Hello mixed-model folks;

I am assessing the significance of regression coefficients in quasi-poisson
mixed models using lmer.  Based on previous discussion in the mixed models
archives, it appears that p-values from likelihood ratio tests on this kind
of model may often be unreliable.

I have been debating with myself over the merits of two alternative
approaches to assess the significance of coefficients...

I have done likelihood ratio tests (comparing nested models), and I have
written a program in R to build 'bias-corrected and accelerated' bootstrap
confidence intervals for coefficient estimates.  In most cases the
inferences based on the bootstrap intervals and likelihood ratio tests
qualitatively agree (i.e. when the bootstrap confidence interval does not
contain zero the likelihood ratio test is significant for the particular
coefficient estimate). However in a few important cases, the inferences
based on bootstrap intervals and likelihood ratio tests are different (i.e.
a few coefficients would be determined to be siginficant based on the
bootstrap intervals and either marginally significant or insignificant based
on the likelihood ratio test).  I am attracted to the bootstrapping
confidence intervals approach because it appears that it is likely to be
more reliable.  I am attracted to the likelihood ratio tests because they
provide p-values (which editors and readers of scientific journals seem to
enjoy having).

Here is an example of the models I am working with:
nongene<-
lmer(data$offspring~data$age+data$weight+data$agesq+data$Het+(1|data$ID)+(1|factor(data$year)),family=quasipoisson,REML=FALSE,na.action=na.omit)

Do any of you have an opinion on which of these approaches may be best, or
ideas for better a better approach?

Thanks for any thoughts you might have!
-- 
Marty Kardos
Ph.D. Candidate
Montana Conservation Genetics Lab
Organismal Biology & Ecology
MEID IGERT Program
University of Montana

-- 
Marty Kardos
Ph.D. Candidate
Montana Conservation Genetics Lab
Organismal Biology & Ecology
MEID IGERT Program
University of Montana
406-599-1358
http://dbs.umt.edu/people/gradStudents.php

	[[alternative HTML version deleted]]


