Nothing
"SpatDensReg" <- function (formula, data, na.action, prior=NULL, state=NULL,
mcmc=list(nburn=3000, nsave=2000, nskip=0, ndisplay=500),
permutation=TRUE, fix.theta=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$assign[!xdrop]
n <- nrow(X)
p <- ncol(X)
if(p==0){
stop("covariate is required; you could creat a working covariate and set phi=0")
}
Sinv = solve(var(X));
# find the maximumu M distance between X[i,] and colMeans(X)
distseq = rep(0, n);
Xbar = colMeans(X);
for(i in 1:n) distseq[i] = sqrt(as.vector((X[i,]-Xbar)%*%Sinv%*%(X[i,]-Xbar)))
maxdist = max(distseq)
phi0 = (-log(0.001))/maxdist;
#########################################################################################
# data structure
#########################################################################################
y1 = Y[,1]; y2 = Y[,1];
type <- attr(Y, "type")
exactsurv <- Y[,ncol(Y)] ==1
if (any(exactsurv)) {
y1[exactsurv]=Y[exactsurv,1];
y2[exactsurv]=Y[exactsurv,1];
}
if (type== 'counting') stop ("Invalid survival type")
if (type=='interval') {
intsurv <- Y[,3]==3;
if (any(intsurv)){
y1[intsurv]=Y[intsurv,1];
y2[intsurv]=Y[intsurv,2];
}
}
delta = Y[,ncol(Y)];
if (!all(is.finite(Y))) {
stop("Invalid survival times for this distribution")
} else {
if (type=='left') delta <- 2- delta;
}
#########################################################################################
# initial MLE analysis and mcmc parameters
#########################################################################################
fit0 <- survival::survreg(formula = survival::Surv(y1, y2, type="interval2")~1, dist="gaussian");
theta1 = fit0$coefficients[1];
theta2 = log(fit0$scale);
theta0 = c(theta1, theta2); theta_prior = c(theta1, theta2);
Vhat0 = as.matrix(fit0$var[c(1,2),c(1,2)]);
#########################################################################################
# priors and initial values
#########################################################################################
alpha=state$alpha; if(is.null(alpha)) alpha=1;
theta=state$theta; if(is.null(theta)) theta=c(theta1, theta2);
phi = state$phi; if(is.null(phi)) phi=phi0;
y <- state$y;
if(is.null(y)){
y <- rep(0, n);
for(i in 1:n){
if(delta[i]==0) y[i] = y1[i]+sd(y);
if(delta[i]==1) y[i] = y1[i];
if(delta[i]==2) y[i] = y2[i]-sd(y);
if(delta[i]==3) y[i] = mean(c(y1[i], y2[i]));
}
}
nburn <- mcmc$nburn;
nsave <- mcmc$nsave;
nskip <- mcmc$nskip;
ndisplay <- mcmc$ndisplay;
maxL <- prior$maxL; if(is.null(maxL)) maxL<-5;
a0=prior$a0; if(is.null(a0)) a0=5;
b0=prior$b0; if(is.null(b0)) b0=1;
if(fix.theta){
V0_prior = diag(0, 2);
}else{
V0_prior = 10*Vhat0;
}
theta0 <- prior$theta0; if(is.null(theta0)) theta0 <- theta_prior;
V0 <- prior$V0; if(is.null(V0)) V0 <- V0_prior;
if(sum(abs(V0))==0){
V0inv <- diag(c(Inf,Inf));
}else {
V0inv <- solve(V0);
}
Vhat <- prior$Vhat; if(is.null(Vhat)) Vhat <- Vhat0;
phiq0 = prior$phiq0; if(is.null(phiq0)) phiq0=0.5;
phia0 = prior$phia0; if(is.null(phia0)) phia0=2;
phib0 = prior$phib0; if(is.null(phib0)) phib0=1/phi0;
## save to output list
mcmc = list(nburn=nburn, nsave=nsave, nskip=nskip, ndisplay=ndisplay)
theta_initial=theta+0;
alpha_initial=alpha+0;
phi_initial = phi+0;
initial.values = list(alpha=alpha_initial, theta=theta_initial, phi=phi_initial);
prior = list(maxL=maxL, a0=a0, b0=b0, theta0=theta0, V0=V0, Vhat=Vhat,
phiq0=phiq0, phia0=phia0, phib0=phib0);
#########################################################################################
# calling the c++ code and # output
#########################################################################################
y1new=y1; y2new=y2;
for(i in 1:n){
if(delta[i]==0) y2new[i] = Inf;
if(delta[i]==2) y1new[i] = -Inf;
}
model.name <- "Spatially Smoothed Polya Tree Density Estimation:";
foo <- .Call("SpatDens", nburn_=nburn, nsave_=nsave, nskip_=nskip, ndisplay_=ndisplay,
y_=y, y1_=y1new, y2_=y2new, type_=delta, X_=t(X), theta_=theta, maxJ_=maxL,
cpar_=alpha, a0_=a0, b0_=b0, theta0_=theta0, V0inv_=V0inv, Vhat_=Vhat,
l0_=round(min(1000,nburn/2)), adapter_=2.38^2, Sinv_=Sinv, phi_=phi, q0phi_=phiq0,
a0phi_=phia0, b0phi_=phib0, perm_=permutation+0, PACKAGE = "spBayesSurv");
## Bayes Factor for the spatial model vs. the exchangeable model
q.bar = mean(foo$phi==0);
BF = (phiq0*(1-q.bar))/((1-phiq0)*q.bar);
#########################################################################################
# save to a list
#########################################################################################
output <- list(modelname=model.name,
terms=m,
call=Call,
prior=prior,
mcmc=mcmc,
n=n,
p=p,
Surv=survival::Surv(y1, y2, type="interval2"),
X = X,
alpha = foo$cpar,
theta = foo$theta,
phi = foo$phi,
y = foo$y,
maxL = maxL,
ratec = foo$ratec,
ratetheta = foo$ratetheta,
ratephi = foo$ratephi,
ratey = foo$ratey,
initial.values=initial.values,
BF = BF);
class(output) <- c("SpatDensReg")
output
}
#### empirial BF and p-value for the spatial model vs. the exchangeable model
"BF.SpatDensReg" <- function (y, X, prior=NULL, nperm=100, c_seq=NULL, phi_seq=NULL) {
n = length(y);
X = cbind(X);
Sinv = solve(var(X));
# find the maximumu M distance between X[i,] and colMeans(X)
distseq = rep(0, n);
Xbar = colMeans(X);
for(i in 1:n) distseq[i] = sqrt(as.vector((X[i,]-Xbar)%*%Sinv%*%(X[i,]-Xbar)))
maxdist = max(distseq)
phi0 = (-log(0.001))/maxdist;
#########################################################################################
# initial MLE analysis
#########################################################################################
fit0 <- survival::survreg(formula = survival::Surv(y)~1, dist="gaussian");
theta1 = fit0$coefficients[1];
theta2 = log(fit0$scale);
theta = c(theta1, theta2);
#########################################################################################
# priors and initial values
#########################################################################################
maxL <- prior$maxL; if(is.null(maxL)) maxL<-5;
a0=prior$a0; if(is.null(a0)) a0=5;
b0=prior$b0; if(is.null(b0)) b0=1;
phiq0 = prior$phiq0; if(is.null(phiq0)) phiq0=0.5;
phia0 = prior$phia0; if(is.null(phia0)) phia0=2;
phib0 = prior$phib0; if(is.null(phib0)) phib0=1/phi0;
if(is.null(c_seq)) c_seq=c(0.001, 0.01, 0.1, 0.5, 1, 5, 10, 50, 100, 1000);
if(is.null(phi_seq)) phi_seq = qgamma((1:10)/11, phia0, phib0)
#########################################################################################
# calling the c++ code and # output
#########################################################################################
foo <- .Call("SpatDens_BF", y_=y, X_=t(X), Sinv_=Sinv, theta_=theta, maxJ_=maxL,
cpar_=c_seq, a0_=a0, b0_=b0, phi_=phi_seq, q0phi_=phiq0,
a0phi_=phia0, b0phi_=phib0, nperm_=nperm, PACKAGE = "spBayesSurv");
## Bayes Factor for the spatial model vs. the exchangeable model
BF = foo$BF;
pvalue = sum(foo$BFperm>foo$BF)/nperm;
output <- list(BF = BF,
pvalue = pvalue);
output
}
#### print, summary, plot
"print.SpatDensReg" <- function (x, digits = max(3, getOption("digits") - 3), ...)
{
cat(x$modelname,"\nCall:\n", sep = "")
print(x$call)
cat(paste("\nBayes Factor for the spatial model vs. the exchangeable model:", sep=""), x$BF);
cat("\nn=",x$n, "\n", sep="")
invisible(x)
}
"plot.SpatDensReg" <- function (x, xnewdata, ygrid=NULL, CI=0.95, PLOT=TRUE, ...) {
if(is.null(ygrid)) ygrid = seq(min(x$Surv[,1], na.rm=T)-sd(x$Surv[,1], na.rm=T),
max(x$Surv[,2], na.rm=T)+sd(x$Surv[,2], na.rm=T), length.out=200);
if(missing(xnewdata)){
stop("please specify xnewdata")
}else{
rnames = row.names(xnewdata)
m = x$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
if(ncol(xpred)!=x$p) stop("please make sure the number of columns matches!");
}
xpred = cbind(xpred);
nxpred = nrow(xpred);
Sinv = solve(var(x$X));
estimates <- .Call("SpatDens_plots", ygrid, t(xpred), x$theta, x$alpha, x$phi, x$maxL,
x$y, t(x$X), Sinv, 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(ygrid, estimates$fhat[,1], "l", lwd=3, xlab="log time", ylab="density",
xlim=c(min(ygrid), max(ygrid)), ylim=c(0,max(estimates$fhatup)));
for(i in 1:nxpred){
polygon(x=c(rev(ygrid),ygrid),
y=c(rev(estimates$fhatlow[,i]),estimates$fhatup[,i]),
border=NA,col="lightgray");
}
for(i in 1:nxpred){
lines(ygrid, estimates$fhat[,i], lty=i, lwd=3, col=i);
}
legend("topright", rnames, col = 1:nxpred, lty=1:nxpred, ...)
}
estimates$ygrid=ygrid;
invisible(estimates)
}
"summary.SpatDensReg" <- function(object, CI.level=0.95, ...) {
ans <- c(object[c("call", "modelname")])
### Baseline Information
mat <- as.matrix(object$theta)
coef.p <- apply(mat, 1, mean); names(coef.p)=c("location", "log(scale)");
coef.m <- apply(mat, 1, median)
coef.sd <- apply(mat, 1, sd)
limm <- apply(mat, 1, function(x) as.vector(quantile(x, probs=c((1-CI.level)/2, 1-(1-CI.level)/2))) )
coef.l <- limm[1,]
coef.u <- limm[2,]
coef.table <- cbind(coef.p, coef.m, coef.sd, coef.l , coef.u)
dimnames(coef.table) <- list(names(coef.p), c("Mean", "Median", "Std. Dev.",
paste(CI.level*100, "%CI-Low", sep=""),
paste(CI.level*100, "%CI-Upp", sep="")))
ans$theta.var <- coef.table
### Precision parameter
if(object$prior$a0<=0){
ans$alpha.var <- NULL
}else{
mat <- object$alpha
coef.p <- mean(mat); names(coef.p)="alpha";
coef.m <- median(mat)
coef.sd <- sd(mat)
limm <- as.vector(quantile(mat, probs=c((1-CI.level)/2, 1-(1-CI.level)/2)))
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, "%CI-Low", sep=""),
paste(CI.level*100, "%CI-Upp", sep="")))
ans$alpha.var <- coef.table
}
### phi parameter
mat <- object$phi
coef.p <- mean(mat); names(coef.p)="range";
coef.m <- median(mat)
coef.sd <- sd(mat)
limm <- as.vector(quantile(mat, probs=c((1-CI.level)/2, 1-(1-CI.level)/2)))
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, "%CI-Low", sep=""),
paste(CI.level*100, "%CI-Upp", sep="")))
ans$phi.var <- coef.table;
ans$BF <- object$BF
ans$n <- object$n
ans$p <- object$p
ans$prior <- object$prior
### acceptance rates
ans$ratetheta = object$ratetheta;
ans$ratephi = object$ratephi;
ans$ratey = object$ratey;
ans$ratec = object$ratec;
class(ans) <- "summary.SpatDensReg"
return(ans)
}
"print.summary.SpatDensReg"<-function (x, digits = max(3, getOption("digits") - 3), ...)
{
cat(x$modelname,"\nCall:\n", sep = "")
print(x$call)
if(x$theta.var[1,3]==0){
cat("\nCentering distribution parameters are fixed at:\n")
cat("location=", x$theta.var[1,1], ", log(scale)=", x$theta.var[2,1], "\n", sep="")
}else{
cat("\nPosterior inference of centering distribution parameters\n")
cat("(Adaptive M-H acceptance rate: ", x$ratetheta, "):\n", sep="")
print.default(format(x$theta.var, digits = digits), print.gap = 2,
quote = FALSE)
}
if (!is.null(x$alpha.var)) {
cat("\nPosterior inference of precision parameter\n")
cat("(Adaptive M-H acceptance rate: ", x$ratec, "):\n", sep="")
print.default(format(x$alpha.var, digits = digits), print.gap = 2,
quote = FALSE)
}
cat("\nPosterior inference of distance function range phi\n")
cat("(Adaptive M-H acceptance rate: ", x$ratephi, "):\n", sep="")
print.default(format(x$phi.var, digits = digits), print.gap = 2,
quote = FALSE)
cat(paste("\nBayes Factor for the spatial model vs. the exchangeable model:", sep=""), x$BF)
cat("\nNumber of subjects: n=", x$n, "\n", sep="")
invisible(x)
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.