R help - Jul 2008 - Running "all possible subsets" of a GLM (binomial) model

I have spent a fair amount of time looking for a package that is automated
to run glm (binomial) regression models with all possible subsets of my
independent variables.  Something akin to Lumley's "leaps"
package, but can
be applied to glms, not just lms; or something similar to Stata's brute
force "tryem" function?  If anyone can point me in the right direction
I
would really appreciate it.

Thanks, Eric vdWal

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On Mon, Jul 7, 2008 at 10:18 AM, Eric Vander Wal <ejvander at
lakeheadu.ca> wrote:
> I have spent a fair amount of time looking for a package that is automated
> to run glm (binomial) regression models with all possible subsets of my
> independent variables.  Something akin to Lumley's "leaps"
package, but can
> be applied to glms, not just lms; or something similar to Stata's brute
> force "tryem" function?  If anyone can point me in the right
direction I
> would really appreciate it.
Have a look at fitall in the meifly package:

fitall <- function(y, x, method=lm, ...) {
	data <- cbind(y=y, x)

	combs <- do.call(expand.grid, rep(list(c(FALSE, TRUE)), ncol(x)))[-1, ]

	vars <- apply(combs, 1, function(i) names(x)[i])
	form <- paste("y ~ ", lapply(vars, paste, collapse=" +
"), sep = "")
	form <- lapply(form, as.formula)

	models <- lapply(form, function(f) eval(substitute(method(f,
data=data, ...), list(f=f, data=data, method=method))))
	names(models) <- 1:length(models)
	class(models) <- c("ensemble", class(models))
	models
}

That should get you started -  the meifly package also contains a few
functions for summarising and visualising these ensembles of models.
See http://had.co.nz/meifly/ for a little more detail, and a paper
using meifly for a simple case study.

Hadley


-- 
http://had.co.nz/

Eric Vander Wal <ejvander <at> lakeheadu.ca> writes:
> 
> I have spent a fair amount of time looking for a package that is automated
> to run glm (binomial) regression models with all possible subsets of my
> independent variables.  Something akin to Lumley's "leaps"
package, but can
> be applied to glms, not just lms; or something similar to Stata's brute
> force "tryem" function?  If anyone can point me in the right
direction I
> would really appreciate it.
> 
> Thanks, Eric vdWal
> 
> 	[[alternative HTML version deleted]]
> 
  There is a "dRedging" package at

http://www.zbs.bialowieza.pl/users/kamil/r/

that is oriented toward model-averaging but
might do what you want.  (The potential for
abuse of this package makes me terribly
nervous, but if used with great care and
common sense I can see that it can be
useful.)

  Ben Bolker