R help - Oct 2002 - New home for S Poetry

Many readers on this list probably don't know that S Poetry
had an old home.  S Poetry is a book in pdf about programming
in the S language.  It was written mainly with S-PLUS 3.x in
mind when R was still an infant relative to what it is now.

Some parts of the book are still quite accurate for R.  In other
places R is rather different from what is in the book.  The FAQ
on the differences between R and S-PLUS at the R Project is
a good mediator between R and S Poetry.

S Poetry can be found on http://www.burns-stat.com/

Also on that site in the public domain area are three  functions
that run in both R and S-PLUS (corner, head, tail).  These are
useful interactively and occasionally in programming.

There is also a tutorial -- really an advertisement -- on the S
language.

Patrick Burns
patrick at burns-stat.com
+44 (0) 208 525 0696
http://www.burns-stat.com/


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Dear all,
  does anyone know how to setup a generalized linear model for data looking
like
the following example. This would be an experiment on food preferences for,
say,
snails, and I would measure for each specimen its choice made on food type
food
plus one or more quantitative (or factorial) explanatory variables, here
represented
by the snail size.
SpeciesSelected    SnailSize
    Spc1                      2.7
    Spc2                      3.5
    Spc1                      2.9
    Spc3                      4.6

.....

I believe that even in the situation where my explanatory variables were
categorial,
I shall not use the loglinear models (i.e. modelling counts and moving the
"SpeciesSelected"
descriptor to the right side, studying its interaction terms with the
predictors), because
the total count has an upper bound determined by the experimental design and I
shall
model the probability of selecting particular plant species given snail size.
Doing separate models with binomial distribution for each food type is not
correct, either,
because the constrain of probabilities summing up to 1.0 is violated.

Does anyone have any thoughts on this issue? This shall not be so rare type
of analysis, given the various customer preference studies. Of course, I can
use
classification trees (or neural-network models), but I would like to use more
parametric
description of dependence.

Thank you for any help you might provide!


Best wishes
                        from

--------------------------------------------------------------
Petr Smilauer
Faculty of Biological Sciences
Ceske Budejovice, Czech Republic
--------------------------------------------------------------
Course Multivariate Analysis of Ecological Data <http://regent.bf.jcu.cz>


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