Re: [R] difference between lrm's "Model L.R." and anova's "Chi-Square"

From: <>
Date: Sun, 02 Mar 2008 03:38:08 -0500

Quoting Frank E Harrell Jr <>:
> anova (anova.Design) computes Wald statistics. When the log-likelihood
> is very quadratic, these statistics will be very close to log-likelihood
> ratio chi-square statistics. In general LR chi-square tests are better;
> we use Wald tests for speed. It's best to take the time and do
> lrtest(fit1,fit2) in Design, where one of the two fits is a subset of
> the other.
> Frank Harrell

Thanks, this is great, but in my case, there's just one factor,

fit1 <- lrm(outcome~factor,data)

and I'm having trouble constructing the subset 'null model', as e.g.

fit2 <- lrm(outcome~1,data)

returns an error message.

How do I construct a null model with lrm() so that I can use lrtest() to test a model with only one predictor?

I apologize for asking what must be a very simple question but I have been unable to find the answer by searching R-help.


P.S. Second point: I have another case where I use lmer(), and there the null model includes a random effect so I don't get the problem above. It looks like with lmer objects anova() uses LLR, not Wald. Is that right? mailing list PLEASE do read the posting guide and provide commented, minimal, self-contained, reproducible code. Received on Sun 02 Mar 2008 - 08:40:43 GMT

Archive maintained by Robert King, hosted by the discipline of statistics at the University of Newcastle, Australia.
Archive generated by hypermail 2.2.0, at Sun 02 Mar 2008 - 15:30:18 GMT.

Mailing list information is available at Please read the posting guide before posting to the list.

list of date sections of archive