Message-ID: <47CAB67C.6080003@vanderbilt.edu>
Date: 2008-03-02T14:15:24Z
From: Frank E Harrell Jr
Subject: difference between lrm's "Model L.R." and anova's "Chi-Square"
In-Reply-To: <1204447088.47ca67702ab44@webmail.sas.upenn.edu>
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