[R] weighted regression inside FOREACH loop

[William Dunlap]

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

On Fri, Oct 7, 2016 at 8:18 AM, William Dunlap <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
>
> On Fri, Oct 7, 2016 at 7:44 AM, Bos, Roger <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$coef)
>> }
>> Models
>>
>>
>>
>>
>>
>> -----Original Message-----
>> From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Bos,
>> Roger
>> Sent: Friday, October 07, 2016 9:25 AM
>> To: R-help
>> Subject: [R] weighted regression inside FOREACH loop
>>
>> I have a foreach loop that runs regressions in parallel and works fine,
>> but when I try to add the weights parameter to the regression the
>> coefficients don’t get stored in the “models” variable like they are
>> supposed to.  Below is my reproducible example:
>>
>> library(doParallel)
>> cl <- makeCluster(4)
>> registerDoParallel(cl)
>> fmla <- as.formula("y ~ .")
>> models <- foreach(d=1:10, .combine=rbind, .errorhandling='remove')
>> %dopar% {
>>   datdf <- data.frame(y = 1:100+2*rnorm(100), x = 1:100+rnorm(100))
>>   weights <- rep(c(1,2), 50)
>>   mod <- lm(fmla, data=datdf, weights=weights)
>>   #mod <- lm(fmla, data=datdf)
>>   return(mod$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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>> ______________________________________________
>> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide http://www.R-project.org/posti
>> ng-guide.html
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