All,
I figured out how to get it to work, so I am posting the solution in case
anyone is interested. I had to use attr to set the weights as an attribute of
the data object for the linear model. Seems convoluted, but anytime I tried to
pass a named vector as the weights the foreach loop could not find the
variable, even if I tried exporting it. If anybody knows of a better way
please let me know as this does not seem ideal to me, but it works.
library(doParallel)
cl <- makeCluster(4)
registerDoParallel(cl)
fmla <- as.formula("y ~ .")
models <- foreach(d=1:10, .combine=rbind, .errorhandling='pass') %dopar% {
datdf <- data.frame(y = 1:100+2*rnorm(100), x = 1:100+rnorm(100))
attr(datdf, "weights") <- rep(c(1,2), 50)
mod <- lm(fmla, data=datdf, weights=attr(data, "weights"))
return(mod$coef)
}
Models
-----Original Message-----
From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of Bos, Roger
Sent: Friday, October 07, 2016 9:25 AM
To: R-help
Subject: [R] weighted regression inside FOREACH loop
I have a foreach loop that runs regressions in parallel and works fine, but
when I try to add the weights parameter to the regression the coefficients
don’t get stored in the “models” variable like they are supposed to. Below is
my reproducible example:
library(doParallel)
cl <- makeCluster(4)
registerDoParallel(cl)
fmla <- as.formula("y ~ .")
models <- foreach(d=1:10, .combine=rbind, .errorhandling='remove') %dopar% {
datdf <- data.frame(y = 1:100+2*rnorm(100), x = 1:100+rnorm(100))
weights <- rep(c(1,2), 50)
mod <- lm(fmla, data=datdf, weights=weights)
#mod <- lm(fmla, data=datdf)
return(mod$coef)
}
models
You can change the commenting on the two “mod <-“ lines to see that the
non-weighted one works and the weighted regression doesn’t work. I tried using
.export="weights" in the foreach line, but R says that weights is already being
exported.
Thanks in advance for any suggestions.
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