Re: [Rd] proposed simulate.glm method

From: Heather Turner <Heather.Turner_at_warwick.ac.uk>
Date: Fri, 13 Feb 2009 15:52:37 +0000

Yes, thanks to Ben for getting the ball rolling. His code was more streamlined than mine, pointing to further simplifications which I've included in the extended version below.

The code for the additional families uses functions from MASS and SuppDists - I wasn't sure about the best way to do this, so have just used :: for now.

It appears to be working happily for both glm and gnm objects (no gnm-specific code used).

Best wishes,

Heather

simulate.glm <- function (object, nsim = 1, seed = NULL, ...) {

    fam <- object$family$family
    if(fam == "gaussian")

        return(simulate.lm(object, nsim=nsim, seed=seed, ...))

    if(!exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE))
	runif(1)		       # initialize the RNG if necessary
    if(is.null(seed))
	RNGstate <- get(".Random.seed", envir = .GlobalEnv)
    else {
	R.seed <- get(".Random.seed", envir = .GlobalEnv)
	set.seed(seed)
	RNGstate <- structure(seed, kind = as.list(RNGkind()))
	on.exit(assign(".Random.seed", R.seed, envir = .GlobalEnv))
    }
    ## get probabilities/intensities
    pred <- object$fitted
    ntot <- length(pred) * nsim
    val <- switch(fam,

"poisson" = rpois(ntot, pred),
"binomial" = {

                      wts <- object$prior.weights
                      if (any(wts %% 1 != 0))
                          stop("cannot simulate from non-integer
prior.weights")
		      rbinom(ntot, size = wts, prob = pred)/wts
		  },
                  "Gamma" = {
                      shape <- MASS::gamma.shape(object)$alpha
                      rgamma(ntot, shape = shape, rate = shape/pred)
                  },
                  "inverse.gaussian" = {
                      lambda <- 1/summary(object)$dispersion
                      SuppDists::rinvGauss(ntot, nu = pred, lambda = lambda)
                  },
		  stop("family '", fam, "' not yet implemented"))
    ans <- as.data.frame(matrix(val, ncol = nsim))     attr(ans, "seed") <- RNGstate
    ans
}

Martin Maechler wrote:

> Thanks a lot, Heather,
> 
>>>>>> "HT" == Heather Turner <Heather.Turner_at_warwick.ac.uk>
>>>>>>     on Fri, 13 Feb 2009 11:49:06 +0000 writes:
> 
>     HT> Dear Martin,
>     HT> I think a simulate.glm method ought to be able to work for gnm objects
>     HT> too. David Firth and I started to work on this a long time ago, but
>     HT> stopped part-way through when simulate.lm was introduced, thinking that
>     HT> simulate.glm was probably in the pipeline and we were duplicating
>     HT> effort. Obviously we have let this slip when a contribution might have
>     HT> been useful. We developed a prototype for poisson, binomial, gaussian,
>     HT> gamma and inverse gaussian models which might be usefully merged with
>     HT> Ben's proposed simulate.glm. What's the best way to go about this? I
>     HT> would also like to test the proposed simulate.glm to check whether it
>     HT> will work with gnm objects or whether a simulate.gnm will be necessary.
> 
> In the mean time, private e-mail communications have started on
> the subject, and yes, we are very insterested in finding 
> ``the best'' possible way, probably making use of
> Heather+David's code together with Ben's. 
> One alternative (not mentioned yet on R-devel), we've been
> considering is to use simulate.lm() to also deal with "glm" (and
> possibly "gnm") objects ``in one place''.
> 
> Martin 
> 
> 
>     HT> Martin Maechler wrote:
>     >>>>>>> "BB" == Ben Bolker <bolker_at_ufl.edu>
>     >>>>>>> on Thu, 12 Feb 2009 11:29:14 -0500 writes:
>     >> 
>     BB> I have found the "simulate" method (incorporated
>     BB> in some packages) very handy. As far as I can tell the
>     BB> only class for which simulate is actually implemented
>     BB> in base R is lm ... this is actually a little dangerous
>     BB> for a naive user who might be tempted to try
>     BB> simulate(X) where X is a glm fit instead, because
>     BB> it defaults to simulate.lm (since glm inherits from
>     BB> the lm class), and the answers make no sense ...
>     >> 
>     BB> Here is my simulate.glm(), which is modeled on
>     BB> simulate.lm .  It implements simulation for poisson
>     BB> and binomial (binary or non-binary) models, should
>     BB> be easy to implement others if that seems necessary.
>     >> 
>     BB> I hereby request comments and suggest that it wouldn't
>     BB> hurt to incorporate it into base R ...  (I will write
>     BB> docs for it if necessary, perhaps by modifying ?simulate --
>     BB> there is no specific documentation for simulate.lm)
>     >> 
>     BB> cheers
>     BB> Ben Bolker
>     >> 
>     >> [...............]
>     >> 
>     >> Hi Ben,
>     >> thank you for your proposals.
>     >> 
>     >> I agree that  simulate.glm() has been in missing in some way,
>     >> till now, in particular, as, as you note, "glm" objects extend
>     >> "lm" ones and hence  simulate(<glm>, ...) currently dispatches to
>     >> calling simulate.lm(....) which is only correct in the case of
>     >> the gaussian family.
>     >> 
>     >> I have looked at your proposal a bit, already "improved" the
>     >> code slightly (e.g. re-include the comment you lost when you
>     >> ``copied'' simulate.lm():  In such cases, please work from the
>     >> source, not from what you get by print()ing
>     >> stats:::simulate.lm --- the source is either a recent tarball,
>     >> or the SVN repository, in this case, file
>     >> https://svn.r-project.org/R/trunk/src/library/stats/R/lm.R ]
>     >> and am planning to look at your and some own examples; 
>     >> all with the goal to indeed include this in the R standard
>     >> 'stats' package in R-devel [to become R 2.9.0 in the future].
>     >> 
>     >> About the help page:  At the moment, I think that only a few
>     >> words would need to be added to the simulate help page,
>     >> i.e., https://svn.r-project.org/R/trunk/src/library/stats/man/simulate.Rd
>     >> and will be happy to receive a patch against this file.
>     >> 
>     >> Thank you again, and best regards,
>     >> Martin Maechler, ETH Zurich

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