logit regression, test among groups

Dear all:
My logistic regression model includes one qualitative and one quantitative
predictor variable,

aes <- glm(p.a ~ spp * log(light), family=binomial(link=logit)),

where spp is abundance of 3 species and light is subcanopy light
availability varying from 0  1.

  I want to test differences among levels of the quantitative variable at a
value of x other than the current log(light)=0.  I tried using
I(log(light+.5)) but this caused serious lack of fit (AIC went from 1574 to
1656, and diagnostic plots demonstrated poor fit). How would create a model
statement such that I test differences among groups at log(light)=approx. 2?
Many thanks.

aes <- glm(p.a ~ spp * I(log(light)-log(0.5)),
		family=binomial(link=logit)),

will test at light==0.5, and so on.

Theoretically it's horribly inefficient to waste perhaps whole tenths of a
second refitting the model when you can in fact compute these contrasts
from the fitted parameters and covariance matrices, and Frank Harrell's
solution is much nicer.


	-thomas

-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-
r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html
Send "info", "help", or "[un]subscribe"
(in the "body", not the subject !)  To: r-help-request at stat.math.ethz.ch
_._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._