log-linear

1.  What did you use for logistic regression?  "glm"?  If your 
response variable is "number of landslides", I would think that "glm" 
with "family = poisson" might be appropriate.  Have you checked the R 
help for "?glm" and "?family" and the R search site at 
"http://www.r-project.org/" -> search -> "R search site"?  In 
particular, if you don't have "Modern Applied Statistics with S" by 
Venables and Ripley (2002), I suggest you get a copy.  This is the best 
reference I know on R.  If you've digested Venables and Ripley, at least 
on "glm", the next best book I know for your issues may be  McCullagh P. 
and Nelder, J. A. (1989) Generalized Linear Models (London: Chapman and 
Hall).

	  2.  You can use interactions with logistic regression, as you could 
with Poisson regression, "glm(..., family = poisson)".  If your 
explanatory variables are all categorical, then you might have a problem 
with estimating too many parameters:  If you have 5 categories in one 
variable and 7 in another, the main effects will estimate 4=(5-1) and 
6=(7-1) parameters, and the interaction will involve 4*6 = 24 
parameters.  Moreover, if you do NOT have data on at least 24 
sufficiently different combinations out of the 5*7 = 35 possible, you 
won't be able to estimate all the parameters in the interaction.  I 
suggest you try to construct at least ordinal scales, code the 
categories as numbers whereever that might be done plausibly, then look 
for linear terms, parabolics, etc., and linear*linear interactions, 
etc., THEN look for large residuals from the fitted model.

Hope this helps,
Spencer Graves

log-linear

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