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

[Frank E Harrell Jr]

johnson4 at babel.ling.upenn.edu wrote:
> Quoting Frank E Harrell Jr <f.harrell at vanderbilt.edu>:
>> 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?

The overall LR chi-square test statistic is in the standard output of 
lrm (which uses print.lrm).

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

Please check the lmer documentation.

Frank

> 


-- 
Frank E Harrell Jr   Professor and Chair           School of Medicine
                      Department of Biostatistics   Vanderbilt University