Nothing
survfitJM.JMbayes <- function (object, newdata, type = c("SurvProb", "Density"),
idVar = "id", simulate = TRUE, survTimes = NULL,
last.time = NULL, LeftTrunc_var = NULL, M = 200L,
CI.levels = c(0.025, 0.975),
log = FALSE, scale = 1.6, weight = rep(1, nrow(newdata)),
init.b = NULL, seed = 1L, ...) {
if (!inherits(object, "JMbayes"))
stop("Use only with 'JMbayes' objects.\n")
if (!is.data.frame(newdata) || nrow(newdata) == 0L)
stop("'newdata' must be a data.frame with more than one rows.\n")
if (is.null(newdata[[idVar]]))
stop("'idVar' not in 'newdata.\n'")
type <- match.arg(type)
TT <- object$y$Time
if (is.null(survTimes) || !is.numeric(survTimes)) {
survTimes <- seq(min(TT), quantile(TT, 0.90) + 0.01, length.out = 35L)
}
if (type != "SurvProb") simulate <- TRUE
timeVar <- object$timeVar
df.RE <- object$y$df.RE
param <- object$param
densLong <- object$Funs$densLong
hasScale <- object$Funs$hasScale
anyLeftTrunc <- object$y$anyLeftTrunc
densRE <- object$Funs$densRE
transFun.value <- object$Funs$transFun.value
transFun.extra <- object$Funs$transFun.extra
extraForm <- object$Forms$extraForm
indFixed <- extraForm$indFixed
indRandom <- extraForm$indRandom
indBetas <- object$y$indBetas
TermsX <- object$Terms$termsYx
TermsZ <- object$Terms$termsYz
TermsX.extra <- object$Terms$termsYx.extra
TermsZ.extra <- object$Terms$termsYz.extra
mfX <- model.frame.default(TermsX, data = newdata)
mfZ <- model.frame.default(TermsZ, data = newdata)
formYx <- reformulate(attr(delete.response(TermsX), "term.labels"))
formYz <- object$Forms$formYz
estimateWeightFun <- object$estimateWeightFun
weightFun <- object$Funs$weightFun
max.time <- max(TT)
na.ind <- as.vector(attr(mfX, "na.action"))
na.ind <- if (is.null(na.ind)) {
rep(TRUE, nrow(newdata))
} else {
!seq_len(nrow(newdata)) %in% na.ind
}
id <- newdata[[idVar]]
id <- id. <- match(id, unique(id))
id <- id[na.ind]
y <- model.response(mfX)
X <- model.matrix.default(formYx, mfX)
Z <- model.matrix.default(formYz, mfZ)[na.ind, , drop = FALSE]
TermsT <- object$Terms$termsT
data.id <- newdata[tapply(row.names(newdata), id, tail, n = 1L), ]
data.s <- data.id[rep(1:nrow(data.id), each = object$control$GQsurv.k), ]
idT <- data.id[[idVar]]
idT <- match(idT, unique(idT))
ids <- data.s[[idVar]]
ids <- match(ids, unique(ids))
if (type != "SurvProb") {
SurvT <- model.response(model.frame(TermsT, data.id))
Time <- SurvT[, 1]
event <- SurvT[, 2]
}
mfT <- model.frame.default(delete.response(TermsT), data = data.id)
formT <- if (!is.null(kk <- attr(TermsT, "specials")$cluster)) {
tt <- drop.terms(TermsT, kk - 1, keep.response = FALSE)
reformulate(attr(tt, "term.labels"))
} else {
tt <- attr(delete.response(TermsT), "term.labels")
if (length(tt)) reformulate(tt) else reformulate("1")
}
W <- model.matrix.default(formT, mfT)[, -1L, drop = FALSE]
obs.times <- split(newdata[[timeVar]][na.ind], id)
last.time <- if (is.null(last.time)) {
tapply(newdata[[timeVar]], id., max)
} else if (is.character(last.time) && length(last.time) == 1L) {
tapply(newdata[[last.time]], id., tail, n = 1L)
} else if (is.numeric(last.time)) {
rep_len(last.time, length.out = nrow(data.id))
} else {
stop("\nnot appropriate value for 'last.time' argument.")
}
times.to.pred <- if (type == "SurvProb") {
lapply(last.time, function (t) survTimes[survTimes > t])
} else {
as.list(Time)
}
TimeL <- if (!is.null(anyLeftTrunc) && anyLeftTrunc) {
if (is.null(LeftTrunc_var) || is.null(newdata[[LeftTrunc_var]])) {
warning("The original joint model was fitted in a data set with left-",
"truncation and\nargument 'LeftTrunc_var' of survfitJM() has not ",
"been specified.\n")
}
TimeL <- newdata[[LeftTrunc_var]]
tapply(TimeL, id, head, n = 1)
}
n <- length(TT)
n.tp <- length(last.time)
ncx <- ncol(X)
ncz <- ncol(Z)
ncww <- ncol(W)
if (ncww == 0L)
W <- NULL
lag <- object$y$lag
betas <- object$postMeans$betas
sigma <- object$postMeans$sigma
D <- object$postMeans$D
gammas <- object$postMeans$gammas
alphas <- object$postMeans$alphas
Dalphas <- object$postMeans$Dalphas
shapes <- object$postMeans$shapes
Bs.gammas <- object$postMeans$Bs.gammas
list.thetas <- list(betas = betas, sigma = sigma, gammas = gammas, alphas = alphas,
Dalphas = Dalphas, shapes = shapes, Bs.gammas = Bs.gammas, D = D)
list.thetas <- list.thetas[!sapply(list.thetas, is.null)]
thetas <- unlist(as.relistable(list.thetas))
environment(log.posterior.b) <- environment(S.b) <- environment(logh.b) <- environment()
environment(hMats) <- environment(ModelMats) <- environment()
# construct model matrices to calculate the survival functions
obs.times.surv <- split(data.id[[timeVar]], idT)
survMats <- survMats.last <- vector("list", n.tp)
for (i in seq_len(n.tp)) {
survMats[[i]] <- lapply(times.to.pred[[i]], ModelMats, ii = i, timeL = TimeL[i])
survMats.last[[i]] <- ModelMats(last.time[i], ii = i, timeL = TimeL[i])
}
if (type != "SurvProb")
hazMats <- hMats(Time)
# calculate the Empirical Bayes estimates and their (scaled) variance
modes.b <- matrix(0, n.tp, ncz)
invVars.b <- Vars.b <- vector("list", n.tp)
for (i in seq_len(n.tp)) {
betas.new <- betas
sigma.new <- sigma
D.new <- D
gammas.new <- gammas
alphas.new <- alphas
Dalphas.new <- Dalphas
shapes.new <- shapes
Bs.gammas.new <- Bs.gammas
ff <- function (b, y, tt, mm, i) -log.posterior.b(b, y, Mats = tt, ii = i)
start <- if (is.null(init.b)) rep(0, ncz) else init.b[i, ]
opt <- try(optim(start, ff, y = y, tt = survMats.last, i = i,
method = "BFGS", hessian = TRUE), silent = TRUE)
if (inherits(opt, "try-error")) {
gg <- function (b, y, tt, mm, i) cd(b, ff, y = y, tt = tt, i = i)
opt <- optim(start, ff, gg, y = y, tt = survMats.last,
i = i, method = "BFGS", hessian = TRUE,
control = list(parscale = rep(0.1, ncz)))
}
modes.b[i, ] <- opt$par
invVars.b[[i]] <- opt$hessian/scale
Vars.b[[i]] <- scale * solve(opt$hessian)
}
if (!simulate) {
res <- vector("list", n.tp)
for (i in seq_len(n.tp)) {
S.last <- S.b(last.time[i], modes.b[i, ], i, survMats.last[[i]])
S.pred <- numeric(length(times.to.pred[[i]]))
for (l in seq_along(S.pred))
S.pred[l] <- S.b(times.to.pred[[i]][l], modes.b[i, ], i, survMats[[i]][[l]])
res[[i]] <- cbind(times = times.to.pred[[i]], predSurv = weight[i] * S.pred / S.last)
rownames(res[[i]]) <- seq_along(S.pred)
}
} else {
set.seed(seed)
out <- vector("list", M)
success.rate <- matrix(FALSE, M, n.tp)
b.old <- b.new <- modes.b
if (n.tp == 1)
dim(b.old) <- dim(b.new) <- c(1L, ncz)
mcmc <- object$mcmc
mcmc <- mcmc[names(mcmc) != "b"]
if (M > nrow(mcmc$betas)) {
warning("'M' cannot be set greater than ", nrow(mcmc$betas))
M <- nrow(mcmc$betas)
out <- vector("list", M)
success.rate <- matrix(FALSE, M, n.tp)
}
samples <- sample(nrow(mcmc$betas), M)
mcmc[] <- lapply(mcmc, function (x) x[samples, , drop = FALSE])
proposed.b <- mapply(rmvt, mu = split(modes.b, row(modes.b)), Sigma = Vars.b,
MoreArgs = list(n = M, df = 4), SIMPLIFY = FALSE)
proposed.b[] <- lapply(proposed.b, function (x) if (is.matrix(x)) x else rbind(x))
dmvt.proposed <- mapply(dmvt, x = proposed.b, mu = split(modes.b, row(modes.b)),
Sigma = Vars.b, MoreArgs = list(df = 4, log = TRUE),
SIMPLIFY = FALSE)
for (m in 1:M) {
# Step 1: extract parameter values
betas.new <- mcmc$betas[m, ]
if (hasScale)
sigma.new <- mcmc$sigma[m, ]
if (!is.null(W))
gammas.new <- mcmc$gammas[m, ]
if (param %in% c("td-value", "td-both", "shared-betasRE", "shared-RE"))
alphas.new <- mcmc$alpha[m, ]
if (param %in% c("td-extra", "td-both"))
Dalphas.new <- mcmc$Dalphas[m, ]
if (estimateWeightFun)
shapes.new <- mcmc$shapes[m, ]
D.new <- mcmc$D[m, ]; dim(D.new) <- dim(D)
Bs.gammas.new <- mcmc$Bs.gammas[m, ]
if (type != "SurvProb") {
logHaz <- logh.b(modes.b, hazMats)
}
SS <- vector("list", n.tp)
for (i in seq_len(n.tp)) {
# Step 2: simulate new random effects values
p.b <- proposed.b[[i]][m, ]
dmvt.old <- dmvt(b.old[i, ], modes.b[i, ], invSigma = invVars.b[[i]],
df = 4, log = TRUE)
dmvt.prop <- dmvt.proposed[[i]][m]
a <- min(exp(log.posterior.b(p.b, y, survMats.last, ii = i) + dmvt.old -
log.posterior.b(b.old[i, ], y, survMats.last, ii = i) - dmvt.prop), 1)
ind <- runif(1) <= a
success.rate[m, i] <- ind
if (!is.na(ind) && ind)
b.new[i, ] <- p.b
# Step 3: compute Pr(T > t_k | T > t_{k - 1}; theta.new, b.new)
logS.last <- S.b(last.time[i], b.new[i, ], i, survMats.last[[i]],
log = TRUE)
logS.pred <- numeric(length(times.to.pred[[i]]))
for (l in seq_along(logS.pred))
logS.pred[l] <- S.b(times.to.pred[[i]][l], b.new[i, ], i,
survMats[[i]][[l]], log = TRUE)
if (type != "SurvProb") {
logS.pred <- event[i] * logHaz[i] + logS.pred
}
SS[[i]] <- weight[i] * if (log) logS.pred - logS.last else exp(logS.pred - logS.last)
}
b.old <- b.new
out[[m]] <- SS
}
res <- vector("list", n.tp)
for (i in seq_len(n.tp)) {
rr <- sapply(out, "[[", i)
if (!is.matrix(rr))
rr <- rbind(rr)
res[[i]] <- cbind(
times = times.to.pred[[i]],
"Mean" = rowMeans(rr, na.rm = TRUE),
"Median" = apply(rr, 1L, median, na.rm = TRUE),
"Lower" = apply(rr, 1L, quantile, probs = CI.levels[1], na.rm = TRUE),
"Upper" = apply(rr, 1L, quantile, probs = CI.levels[2], na.rm = TRUE)
)
rownames(res[[i]]) <- as.character(seq_len(NROW(res[[i]])))
}
}
y <- split(y, id)
newdata. <- do.call(rbind, mapply(function (d, t) {
d. <- rbind(d, d[nrow(d), ])
d.[[timeVar]][nrow(d.)] <- t
d.
}, split(newdata, id.), last.time, SIMPLIFY = FALSE))
id. <- newdata.[[idVar]]
id. <- match(id., unique(id.))
mfX. <- model.frame(delete.response(TermsX), data = newdata.)
mfZ. <- model.frame(TermsZ, data = newdata.)
X. <- model.matrix(formYx, mfX.)
Z. <- model.matrix(formYz, mfZ.)
fitted.y <- split(c(X. %*% betas) + rowSums(Z. * modes.b[id., , drop = FALSE]), id.)
names(res) <- names(y) <- names(last.time) <- names(obs.times) <- as.character(unique(newdata[[idVar]]))
res <- list(summaries = res, survTimes = survTimes, last.time = last.time,
obs.times = obs.times, y = y,
fitted.times = split(newdata.[[timeVar]], factor(newdata.[[idVar]])),
fitted.y = fitted.y, ry = range(object$y$y, na.rm = TRUE),
nameY = paste(object$Forms$formYx)[2L], modes.b = modes.b)
if (simulate) {
res$full.results <- out
res$success.rate <- success.rate
}
if (simulate) rm(list = ".Random.seed", envir = globalenv())
class(res) <- "survfit.JMbayes"
res
}
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