Without seeing the model I'm not clear on the cause. In general it is not
very useful to compute 1 d.f. tests for parameters that represent only part
of a meaningful hypothesis.
Note that anova.lrm computes Wald statistics, which are subject to the
Hauck-Donner effect (which may not be the cause
Greg Snow imail.org> writes:
> The individual tests on coefficients in logistic regression
> are generally based on a Wald test statistic.
> Unfortunately there is a bit of a paradox possible in this
> case where the coefficient is highly
> significant, but due to a flattening of the likelihoo
To: r-help@r-project.org
Subject: [R] When models and anova(model) disagree...
I have a situation where the parameter estimates from lrm identify a
binary predictor variable ("X") as clearly non-significant (p>0.3), but
the ANOVA of that same model gives X a chi^2-df rank of > 20
I have a situation where the parameter estimates from lrm identify a
binary predictor variable ("X") as clearly non-significant (p>0.3), but
the ANOVA of that same model gives X a chi^2-df rank of > 200, and
adjudicates X and one interaction of X and a continuous measure as
highly significa
4 matches
Mail list logo
