Dear R experts,

In order to analyze a longitudinal data set, I used the lmer function from
the lme4 library for R to fit GLMM (with Poisson error structure) using
restricted maximum likelihood (*REML*), which corrects the estimates from
maximum likelihood method for degrees of freedom.  Estimated p values on F
ratios will be yet anti-conservative since the denominator degrees of
freedom (number of observations –  rank [X], where X is the model matrix for
fixed effects) is essentially an upper bound.  Ideal approach is to use
Markov chain Monte Carlo (MCMC) sampling  (mcmcsamp function) ; but since
mcmcsamp function available in R is broken,  assuming t distribution, I
decided to compute 95% confidence interval with conservative degrees of
freedom (number of observations – rank [X:Z], where Z is the model matrix
for random effects).

Dr. Bates have mentioned previously that it is not easy to assess rank of
Z:X numerically. An alternative is to estimate n - t where t is the trace of
the ‘hat matrix’.  But it seems that function ‘hatTrace’ is also broken. Is
it not working any more ?

I am curious if there is an alternative way to estimate the conservative
degrees of freedom accurately.  Also will mcmcsamp be active in the near
future ?

Thank you,

Byju N Govindan

Doctoral Candidate

Forestry and Natural Resources,

Purdue University, IN

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