Displaying 4 results from an estimated 4 matches for "loo_predict".
2018 Mar 09
2
Package gamlss used inside foreach() and %dopar% fails to find an object
...FP = round(runif(sample.size) * 100),
x = runif(sample.size))
# Fit Beta-binomial
model3 <- gamlss(formula = cbind(TP, FP) ~ x,
family = BB,
data = input.processed.cut)
# Get the leave-one-out values
loo_predict.mu <- function(model.obj, input.data) {
yhat <- foreach(i = 1 : nrow(input.data), .packages="gamlss", .combine = rbind) %dopar% {
updated.model.obj <- update(model.obj, data = input.data[-i, ])
predict(updated.model.obj, what = "mu", newdata = input.data[i,],...
2018 Mar 09
0
Package gamlss used inside foreach() and %dopar% fails to find an object
...runif(sample.size) * 100),
> x = runif(sample.size))
># Fit Beta-binomial
>model3 <- gamlss(formula = cbind(TP, FP) ~ x,
> family = BB,
> data = input.processed.cut)
>
># Get the leave-one-out values
>loo_predict.mu <- function(model.obj, input.data) {
>yhat <- foreach(i = 1 : nrow(input.data), .packages="gamlss", .combine
>= rbind) %dopar% {
> updated.model.obj <- update(model.obj, data = input.data[-i, ])
>predict(updated.model.obj, what = "mu", newdata = input....
2018 Mar 10
0
. Package gamlss used inside foreach() and %dopar% fails to find an object (Nik Tuzov)
Dear Nik
Try the following code
loo_predict.mu <- function(model.obj, input.data) {
yhat <- foreach(i = 1 : nrow(input.data), .packages="gamlss", .combine = rbind) %dopar% {
updated.model.obj <- update(model.obj, data = input.data[-i, ])
predict(updated.model.obj, what = "mu", data = input.data[-i, ],...
2018 Mar 12
0
Package gamlss used inside foreach() and %dopar% fails to find an object
...zov.com>
Subject: [R] . Package gamlss used inside foreach() and %dopar% fails
to find an object (Nik Tuzov)
Message-ID:
<424DC535-4CAF-4DD5-8BD5-EB19BF6F7247 at staff.londonmet.ac.uk>
Content-Type: text/plain; charset="utf-8"
Dear Nik
Try the following code
loo_predict.mu <- function(model.obj, input.data) {
yhat <- foreach(i = 1 : nrow(input.data), .packages="gamlss", .combine = rbind) %dopar% {
updated.model.obj <- update(model.obj, data = input.data[-i, ])
predict(updated.model.obj, what = "mu", data = input.data[-i, ],...