Hi David,
Thank you very much for your answer. It helps me a lot. The offset
argument was the key (I didn't understand the description in the R-help
file)
Rereading my email I found a mistake in the definition of my formula.
Instead of p(y) = exp(a + c1*x1 + c2*x2), it has to be: p(y) = exp(a +
c1*x1 + c2*x2)/(1+exp(a + c1*x1 + c2*x2)), but actually that doesn't
matter much in our case.
The anova results would have not much interpretability in this
setting. You would be testing for the Intercept being zero under very
artificial conditions. You have eliminated much statistical meaning by
forcing the form of the results.
Imagine the following. I develop a model on one dataset and want to
validate it on another. So I could use the coefficents trained on the
first dataset to define a glm model (named: ModelV) on the second
dataset. Then i could test this model against a NULL model (named:
ModelV0) of the second dataset with anova(ModelV, ModelV0, test="Chisq").
Best Wishes
Jürgen
--
-----------------------------------
Jürgen Biedermann
Blücherstraße 56
10961 Berlin-Kreuzberg
Mobil: +49 176 247 54 354
Home: +49 30 250 11 713
e-mail: juergen.biederm...@gmail.com
--------- Korrespondenz ----------
Betreff: Re: [R] Define a glm object with user-defined coefficients
(logistic regression, family="binomial")
Von: David Winsemius <dwinsem...@comcast.net>
An: Jürgen Biedermann <juergen.biederm...@googlemail.com>
Datum: 13.11.2010 17:15
On Nov 13, 2010, at 7:43 AM, Jürgen Biedermann wrote:
Hi there,
I just don't find the solution on the following problem. :(
Suppose I have a dataframe with two predictor variables (x1,x2) and
one depend binary variable (y). How is it possible to define a glm
object (family="binomial") with a user defined logistic function like
p(y) = exp(a + c1*x1 + c2*x2) where c1,c2 are the coefficents which I
define. So I would like to do no fitting of the coefficients. Still,
I would like to define a GLM object because I could then easily use
other functions which need a glm object as argument (e.g. I could use
the anova,
The anova results would have not much interpretability in this
setting. You would be testing for the Intercept being zero under very
artificial conditions. You have eliminated much statistical meaning by
forcing the form of the results.
summary functions).
# Assume dataframe name is dfrm with variables event, no_event, x1,
x2, and further assume c1 and c2 are also defined:
dfrm$logoff <- with(dfrm, log(c1*x1 + c2*x2))
forcedfit <- glm( c(event,no_event) ~ 1 + offset(logoff), data=dfrm)
(Obviously untested.)
Thank you very much! Greetings
Jürgen
--
-----------------------------------
Jürgen Biedermann
David Winsemius, MD
West Hartford, CT
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