[R-sig-ME] binomial fixed-effect p-values by simulation

Ben Bolker bolker at ufl.edu
Sat Aug 23 01:40:16 CEST 2008

  I just wrote a "simulate" method for glmer objects, to extend the
version that's already in lme4 for lmer objects.  Given a nested pair
of models m0, m1, the simulation distribution of t-statistics would


See simulate.lme in nlme, which also has a plot method
that displays the nominal and empirical p-values for REML
and ML estimates. 

  The bad news is that it's essentially doing the same thing you
discuss below -- and therefore it's not any faster (I was able
to do about 10 replicates/minute ...)

  If you're interested let me know and I can send my
  Ben Bolker

Daniel Ezra Johnson wrote:
> Consider the common situation of modeling multiple responses from
> multiple subjects, where we want to know whether a fixed-effect
> between-subjects variable -- for example, gender -- has a significant
> effect.
> In my field (quantitative sociolinguistics) the response is usually
> binomial, and for some 30 years the usual procedure has been to carry
> out logistic regression, setting the subject variable aside, and
> assessing fixed effect significance with a likelihood-ratio test. When
> between-subject variation is high, this produces exceptionally
> anti-conservative p-values (see old.p.value below).
> I am writing an article and some wrapper functions designed to
> introduce the use of mixed models to this audience. With subject
> included as a random effect, the p-values for fixed between-subject
> effects are much more conservative, even using the likelihood-ratio
> test (see lrt.p.value below). In other words, this is an improvement.
> But as has been discussed many times on this list, likelihood-ratio
> tests are still anti-conservative for fixed effect significance. I
> believe the extent of anti-conservatism depends on the number of
> observations relative to the number of groups, the balance of the
> data, and probably other factors too.
> I have seen the MCMC method discussed, but I think this is not yet
> available for binomial data with lmer(), is that correct? In addition,
> that method, which operates in the parameter space, seems to be
> testing a slightly different null hypothesis than the usual
> frequentist one, though I may be misunderstanding this.
> Would it be appropriate for me to carry out a simulation of the type
> given below, to assess fixed effect significance?
> The code starts by generating a dataset which happens to have a
> potentially significant gender effect, according to the LRT. (In fact
> the data is randomly generated and has no "real" gender effect, but
> assume it was real data and we did not know that.)
> A null model (no fixed effect) and an alternative model (fixed gender
> effect) are fit to the data by lmer(), using a random effect for
> subject in both cases.
> It then takes the two parameters from the null model -- intercept and
> subject s.d. -- and randomly generates new data. The simulated data is
> fit with the alternative-style model and the proportion of times the
> fixed effect magnitude exceeds that from the 'observed' data is the
> p-value.
> Does this procedure make sense? It would generalize to linear models
> easily. Making it work for unbalanced data and when there are other
> fixed effects in the model would not be that hard either, I think.
> To my mind, this is a direct implementation of the frequentist p-value
> concept via simulation. Am I missing something important?
> Of course, especially with binomial data this simulation is quite slow
> (i.e. compared to the unimplemented MCMC method). The code below, with
> 1000 simulations of a 2000-observation dataset, took 15 minutes to run
> on a MacBook Pro. (The result was a p-value of 0.027, compared to
> 0.015 from the LRT, 0.009 from the Wald statistic... and 1.1e-56 from
> the fixed-effects model!)
> It's important to me to be able to give p-values that are
> accurate/conservative and that I understand. So if anyone can give me
> guidance on whether this simulation procedure makes sense, it would be
> much appreciated.
> And if there's a faster way to do something like this for binomial
> data, please let me know.
> Thanks,
> Daniel
> library(lme4)
> library(boot)
> set.seed(10)
> observed <- data.frame(subject = rep(letters[1:20],each=100),
> 	gender = rep(c("M","F"),each=1000),
> 	response = rbinom(2000,1,rep(plogis(rnorm(20,0,3)),each=100)))
> old <- glm(response~gender,binomial,observed)
> old.p.value <- pchisq(old$null.deviance-old$deviance,df=1,lower.tail=F)
> # 1.083e-56
> null <- lmer(response ~ (1|subject),observed,binomial)
> fixed <- lmer(response ~ gender + (1|subject),observed,binomial)
> wald.p.value <-  summary(fixed)@coefs["genderM","Pr(>|z|)"] # 0.00894
> lrt.p.value <- anova(null,fixed)[,"Pr(>Chisq)"][2] # 0.0152
> null.intercept <- fixef(null) # -0.0914
> null.subject.sd <- attr(VarCorr(null)$subject,"stddev") # 2.276
> gender.observed <- fixef(fixed)[2] # -2.319
> gender.simulated <- vector()
> for (i in 1:1000){
> simulated <- data.frame(subject = rep(letters[1:20],each=100),
> 	gender = rep(c("M","F"),each=1000),
> 	response = rbinom(2000,1,rep(plogis(rnorm(20,0,null.subject.sd)),each=100)))
> fixed.sim <- lmer(response~gender+(1|subject),simulated,binomial)
> gender.simulated[i] <- fixef(fixed.sim)[2]}
> # minutes later...
> simulation.p.value <- mean(abs(gender.simulated)>=abs(gender.observed)) # 0.027
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