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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