[R] weighted regression inside FOREACH loop

[Bos, Roger]

Bill,

Thanks for your help.  Not that I ever doubted you, but I tried your method on my actual data and I can confirm it does work.  I guess I am still wondering why using .export in foreach doesn’t allow the variable to be found as that method would seem to be the most straightforward.

Thanks again for your help!

Roger




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From: William Dunlap [mailto:wdunlap at tibco.com]
Sent: Friday, October 07, 2016 11:57 AM
To: Bos, Roger
Cc: R-help
Subject: Re: [R] weighted regression inside FOREACH loop

Using the temporary child environment works because model.frame, hence lm, looks for the variables used in the formula, subset, and weights arguments first in the data argument and then, if the data argument is not an environment, in the environment of the formula argument.

Bill Dunlap
TIBCO Software
wdunlap tibco.com<http://tibco.com>

On Fri, Oct 7, 2016 at 8:18 AM, William Dunlap <wdunlap at tibco.com<mailto:wdunlap at tibco.com>> wrote:
A more general way is to change the environment of your formula to
a child of its original environment and add variables like 'weights' or
'subset' to the child environment.  Since you change the environment
inside a function call it won't affect the formula outside of the function call.
E.g.

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))
  localEnvir <- new.env(parent=environment(fmla))
  environment(fmla) <- localEnvir
  localEnvir$weights <- rep(c(1,2), 50)
  mod <- lm(fmla, data=datdf, weights=weights)
  return(mod$coef)
}
models
#          (Intercept)         x
#result.1  -0.16910860 1.0022022
#result.2   0.03326814 0.9968325
#result.3  -0.08177174 1.0022907
#...
environment(fmla)
#<environment: R_GlobalEnv>



Bill Dunlap
TIBCO Software
wdunlap tibco.com<http://tibco.com>

On Fri, Oct 7, 2016 at 7:44 AM, Bos, Roger <roger.bos at rothschild.com<mailto:roger.bos at rothschild.com>> wrote:
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-bounces at r-project.org<mailto:r-help-bounces at 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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