Hello Jeff:
Thanks for replying.
I made a mistake in my original post.
The same error is generated with %do% as well, so it seems to be gamlss-related.
Nik
----- Original Message -----
From: "Jeff Newmiller" <jdnew...@dcn.davis.ca.us>
To: "r-help" <r-help@r-project.org>, "Nik Tuzov" <ntu...@beacon.partek.com>
Sent: Friday, March 9, 2018 11:16:21 AM
Subject: Re: [R] Package gamlss used inside foreach() and %dopar% fails to find
an object
If the code you are running in parallel is complicated, maybe foreach is not
sophisticated enough to find all the variables you refer to. Maybe use
parallel::clusterExport yourself? But be a aware that passing parameters is
much safer than directly accessing globals in parallel processing, so this
might just be your warning to not do that anyway.
--
Sent from my phone. Please excuse my brevity.
On March 9, 2018 7:50:44 AM PST, Nik Tuzov <ntu...@beacon.partek.com> wrote:
>
>Hello all:
>
>Please help me with this "can't find object" issue. I'm trying to get
>leave-one-out predicted values for Beta-binomial regression.
>It may be the gamlss issue because the code seems to work when %do% is
>used. I have searched for similar issues, but haven't managed to figure
>it out. This is on Windows 10 platform.
>
>Thanks in advance,
>Nik
>
># --------------------------------------------------------------
>
>library('gamlss')
>library('foreach')
>library('doParallel')
>
>registerDoParallel(cores = 4)
># Generate data
>set.seed(314)
>sample.size <- 30
>input.processed.cut <- data.frame(TP = round(runif(sample.size) * 100),
>
> 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,], type
>= "response")
> }
> return(data.frame(result = yhat[, 1], row.names = NULL))
>}
>
>par.run <- loo_predict.mu(model3, input.processed.cut)
>
># Error in { : task 1 failed - "object 'input.data' not found"
>
>#
>------------------------------------------------------------------------
>
>> version
> _
>platform x86_64-w64-mingw32
>arch x86_64
>os mingw32
>system x86_64, mingw32
>status
>major 3
>minor 4.3
>year 2017
>month 11
>day 30
>svn rev 73796
>language R
>version.string R version 3.4.3 (2017-11-30)
>nickname Kite-Eating Tree
>
>______________________________________________
>R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
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>PLEASE do read the posting guide
>http://www.R-project.org/posting-guide.html
>and provide commented, minimal, self-contained, reproducible code.
______________________________________________
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
