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GLM Contingency table regressions

2 messages · Paul M. Jacobson, Martin Maechler

Original
Paul M. Jacobson
# 
I have a contingency table with a 0/1 variable (fwr) that flags a particular
condition, a weight variable (cnt) and other variables (e.g. zz)
characterizing that observation of the table.
I am trying to use GLM. By converting the variables to factors and running
the following regression:
fwr1<-factor(fwr)
zz1<-factor(zz1)
res1<-glm(fwr1 ~ zz1,weights=cnt)

Is that appropriate?

Paul M. Jacobson
Jacobson Consulting Inc.
80 Front Street East, Suite 720
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E-mail: pmj at jciconsult.com
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1 day later
Martin Maechler
# 
PaulMJ> I have a contingency table with a 0/1 variable (fwr)
    PaulMJ> that flags a particular condition, a weight variable
    PaulMJ> (cnt) and other variables (e.g. zz) characterizing
    PaulMJ> that observation of the table.  I am trying to use
    PaulMJ> GLM. By converting the variables to factors and
    PaulMJ> running the following regression:

    PaulMJ> fwr1<-factor(fwr)
    PaulMJ> zz1<-factor(zz1)
    PaulMJ> res1<-glm(fwr1 ~ zz1,weights=cnt)

(spaces around "<-" would improve readibility}

    PaulMJ> Is that appropriate?

not quite.
Assuming that you really want to predict (a function of) the
probability   P[ fwr = 1 | zz1 ]
you need at least to add

	 family = binomial

to your glm() arguments.
The thing you did above was least-squares regression with a 0/1 variable
which is probably not what you wanted.

Martin Maechler <maechler at stat.math.ethz.ch>	http://stat.ethz.ch/~maechler/
Seminar fuer Statistik, ETH-Zentrum  LEO C16	Leonhardstr. 27
ETH (Federal Inst. Technology)	8092 Zurich	SWITZERLAND
phone: x-41-1-632-3408		fax: ...-1228			<><
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