Bojanowscy wrote:
>
> Hello
>
> I would be very grateful if anyone of you could give me (in short) a list of
> assumptions about data (dimensions, frequencies in contingency table,
> distribution etc) under which one can perform Loglinear analysis (ML
> estimation).
Log-linear analysis assumes that the data follows a Poisson distribution
- this would be a counting problem (of the sort 'how many butterflies
will fly past me today?')
> ...And, if there are any diffrences, in Logit modelling.
>
Yes! This would assume a Binomial distribution - this is sampling from
a finite group with replacement (something like 'of the 25 species of
butterfly in this area, how many have I seen today?).
Both models assume that the observations are independent, and that there
is a constant rate or probability (butterflies may not meet these
assumptions!)
> I've read a few books in the subject (written for social scientists, so not
> very "mathematical"), but I haven't found a full list of assumptions
> underlying these procedures.
>
The 'classic' is probably McCullagh & Nelder (1989) Generalized Linear
Models (2nd ed., Chapman & Hall). Although it's primarily aimed at
statisticians, it is quite readable.
Bob (who would rather spend today counting butterflies....)
--
Bob O'Hara
Metapopulation Research Group
Division of Population Biology
Department of Ecology and Systematics
PO Box 17 (Arkadiankatu 7)
FIN-00014 University of Helsinki
Finland
tel: +358 9 191 7382 fax: +358 9 191 7301
email: [EMAIL PROTECTED]
To induce catatonia, visit:
http://www.helsinki.fi/science/metapop/
I have yet to see any problem, however complicated, which, when you
looked at it in the right way, did not become still more complicated. -
Poul Anderson
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