lmer vs glmmPQL
Federico Calboli wrote:
On 23 Jun 2009, at 22:46, Ken Beath wrote:
This seems to results from the use of a t-test with few df in glmmPQL and z in lmer. z seems fine to me. What is more of a problem is that your random effects variance is effectively 0. There are only 3 blocks so fitting a random effects model will be difficult and appears unnecessary.
That was a sample dataset so I could see what kind of data I had to deal with, the 'real' hing should have a variance > 0 for the random effect. My philosphycal issue was, given such a relatively straightforward model, should I be more (glmmPQL) or less (lmer) conservative? Best, Federico
My take would be to pick lmer over glmmPQL every time, provided it can handle your problem -- in general it should be more accurate. Picking on the basis of more or less conservative for any given problem feels biased. Ben Bolker