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

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?

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?