log-linear

The presence/absence nature of the outcome variable strongly supports
using logistic regression and nothing else.  I strongly encourage you
to stick with logistic regression.  The model formula and interaction
term capabilities in R are just the same for logistic regression as for
log-linear models.  (In some textbooks, log-linear models are used as
the motivation and example for introducing the ideas of interaction
terms, but once introduced, the ideas apply very generally.)

I would set up the data as you have, as a data frame or a matrix with
columns representing the number of landslide presence cells, the number
of landslide absence cells, and then one column for each predictor.

Then use  glm() with a call something like:

result <- glm(cbind(present, absent) ~ (a+b+c+d)^3,  family=binomial,
                 data = name.of.data.frame)

In  help("glm"), there's a sentence under "Details" which describes
the cbind() syntax I've used above, and  help("formula")  explains
the (.)^3 syntax.

-  tom blackwell  -  u michigan medical school  -  ann arbor  -

hello

I have spatial data which contain number of landslide presence cells
with respect to landslide predictors and number of landslide absence
cells with respect to same predictors.

predictors are essentially categorical data.

I tried logistic regression. But because of providing interaction
capability of predictors, I want to use log-linear method.
I hesitate the way I should use landslide count as response variable.
only landslide presence data should be regarded ? or both landslide
presence and absent data should be regarded as response variable ?

I will appreciate if anyone can supply information

thanks in advance

Ahmet Temiz
Gen Dir of Disaster of Affairs
TURKEY