I'm pretty new to GLMMs and model averaging, but think I'm getting some
understanding of it all through lots of reading. However, I keep receiving
an error message when trying to average models that I don't understand and
can't find any resources about. I'm doing science education research trying
to evaluate population demographic factors that predict biology student
math performance. I have a lot of factors and so I tested a lot of models.
6 of my models had pretty similar AIC values (and evidence ratios of less
than 2.7) so I'm trying to average them. I keep receiving an error message
that says the models are not fitted to the same data, but I have no idea
how this is possible because all the models are from the same set of data
(same file and same variables)...strangely it seems to work when I try to
average MEx7, MEx10, & MEx22 only OR MEx24, MEx29, and MEx47 only. My code
is below. Any ideas? Thanks for any advice you can offer!!
library(MuMIn)
MEx7=lmer(cbind(c.score, w.score) ~ year + transfer + gender + p.math +
(1|section) + (1|quarter), family=binomial, data=survey.full, REML=F)
MEx10=lmer(cbind(c.score, w.score) ~ transfer + gender + p.math + Pmajor +
(1|section) + (1|quarter), family=binomial, data=survey.full, REML=F)
MEx22=lmer(cbind(c.score, w.score) ~ year + transfer + p.math + (1|section)
+ (1|quarter), family=binomial, data=survey.full, REML=F)
MEx24=lmer(cbind(c.score, w.score) ~ transfer + gender + p.math +
(1|section) + (1|quarter), family=binomial, data=survey.full, REML=F)
MEx29=lmer(cbind(c.score, w.score) ~ transfer + p.math + Pmajor +
(1|section) + (1|quarter), family=binomial, data=survey.full, REML=F)
MEx47=lmer(cbind(c.score, w.score) ~ transfer + p.math + (1|section) +
(1|quarter), family=binomial, data=survey.full, REML=F)
MExAvg=model.avg(rank=AIC, MEx24, MEx7, MEx10, MEx47, MEx29, MEx22)
Error in model.avg.default(rank = AIC, MEx24, MEx7, MEx10, MEx47, MEx29, :
models are not all fitted to the same data
Lilly Dethier
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