cbind(t1, t2), type_ = delta, xce_ = t(X1), xtf_ = t(Xtf1), alpha_ = alpha,
betace_ = betace, betatf_ = betatf, sigma2_ = sigma2, y_ = y, v_ = v, blocki_ = blocki,
tau2_ = tau2, W_ = W, D_ = D, maxL_ = maxL, a0_ = a0, b0_ = b0, m0_ = m0, S0inv_ = S0inv,
gprior_ = gprior, a0sig_ = siga0, b0sig_ = sigb0, a0tau_ = taua0, b0tau_ = taub0,
win_ = win, mm_ = mm, PACKAGE = "spBayesSurv");
}else if (frail.prior=="grf"){
foo <- .Call("frailty_GRF_LDTFP", nburn_ = nburn, nsave_ = nsave, nskip_ = nskip, ndisplay_ = ndisplay,
tobs_ = cbind(t1, t2), type_ = delta, xce_ = t(X1), xtf_ = t(Xtf1), alpha_ = alpha,
betace_ = betace, betatf_ = betatf, sigma2_ = sigma2, y_ = y, v_ = v, blocki_ = blocki,
tau2_ = tau2, Dmm_ = Dmm, maxL_ = maxL, a0_ = a0, b0_ = b0,
m0_ = m0, S0inv_ = S0inv, gprior_ = gprior, a0sig_ = siga0, b0sig_ = sigb0,
a0tau_ = taua0, b0tau_ = taub0, nu_ = nu, phi_ = phi,
a0phi_ = phia0, b0phi_ = phib0, win_ = win, mm_ = mm, PACKAGE = "spBayesSurv");
}
}else{
model.name <- "Generalized accelerated failure time model:";
foo <- .Call("nonfrailtyLDTFP", nburn_ = nburn, nsave_ = nsave, nskip_ = nskip, ndisplay_ = ndisplay,
tobs_ = cbind(t1, t2), type_ = delta, xce_ = t(X1), xtf_ = t(Xtf1), alpha_ = alpha,
betace_ = betace, betatf_ = betatf, sigma2_ = sigma2, y_ = y, maxL_ = maxL, a0_ = a0, b0_ = b0,
m0_ = m0, S0inv_ = S0inv, gprior_ = gprior, a0sig_ = siga0, b0sig_ = sigb0,
win_ = win, mm_ = mm, PACKAGE = "spBayesSurv");
}
#########################################################################################
# save state
#########################################################################################
#### transfer the estimates back to original scales;
beta.scaled = matrix(foo$beta, pce, nsave); beta.original = matrix(foo$beta, pce, nsave);
if(p>0){
beta.original[2:pce, ] = matrix(beta.scaled[2:pce, ], p, nsave)/matrix(rep(X.scale, nsave), p, nsave);
mat.tmp1 = matrix(beta.scaled[2:pce, ], p, nsave)
mat.tmp2 = matrix(rep(X.center, nsave), p, nsave)
mat.tmp3 = matrix(rep(X.scale, nsave), p, nsave)
beta.original[1, ] = beta.scaled[1,] - colSums(mat.tmp1*mat.tmp2/mat.tmp3)
}
#### coefficients
coeff <- c( apply(beta.original, 1, mean), mean(sqrt(foo$sigma2)), mean(foo$alpha) );
names(coeff) = c(colnames(X1), "scale", "precision");
#### Save to a list
output <- list(modelname=model.name,
terms = m,
coefficients=coeff,
call=Call,
prior=prior,
mcmc=mcmc,
n=n,
pce=pce,
ptf=ptf,
Surv=Y,
X.scaled=X.scaled,
X=X,
Xtf.scaled=Xtf.scaled,
Xtf=Xtf,
sigma2 = foo$sigma2,
beta = beta.original,
beta.scaled = beta.scaled,
alpha = foo$alpha,
maxL = maxL,
betatf = foo$betatf,
logt = foo$y,
cpo = foo$cpo,
accept_beta = foo$ratebetace,
accept_betatf = foo$ratebetatf,
frail.prior=frail.prior);
## check frailties
if(!is.null(frail.prior)){
output$v = foo$v;
output$ratev = foo$ratev;
output$tau2 = foo$tau2;
output$ID = ID;
if (frail.prior=="grf"){
output$Coordinates = Coordinates
output$phi = foo$phi;
output$ratephi = foo$ratephi;
}
}
### Calculate Bayes Factors for betatf
gprior = solve(t(Xtf1)%*%Xtf1)*(2*n);
betatfmat = matrix(as.vector(foo$betatf[,-1,]), (2^maxL-2)*ptf, nsave);
BFs = .Call("BayesFactor", betatf_=betatfmat, maxL_=maxL, gprior_=gprior, alpha_=mean(foo$alpha), PACKAGE = "spBayesSurv");
BayesFactors = c(as.vector( BFs$BFindividual ), BFs$BFoverallLDTFP, BFs$BFoverallParam);
names(BayesFactors) = c( colnames(Xtf1), "overall", "normality");
output$BF = BayesFactors;
class(output) <- c("frailtyGAFT")
output
}
#### print, summary, plot
"print.frailtyGAFT" <- function (x, digits = max(3, getOption("digits") - 3), ...)
{
cat(x$modelname,"\nCall:\n", sep = "")
print(x$call)
cat("\nPosterior means for regression coefficients:\n")
print.default(format(x$coefficients[1:x$pce], digits = digits), print.gap = 2,
quote = FALSE)
cat("\nBayes factors for LDTFP covariate effects:\n")
print.default(format(x$BF, digits = digits), print.gap = 2,
quote = FALSE)
cat("\nLPML:", sum(log(x$cpo)))
cat("\nn =",x$n, "\n")
invisible(x)
}
"plot.frailtyGAFT" <- function (x, xnewdata, xtfnewdata, tgrid = NULL,
frail = NULL, CI = 0.95, PLOT = TRUE, ...) {
if(is.null(tgrid)) tgrid = seq(0.01, max(x$Surv[,1], na.rm=T), length.out=200);
if(x$pce==1){
if(is.null(frail)){
if(missing(xtfnewdata)){
xpred = cbind(1); nxpred=1;
rnames = "baseline"
}else{
nxpred = nrow(xtfnewdata);
xpred = matrix(1, nrow = nxpred);
rnames = row.names(xtfnewdata)
}
}else{
if(is.vector(frail)) frail=matrix(frail, nrow=1);
nxpred = nrow(frail);
xpred = matrix(1, nrow = nxpred);
rnames = row.names(as.data.frame(frail))
if(!missing(xtfnewdata)){
if(nrow(xtfnewdata)!=nxpred) stop("xtfnewdata and frail should have the same numbers of rows")
}
}
}else{
if(missing(xnewdata)) {
stop("please specify xnewdata")
}else{
rnames = row.names(xnewdata)
m = x$terms
Terms = attr(m, 'terms')
baseline0 <- attr(Terms, "specials")$baseline
frailtyprior0<- attr(Terms, "specials")$frailtyprior
dropx <- NULL
if (length(frailtyprior0)) {
temp <- survival::untangle.specials(Terms, 'frailtyprior', 1)
dropx <- c(dropx, temp$terms)
frail.terms <- m[[temp$vars]]
}else{
frail.terms <- NULL;
}
if (length(baseline0)) {
temp <- survival::untangle.specials(Terms, 'baseline', 1)
dropx <- c(dropx, temp$terms)
Xtf <- m[[temp$vars]]
}else{
Xtf <- NULL;
}
if (length(dropx)) {
newTerms <- Terms[-dropx]
# R (version 2.7.1) adds intercept=T anytime you drop something
if (is.R()) attr(newTerms, 'intercept') <- attr(Terms, 'intercept')
} else newTerms <- Terms
newTerms <- delete.response(newTerms)
mnew <- model.frame(newTerms, xnewdata, na.action = na.omit, xlev = .getXlevels(newTerms, m))
Xnew <- model.matrix(newTerms, mnew);
if (is.R()) {
assign <- lapply(survival::attrassign(Xnew, newTerms)[-1], function(x) x-1)
xlevels <- .getXlevels(newTerms, mnew)
contr.save <- attr(Xnew, 'contrasts')
}else {
assign <- lapply(attr(Xnew, 'assign')[-1], function(x) x -1)
xvars <- as.character(attr(newTerms, 'variables'))
xvars <- xvars[-attr(newTerms, 'response')]
if (length(xvars) >0) {
xlevels <- lapply(mnew[xvars], levels)
xlevels <- xlevels[!unlist(lapply(xlevels, is.null))]
if(length(xlevels) == 0)
xlevels <- NULL
} else xlevels <- NULL
contr.save <- attr(Xnew, 'contrasts')
}
# drop the intercept after the fact, and also drop baseline if necessary
adrop <- 0 #levels of "assign" to be dropped; 0= intercept
Xatt <- attributes(Xnew)
xdrop <- Xatt$assign %in% adrop #columns to drop (always the intercept)
Xnew <- Xnew[, !xdrop, drop=FALSE]
attr(Xnew, "assign") <- Xatt$assign[!xdrop]
xpred = Xnew
nxpred = nrow(xpred);
X.center = attributes(x$X.scaled)$`scaled:center`;
X.scale = attributes(x$X.scaled)$`scaled:scale`;
for(i in 1:nxpred) xpred[i,] = (xpred[i,]-X.center)/X.scale;
xpred = cbind(rep(1,nxpred),xpred);
if(ncol(xpred)!=x$pce) stop("please make sure the number of columns matches!");
}
}
if(x$ptf==1){
xtfpred = matrix(1, nrow = nxpred);
}else{
if(missing(xtfnewdata)) {
stop("please specify xtfnewdata")
}else{
newTerms = attr(x$Xtf, 'terms')
mnew <- model.frame(newTerms, xtfnewdata, na.action = na.omit, xlev = attr(x$Xtf, "levels"))
Xnew <- model.matrix(newTerms, mnew);
xtfpred = cbind(Xnew);
Xtf.center = attributes(x$Xtf.scaled)$`scaled:center`;
Xtf.scale = attributes(x$Xtf.scaled)$`scaled:scale`;
for(i in 1:nxpred) xtfpred[i, 2:x$ptf] = (xtfpred[i,2:x$ptf]-Xtf.center)/Xtf.scale;
if(ncol(xtfpred)!=x$ptf) stop("please make sure the number of columns matches!");
}
}
if(nrow(xpred)!=nrow(xtfpred)) stop("please make sure xnewdata and xtfnewdata have the same numbers of rows!");
if(is.null(frail)){
frail=matrix(0, nrow=nxpred, ncol=x$mcmc$nsave);
}else{
if(is.vector(frail)) frail=matrix(frail, nrow=1);
if((nrow(frail)!=nxpred)|(ncol(frail)!=x$mcmc$nsave)) stop("The dim of frail should be nrow(xpred) by nsave.")
}
estimates <- .Call("frailtyGAFTplots", tgrid, xpred, xtfpred, x$beta.scaled, x$betatf, frail,
x$sigma2, x$maxL, CI, PACKAGE = "spBayesSurv");
if(PLOT){
par(cex=1.5,mar=c(4.1,4.1,1,1),cex.lab=1.4,cex.axis=1.1)
plot(tgrid, estimates$Shat[,1], "l", lwd=3, xlab="time", ylab="survival",
xlim=c(0, max(tgrid)), ylim=c(0,1));
for(i in 1:nxpred){
polygon(x=c(rev(tgrid),tgrid),
y=c(rev(estimates$Shatlow[,i]),estimates$Shatup[,i]),
border=NA,col="lightgray");
}
for(i in 1:nxpred){
lines(tgrid, estimates$Shat[,i], lty=i, lwd=3, col=i);
}
legend("topright", rnames, col = 1:nxpred, lty=1:nxpred, ...)
}
estimates$tgrid=tgrid;
invisible(estimates)
}
"summary.frailtyGAFT" <- function(object, CI.level=0.95, ...) {
ans <- c(object[c("call", "modelname")])
### CPO
ans$cpo <- object$cpo
### Median information
mat <- as.matrix(object$beta)
coef.p <- object$coefficients[(1:object$pce)];
coef.m <- apply(mat, 1, median)
coef.sd <- apply(mat, 1, sd)
limm <- apply(mat, 1, function(x) as.vector(coda::HPDinterval(coda::as.mcmc(x), prob=CI.level)))
coef.l <- limm[1,]
coef.u <- limm[2,]
coef.table <- cbind(coef.p, coef.m, coef.sd, coef.l , coef.u)
dimnames(coef.table) <- list(names(coef.p), c("Mean", "Median", "Std. Dev.",
paste(CI.level*100, "%HPD-Low", sep=""),
paste(CI.level*100, "%HPD-Upp", sep="")))
ans$coeff <- coef.table
### Scale parameter
mat <- c(sqrt( object$sigma2));
coef.p <- object$coefficients[c(object$pce+1)];
coef.m <- median(mat)
coef.sd <-sd(mat)
limm <- as.vector(coda::HPDinterval(coda::mcmc(mat), prob=CI.level))
coef.l <- limm[1]
coef.u <- limm[2]
coef.table <- cbind(coef.p, coef.m, coef.sd, coef.l , coef.u)
dimnames(coef.table) <- list(names(coef.p), c("Mean", "Median", "Std. Dev.",
paste(CI.level*100, "%HPD-Low", sep=""),
paste(CI.level*100, "%HPD-Upp", sep="")))
ans$scale<- coef.table
### Precision parameter
if(object$prior$a0<=0){
ans$prec <- NULL
}else{
mat <- object$alpha
coef.p <- mean(mat); names(coef.p)="alpha";
coef.m <- median(mat)
coef.sd <- sd(mat)
limm <- as.vector(coda::HPDinterval(coda::mcmc(mat), prob=CI.level))
coef.l <- limm[1]
coef.u <- limm[2]
coef.table <- cbind(coef.p, coef.m, coef.sd, coef.l , coef.u)
dimnames(coef.table) <- list(names(coef.p), c("Mean", "Median", "Std. Dev.",
paste(CI.level*100, "%HPD-Low", sep=""),
paste(CI.level*100, "%HPD-Upp", sep="")))
ans$prec <- coef.table
}
### frailty variance parameter
ans$frail.prior=object$frail.prior;
if(is.null(object$frail.prior)){
ans$frailvar <- NULL
}else{
mat <- object$tau2
coef.p <- mean(mat); names(coef.p)="variance";
coef.m <- median(mat)
coef.sd <- sd(mat)
limm <- as.vector(coda::HPDinterval(coda::mcmc(mat), prob=CI.level))
coef.l <- limm[1]
coef.u <- limm[2]
coef.table <- cbind(coef.p, coef.m, coef.sd, coef.l , coef.u)
dimnames(coef.table) <- list(names(coef.p), c("Mean", "Median", "Std. Dev.",
paste(CI.level*100, "%HPD-Low", sep=""),
paste(CI.level*100, "%HPD-Upp", sep="")))
ans$frailvar <- coef.table;
if(object$frail.prior=="grf"){
mat <- object$phi
coef.p <- mean(mat); names(coef.p)="range";
coef.m <- median(mat)
coef.sd <- sd(mat)
limm <- as.vector(coda::HPDinterval(coda::mcmc(mat), prob=CI.level))
coef.l <- limm[1]
coef.u <- limm[2]
coef.table <- cbind(coef.p, coef.m, coef.sd, coef.l , coef.u)
dimnames(coef.table) <- list(names(coef.p), c("Mean", "Median", "Std. Dev.",
paste(CI.level*100, "%HPD-Low", sep=""),
paste(CI.level*100, "%HPD-Upp", sep="")))
ans$range <- coef.table;
}
}
### summaries
ans$n <- object$n
ans$pce <- object$pce
ans$LPML <- sum(log(object$cpo))
ans$BF <- object$BF;
### acceptance rates
ans$ratephi = object$ratephi;
class(ans) <- "summary.frailtyGAFT"
return(ans)
}
"print.summary.frailtyGAFT"<-function (x, digits = max(3, getOption("digits") - 3), ...)
{
cat(x$modelname,"\nCall:\n", sep = "")
print(x$call)
if(!is.null(x$coeff)){
cat("\nPosterior inference of regression coefficients\n")
print.default(format(x$coeff, digits = digits), print.gap = 2,
quote = FALSE)
}
cat("\nPosterior inference of scale parameter\n")
print.default(format(x$scale, digits = digits), print.gap = 2, quote = FALSE)
if (!is.null(x$prec)) {
cat("\nPosterior inference of precision parameter of LDTFP\n")
print.default(format(x$prec, digits = digits), print.gap = 2,
quote = FALSE)
}
if (!is.null(x$frailvar)) {
if(x$frail.prior=="car"){
cat("\nPosterior inference of conditional CAR frailty variance\n")
print.default(format(x$frailvar, digits = digits), print.gap = 2,
quote = FALSE)
}else if(x$frail.prior=="iid"){
cat("\nPosterior inference of frailty variance\n")
print.default(format(x$frailvar, digits = digits), print.gap = 2,
quote = FALSE)
} else if(x$frail.prior=="grf"){
cat("\nPosterior inference of frailty variance\n")
print.default(format(x$frailvar, digits = digits), print.gap = 2,
quote = FALSE)
cat("\nPosterior inference of correlation function range phi \n")
print.default(format(x$range, digits = digits), print.gap = 2,
quote = FALSE)
}
}
cat("\nBayes factors for LDTFP covariate effects:\n")
print.default(format(x$BF, digits = digits), print.gap = 2,
quote = FALSE)
cat("\nLog pseudo marginal likelihood: LPML", x$LPML, sep="=")
#cat("\nDeviance information criterion: DIC", x$DIC, sep="=")
cat("\nNumber of subjects:", x$n, sep="=")
invisible(x)
}
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