Re: [R] weighted regression inside FOREACH loop

[William Dunlap via R-help]

foreach's '.export' argument lists the objects that should be copied to the
environment in which the expression is to be evaluated, which is not the
global environment.  In your example, environment(formula) is the global
environment so lm(formula, data=d, weights=weights) only looks in the
data.frame d and the global environment for a variable called 'weights'.

The following example uses the subset argument instead of weights.  Note
that all the variables involved are exported, but to the environment in
which coef(lm(...)) will be evaluated, not the global environment, hence
they are not found by lm (really, model.frame) when it looks in
environment(formula).

> d <- data.frame(x=log(1:4), y=1:4)
> formula <- y ~ x
> subsets <- list(1:2, 2:3, 3:4)
> foreach(k=1, .combine=list) %dopar% { list(
FormulaObjects=objects(environment(formula), all=TRUE),
LocalObjects=objects(environment(), all=TRUE),
tryCatch(error=function(e)conditionMessage(e), coef(lm(data=d, formula,
subset=subsets[[k]] }
$FormulaObjects
[1] ".Random.seed"

$LocalObjects
[1] "d"   "formula" "k"   "subsets"

[[3]]
[1] "object 'subsets' not found"

Using the temporary child environment for things like this is helpful is
lots of situations, not just when using foreach.


Bill Dunlap
TIBCO Software
wdunlap tibco.com

On Fri, Oct 7, 2016 at 9:23 AM, Bos, Roger <roger@rothschild.com> wrote:

> 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
>
>
>
>
>
> This message and any attachments are for the intended recipient’s use
> only.
>
> This message may contain confidential, proprietary or legally privileged
>
> information. No right to confidential or privileged treatment
>
> of this message is waived or lost by an error in transmission.
>
> If you have received this message in error, please immediately
>
> notify the sender by e-mail, delete the message, any attachments and all
>
> copies from your system and destroy any hard copies.  You must
>
> not, directly or indirectly, use, disclose, distribute,
>
> print or copy any part of this message or any attachments if you are not
>
> the intended recipient.
>
> *From:* William Dunlap [mailto:wdun...@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
>
>
>
> On Fri, Oct 7, 2016 at 8:18 AM, William Dunlap <wdun...@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)
>
> #
>
>
>
>
>
>
> Bill Dunlap
> TIBCO Software
> wdunlap tibco.com
>
>
>
> On Fri, Oct 7, 2016 at 7:44 AM, Bos, Roger <roger@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 

Re: [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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the intended recipient.
From: William Dunlap [mailto:wdun...@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 
<wdun...@tibco.com<mailto:wdun...@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)
#



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

On Fri, Oct 7, 2016 at 7:44 AM, Bos, Roger 
<roger@rothschild.com<mailto:roger@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-boun...@r-project.org<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.





***
This message and any attachments are for the intended recipient's use only.
This message may contain confidential, proprietary or legally privileged 
information. No right to confi

Re: [R] weighted regression inside FOREACH loop

[William Dunlap via R-help]

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 <wdun...@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)
> #
>
>
>
> Bill Dunlap
> TIBCO Software
> wdunlap tibco.com
>
> On Fri, Oct 7, 2016 at 7:44 AM, Bos, Roger <roger@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$coef)
>> }
>> Models
>>
>>
>>
>>
>>
>> -Original Message-
>> From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of Bos,
>> Roger
>> Sent: Friday, October 07, 2016 9:25 AM
>> To: R-help
>> Subject: [R] weighted regression inside FOREACH loop
>>
>> I have a foreach loop that runs regressions in parallel and works fine,
>> but when I try to add the weights parameter to the regression the
>> coefficients don’t get stored in the “models” variable like they are
>> supposed to.  Below is my reproducible example:
>>
>> library(doParallel)
>> cl <- makeCluster(4)
>> registerDoParallel(cl)
>> fmla <- as.formula("y ~ .")
>> models <- foreach(d=1:10, .combine=rbind, .errorhandling='remove')
>> %dopar% {
>>   datdf <- data.frame(y = 1:100+2*rnorm(100), x = 1:100+rnorm(100))
>>   weights <- rep(c(1,2), 50)
>>   mod <- lm(fmla, data=datdf, weights=weights)
>>   #mod <- lm(fmla, data=datdf)
>>   return(mod$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.
>>
>>
>>
>>
>>
>> ***
>> This message and any attachments are for the intended recipient's use
>> only.
>> This message may contain confidential, proprietary or legally privileged
>> information. No right to confidential or privileged treatment of this
>> message is waived or lost by an error in transmission.
>> If you have received this message in error, please immediately notify the
>> sender by e-mail, delete the message, any attachments and all copies from
>> your system and destroy any hard copies.  You must not, directly or
>> indirectly, use, disclose, distribute, print or copy any part of this
>> message or any attachments if you are not the intended recipient.
>>
>

Re: [R] weighted regression inside FOREACH loop

[William Dunlap via R-help]

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)
#



Bill Dunlap
TIBCO Software
wdunlap tibco.com

On Fri, Oct 7, 2016 at 7:44 AM, Bos, Roger <roger@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$coef)
> }
> Models
>
>
>
>
>
> -Original Message-
> From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of Bos, Roger
> Sent: Friday, October 07, 2016 9:25 AM
> To: R-help
> Subject: [R] weighted regression inside FOREACH loop
>
> I have a foreach loop that runs regressions in parallel and works fine,
> but when I try to add the weights parameter to the regression the
> coefficients don’t get stored in the “models” variable like they are
> supposed to.  Below is my reproducible example:
>
> library(doParallel)
> cl <- makeCluster(4)
> registerDoParallel(cl)
> fmla <- as.formula("y ~ .")
> models <- foreach(d=1:10, .combine=rbind, .errorhandling='remove') %dopar%
> {
>   datdf <- data.frame(y = 1:100+2*rnorm(100), x = 1:100+rnorm(100))
>   weights <- rep(c(1,2), 50)
>   mod <- lm(fmla, data=datdf, weights=weights)
>   #mod <- lm(fmla, data=datdf)
>   return(mod$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.
>
>
>
>
>
> ***
> This message and any attachments are for the intended recipient's use only.
> This message may contain confidential, proprietary or legally privileged
> information. No right to confidential or privileged treatment of this
> message is waived or lost by an error in transmission.
> If you have received this message in error, please immediately notify the
> sender by e-mail, delete the message, any attachments and all copies from
> your system and destroy any hard copies.  You must not, directly or
> indirectly, use, disclose, distribute, print or copy any part of this
> message or any attachments if you are not the intended recipient.
>
> __
> R-help@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/
> posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
> __
> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
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> posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.

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Re: [R] weighted regression inside FOREACH loop

[Bos, Roger]

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.





***
This message and any attachments are for the intended recipient's use only.
This message may contain confidential, proprietary or legally privileged 
information. No right to confidential or privileged treatment of this message 
is waived or lost by an error in transmission.
If you have received this message in error, please immediately notify the 
sender by e-mail, delete the message, any attachments and all copies from your 
system and destroy any hard copies.  You must not, directly or indirectly, use, 
disclose, distribute, print or copy any part of this message or any attachments 
if you are not the intended recipient.

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and provide commented, minimal, self-contained, reproducible code.

Re: [R] weighted regression inside FOREACH loop

[Thierry Onkelinx]

Dear Roger,

Maybe you want to return(mod) instead of return(mod$coef)

Best regards,

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey

2016-10-07 15:24 GMT+02:00 Bos, Roger :

> 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.
>
>
>
>
>
> ***
> This message and any attachments are for the intended recipient's use only.
> This message may contain confidential, proprietary or legally privileged
> information. No right to confidential or privileged treatment
> of this message is waived or lost by an error in transmission.
> If you have received this message in error, please immediately
> notify the sender by e-mail, delete the message, any attachments and all
> copies from your system and destroy any hard copies.  You must
> not, directly or indirectly, use, disclose, distribute,
> print or copy any part of this message or any attachments if you are not
> the intended recipient.
>
> __
> R-help@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/
> posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.

[[alternative HTML version deleted]]

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[R] weighted regression inside FOREACH loop

[Bos, Roger]

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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