[R] compare GLM coefficients

Michael Bedward michael.bedward at gmail.com
Tue Nov 23 07:55:58 CET 2010

Hello Kayce,

My (very basic) understanding is that you can't directly compare the
coefficients across models that have different response variables, nor
could you use AIC and similar metrics of model goodness of fit.
Instead, I think you have to carefully define what you mean by "reveal
similar population trends".

If you treat the model with the count response as your reference, and
it predicts (for example) population decline of magnitude X over
period T, then you can investigate to what extent this same trend is
retrieved by the presence response model. But the specifics of the
comparison(s) should be closely tied to the population behaviours /
syndromes / critical points that you are most interested in. If there
are multiple behaviours of interest you want to know to what extent
the presence data perform as well as the count data for each of them.

That's my general take on the style of the approach. Hopefully others
here will have more detailed and knowledgable comments for you.


On 23 November 2010 17:20, Kayce anderson <kaycelu at gmail.com> wrote:
> I have a data set of repeated abundance counts over time.  I am
> investigating whether count data reduced to presence-absence (presence) data
> will reveal similar population trends.  I am using a negative binomial
> distribution for the glm (package MASS) because the count data contains many
> zeros and extreme values.  "count" and "presence" are annual sums for each
> metric.  I have also included sampling effort (visits) as an independent
> variable because sampling varies between 29-33 visits per year.  My models
> are:
> glm.nb(count ~ year + visits) and
> glm.nb(presence ~ year + visits)
> I would like to test whether the coefficients for "year" are significantly
> different between models.  Please advise me on the best method to make such
> a comparison.
> Thank you,
> Kayce
>        [[alternative HTML version deleted]]
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