JMbayes:::update.mvJMbayes
JMbayes:::update.JMbayes
function (object, ...)
{
call <- object$call
if (is.null(call))
stop("need an object with call component.\n")
extras <- match.call(expand.dots = FALSE)$...
if (length(extras) > 0) {
nams <- names(extras)
existing <- !is.na(match(nams, names(call)))
for (a in names(extras)[existing]) {
call[[a]] <- extras[[a]]
}
if (any(!existing)) {
call <- c(as.list(call), extras[!existing])
call <- as.call(call)
}
if (all(nams %in% c("scales", "n.iter", "n.burnin",
"n.adapt", "n.thin"))) {
call <- c(as.list(call), list(init = extractInits(object)))
call <- as.call(call)
}
}
else {
call <- c(as.list(call), list(init = extractInits(object)))
call <- as.call(call)
}
eval(call, parent.frame())
}
i <- 1
x <- joint_model_fit_1
names(x_mcmc$b)
length(x_mcmc[namsX %in% 'b'])
lapply(list, function)
extract_last_iterations <- function(x) {
x_mcmc <- x$mcmc
n_chains <- x$control$n_chains
namsX <- names(x_mcmc)
last_iter_b <- list(NULL)
last_iter <- list(NULL)
for (i in 1:n_chains) {
last_iter_b[[i]] <- lapply(x_mcmc[namsX %in% 'b'], function(x, i) x[[i]][, , dim(x[[i]])[length(dim(x[[i]]))], drop = FALSE], i = i)
last_iter[[i]] <- lapply(x_mcmc[!namsX %in% 'b'], function(x, i) x[[i]][nrow(x[[i]]), , drop = FALSE], i = i)
last_iter[[i]] <- c(last_iter[[i]], last_iter_b[[i]])
}
last_iter
}
chk <- extract_last_iteration(x)
chk1 <- last_iter_b[[i]]$b
chk2 <- x$mcmc$b$`1`
chk3 <- last_iter[[i]]$bs_gammas
chk4 <- x$mcmc$bs_gammas$`1`[6000, ]
all.equal(chk3, chk4)
class(chk1)
length(chk)
chk[[2]]$bs_gammas
x$mcmc$bs_gammas$`2`[6000, ]
last_iterations[[1]]$betas <- list(last_iterations[[1]]$betas1)
last_iterations[[1]]$b <- list(last_iterations[[1]]$b)
last_iterations[[1]]$D <-
initial_values$D
last_iterations[[1]]$
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