Nothing
dynInfo <- function (object, newdata, Dt, K = 5, M = 500, idVar = "id",
simulateFun = function (eta, scale) rnorm(length(eta), eta, scale),
seed = 1L) {
if (!inherits(object, "JMbayes"))
stop("'object' must inherit from class JMbayes.")
set.seed(seed)
# extract components from fitted joint model
timeVar <- object$timeVar
df.RE <- object$y$df.RE
param <- object$param
densLong <- object$Funs$densLong
hasScale <- object$Funs$hasScale
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
performHC <- object$control$performHC
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$weightFun
na.ind <- as.vector(attr(mfX, "na.action"))
na.ind <- if (is.null(na.ind)) {
rep(TRUE, nrow(newdata))
} else {
!seq_len(nrow(newdata)) %in% na.ind
}
id <- as.numeric(unclass(newdata[[idVar]]))
id <- match(id, unique(id))
id <- id. <- id[na.ind]
y <- yy <- model.response(mfX)
X <- XX <- model.matrix.default(formYx, mfX)
Z <- ZZ <- model.matrix.default(formYz, mfZ)[na.ind, , drop = FALSE]
TermsT <- object$Terms$termsT
# extract time and variables from newdata
TimeVar <- all.vars(TermsT)[1L]
eventVar <- all.vars(TermsT)[2L]
respVar <- all.vars(TermsX)[1L]
maxTime <- max(object$y$Time)
max_time <- max(newdata[[timeVar]])
times <- seq(max_time, max_time + Dt, len = K + 1)[-1L]
ntimes <- length(times)
data.id <- newdata[tapply(row.names(newdata), id, tail, n = 1L),]
data.s <- data.id[rep(1:nrow(data.id), each = object$control$GQsurv.k), ]
idT <- data.id[[idVar]]
idT <- match(idT, unique(idT))
ids <- data.s[[idVar]]
ids <- match(ids, unique(ids))
data.p <- data.id[rep(1:nrow(data.id), each = ntimes), ]
data.p[[timeVar]] <- times
mfXpred <- model.frame.default(TermsX, data = data.p)
mfZpred <- model.frame.default(TermsZ, data = data.p)
Xpred <- model.matrix.default(formYx, mfXpred)
Zpred <- model.matrix.default(formYz, mfZpred)
mfT <- model.frame.default(delete.response(TermsT), data = data.id)
tt <- attr(delete.response(TermsT), "term.labels")
formT <- if (length(tt)) reformulate(tt) else reformulate("1")
W <- model.matrix.default(formT, mfT)[, -1L, drop = FALSE]
########
n <- nrow(data.id)
n.tp <- length(times)
ncx <- ncol(X)
ncz <- ncol(Z)
ncww <- ncol(W)
if (ncww == 0L)
W <- NULL
lag <- object$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$Bs.gammas
list.thetas <- list(betas = betas, sigma = sigma, gammas = gammas, alphas = alphas,
Dalphas = Dalphas, shapes = shapes, Bs.gammas = Bs.gammas, D = D)
list.thetas <- list.thetas[!sapply(list.thetas, is.null)]
thetas <- unlist(as.relistable(list.thetas))
########
environment(log.posterior.b) <- environment(S.b) <- environment(logh.b) <- environment()
environment(hMats) <- environment(ModelMats) <- environment()
obs.times <- split(newdata[[timeVar]][na.ind], id)
survMats <- lapply(c(max_time, times), ModelMats, ii = 1)
u_times <- lapply(lapply(times, seq, to = maxTime * 1.1, length.out = 31), tail, n = -1)
#hh <- function (t) lapply(t, ModelMats, ii = 1)
#survMats_samp <- lapply(u_times, hh)
#hazMats <- lapply(times, hMats)
# calculate the Empirical Bayes estimates and their (scaled) variance
betas.new <- betas
sigma.new <- sigma
D.new <- D
gammas.new <- gammas
alphas.new <- alphas
Dalphas.new <- Dalphas
shapes.new <- shapes
Bs.gammas.new <- Bs.gammas
ff <- function (b, y, tt, mm, i) -log.posterior.b(b, y, Mats = tt, ii = i)
start <- rep(0, ncz)
opt <- try(optim(start, ff, y = y, tt = survMats, i = 1,
method = "BFGS", hessian = TRUE), silent = TRUE)
if (inherits(opt, "try-error")) {
gg <- function (b, y, tt, mm, i) cd(b, ff, y = y, tt = tt, i = i)
opt <- optim(start, ff, gg, y = y, tt = survMats,
i = 1, method = "BFGS", hessian = TRUE,
control = list(parscale = rep(0.1, ncz)))
}
modes.b <- opt$par
invVars.b <- opt$hessian / 1.5
Vars.b <- 1.5 * solve(opt$hessian)
b.old <- b.new <- modes.b
b.old1 <- b.new1 <- modes.b
b.old2 <- b.new2 <- modes.b
mcmc <- object$mcmc
mcmc <- mcmc[names(mcmc) != "b"]
samples <- sample(nrow(mcmc$betas), 3 * M, replace = TRUE)
mcmc[] <- lapply(mcmc, function (x) x[samples, , drop = FALSE])
proposed.b <- rmvt(n = M, df = 4, mu = modes.b, Sigma = Vars.b)
dmvt.proposed <- dmvt(proposed.b, mu = modes.b, Sigma = Vars.b, df = 4, log = TRUE)
proposed.b1 <- rmvt(n = M, df = 4, mu = modes.b, Sigma = Vars.b)
dmvt.proposed1 <- dmvt(proposed.b1, mu = modes.b, Sigma = Vars.b, df = 4, log = TRUE)
proposed.b2 <- rmvt(n = M, df = 4, mu = modes.b, Sigma = Vars.b)
dmvt.proposed2 <- dmvt(proposed.b2, mu = modes.b, Sigma = Vars.b, df = 4, log = TRUE)
# loop over time points
log.p_Tj <- function (Tj) {
log.S_ti <- S.b(times[1], b.new2, i = 1, survMats[[1]], log = TRUE)
log.S_Tj <- S.b(Tj, b.new2, i = 1, ModelMats(Tj, 1), log = TRUE)
log.h_Tj <- logh.b(b.new2, hMats(Tj))
log.h_Tj + log.S_Tj - log.S_ti
}
sfit <- survfitJM(object, newdata = newdata, M = M, init.b = rbind(modes.b),
survTimes = times, idVar = idVar)
sfit <- 1 - as.vector(sfit$summaries[[1]][, "Mean"])
info.times <- matrix(0, M, ntimes)
for (ti in seq_len(ntimes)) {
# Monte Carlo scheme
old_Tj <- Tj <- 1.1 * times[ti]
count <- count.b <- count.b1 <- count.b2 <- 0.0
info <- numeric(M)
for (m in seq_len(M)) {
# Step 1-1: Simulate parameter values from [theta | D_n]
betas.new <- mcmc$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$Bs.gammas[m, ]
# Step 1-2: Simulate b from [b | T_j > t, Y_j(t)]
id <- id.
y <- yy
X <- XX
Z <- ZZ
p.b <- proposed.b[m, ]
dmvt.old <- dmvt(b.old, modes.b, invSigma = invVars.b, df = 4, log = TRUE)
dmvt.prop <- dmvt.proposed[m]
a <- min(exp(log.posterior.b(p.b, y, list(survMats[[1]]), ii = 1) + dmvt.old -
log.posterior.b(b.old, y, list(survMats[[1]]), ii = 1) - dmvt.prop), 1)
ind <- runif(1) <= a
if (!is.na(ind) && ind) {
b.new <- p.b
count.b <- count.b + 1
}
b.old <- b.new
# Step 1-3: Simulate y_j(u)
eta.y <- drop(Xpred %*% betas.new + Zpred %*% b.new)[ti]
y.new <- simulateFun(eta.y, sigma.new)
##################################################################################
# Step 2-1: Simulate parameter values from [theta | D_n]
betas.new <- mcmc$betas[m + M, ]
if (hasScale)
sigma.new <- mcmc$sigma[m + M, ]
if (!is.null(W))
gammas.new <- mcmc$gammas[m + M, ]
if (param %in% c("td-value", "td-both", "shared-betasRE", "shared-RE"))
alphas.new <- mcmc$alpha[m + M, ]
if (param %in% c("td-extra", "td-both"))
Dalphas.new <- mcmc$Dalphas[m + M, ]
if (estimateWeightFun)
shapes.new <- mcmc$shapes[m + M, ]
D.new <- mcmc$D[m + M, ]; dim(D.new) <- dim(D)
Bs.gammas.new <- mcmc$Bs.gammas[m + M, ]
# Step 2-2: Simulate b from [b | T_j > t, {Y_j(t), y_j(u)}]
id <- c(id., tail(id, 1))
y <- c(yy, y.new)
X <- rbind(XX, Xpred[ti, ])
Z <- rbind(ZZ, Zpred[ti, ])
p.b1 <- proposed.b1[m, ]
dmvt.old1 <- dmvt(b.old1, modes.b, invSigma = invVars.b, df = 4, log = TRUE)
dmvt.prop1 <- dmvt.proposed1[m]
a1 <- min(exp(log.posterior.b(p.b1, y, list(survMats[[1]]), ii = 1) + dmvt.old1 -
log.posterior.b(b.old1, y, list(survMats[[1]]), ii = 1) - dmvt.prop1), 1)
ind1 <- runif(1) <= a1
if (!is.na(ind1) && ind1) {
b.new1 <- p.b1
count.b1 <- count.b1 + 1
}
b.old1 <- b.new1
# Step 2-3: Simulate T_j^* from [T_j^* | T_j > u, {Y_j(t), y_j(u)}]
prop_Tj <- runif(1, times[ti], maxTime * 1.1)
aa <- min(exp(log.p_Tj(prop_Tj) - log.p_Tj(old_Tj)), 1)
ind <- runif(1) <= aa
if (!is.na(ind) && ind) {
Tj <- prop_Tj
count <- count + 1
}
old_Tj <- Tj
##################################################################################
# Step 3-1: Simulate parameter values from [theta | D_n]
betas.new <- mcmc$betas[m + 2*M, ]
if (hasScale)
sigma.new <- mcmc$sigma[m + 2*M, ]
if (!is.null(W))
gammas.new <- mcmc$gammas[m + 2*M, ]
if (param %in% c("td-value", "td-both", "shared-betasRE", "shared-RE"))
alphas.new <- mcmc$alpha[m + 2*M, ]
if (param %in% c("td-extra", "td-both"))
Dalphas.new <- mcmc$Dalphas[m + 2*M, ]
if (estimateWeightFun)
shapes.new <- mcmc$shapes[m + 2*M, ]
D.new <- mcmc$D[m + 2*M, ]; dim(D.new) <- dim(D)
Bs.gammas.new <- mcmc$Bs.gammas[m +2*M, ]
# Step 3-2: Simulate b from [b | T_j > t, {Y_j(t), y_j(u)}]
p.b2 <- proposed.b2[m, ]
dmvt.old2 <- dmvt(b.old2, modes.b, invSigma = invVars.b, df = 4, log = TRUE)
dmvt.prop2 <- dmvt.proposed2[m]
a2 <- min(exp(log.posterior.b(p.b2, y, list(survMats[[1 + ti]]), ii = 1) + dmvt.old2 -
log.posterior.b(b.old2, y, list(survMats[[1 + ti]]), ii = 1) - dmvt.prop2), 1)
ind2 <- runif(1) <= a2
if (!is.na(ind2) && ind2) {
b.new2 <- p.b2
count.b2 <- count.b2 + 1
}
b.old2 <- b.new2
# Step 3-3: Calculate p(T_j | T_j > t, b)
log.S_ti <- S.b(times[ti], b.new2, i = 1, survMats[[1 + ti]], log = TRUE)
log.S_Tj <- S.b(Tj, b.new2, i = 1, ModelMats(Tj, 1), log = TRUE)
log.h_Tj <- logh.b(b.new2, hMats(Tj))
info[m] <- log.h_Tj + log.S_Tj - log.S_ti
}
#print(count/M)
#print(count.b/M)
#print(count.b1/M)
#print(count.b2/M)
info.times[, ti] <- info
}
infoSum <- apply(info.times, 2, median, na.rm = TRUE)
stars <- rep(" ", length.out = ntimes)
stars[which.max(infoSum)] <- "*"
d <- data.frame(times = times, Info = infoSum, pi = sfit,
" " = stars, check.names = FALSE)
rm(list = ".Random.seed", envir = globalenv())
list(summary = d, full.results = info.times)
}
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.