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The `correct' way to do this is a product-multinomial model, but a
surrogate Poisson GLM can be used. (You do have counts, for each ABCD
combination how many, which might be zero, but they are not independent
joint Poisson. Hence `surrogate'.)
Examples and detailed explanation in MASS (the book) chapter 7, or good
books on categorical data (e.g. Agresti, 2002). Function multinom() in
library nnet is the main tool.
Because of Simpson's paradox it is dangerous to look at marginal tables
as you say you did. If you are unaware of that, please research it.
On Wed, 20 Apr 2005, Tristan Lorino wrote:
Hi,
I have four categorical variables (A, B, C and D) in column, and
around 1,000 observations. Variables "B", "C" and "D" are covariates,
and variable "A" the variable to explain. I computed crosstables for
A-B, A-C and A-D: each chi-square are significant. Now I would like
to perform a model with all the covariates in the same time.
I think that glm should be a good choice. But I cannot find the
good family/link option: "poisson" family seems to be only for
counts (this is not the case here) and "binomial" family (with
"logit" link) is only for binary outcomes (all my variables have more
than 2 levels).
The aim is to analyze the joint impact of the three covariates, and
maybe to determine the covariate the "most related" to A.
Another question still remains to me: how can I create a new file
with five columns, the first fourth presenting all the possible combinations
of the levels of my four variables (i.e. one combination by
line), and the fifth column (say E) containing the amount of
observations for each combination?
Thank you in advance,
Tristan Lorino
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Brian D. Ripley, ripley@stats.ox.ac.uk
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
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