On Wed, 1 Sep 2010, GMail (KU) wrote:
Hello,
I was looking for a way to evaluate the goodness-of-fit of a logistic regression model. After googling, I
found that I could use "resid(fit, 'gof')" method implemented in the rms package. However, since I
am not used to the "le Cessie-van Houwelingen normal test statistic," I do not know which statistic
from the returned from the "resid(fit, 'gof')" call that I could use to evaluate the goodness of
fit.
When I ran the "resid(fit, 'gof')", I got the following results:
##############################################
Sum of squared errors Expected value|H0 SD
6844.684594 6805.672315 2.790969
Z P
13.978043 0.000000
##############################################
I tried to read the le Cessie and van Houwelingen's original paper, but I found
that it required prerequisite knowledge I don't current have.
Could someone explain how to interpret the results from "resid(fit, 'gof') call?
Any help would be much appreciated.
Young-Jin Lee
Young-Jin,
I think everyone has trouble interpreting omnibus tests of lack of
fit, so don't feel bad. You just know that something somewhere is
probably wrong with the model. I focus on directed tests such as
allowing all continuous variables to have nonlinear effects or
allowing selected interactions, and finding out how important the
complex model terms are.
Frank Harrell
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