It's the (typical) na.action = na.omit problem. You have missing values in your data, so the number of observations differs between models using different variables.
BTW with the recent lme4 package, your code throws a lot of warnings about the use of lmer with non-gaussian family and ignored REML argument. Also, consider using "update" rather than rewriting the models each time. kamil On 2013-11-15 17:10, Lilly Dethier wrote:
Of course! Here's my data file and R code file. Thanks so much for your help!! Lilly Dethier On Fri, Nov 15, 2013 at 8:14 AM, Kamil Bartoń <k.bar...@abdn.ac.uk <mailto:k.bar...@abdn.ac.uk>> wrote: works ok with mock-up data. Can you give some code to reproduce this error? kamil On 2013-11-15 11:00, r-help-requ...@r-project.org <mailto:r-help-requ...@r-project.org> wrote: Message: 56 Date: Thu, 14 Nov 2013 18:01:27 -0800 From: Lilly Dethier<lillydeth...@gmail.com <mailto:lillydeth...@gmail.com>__> To:r-help@r-project.org <mailto:to%3ar-h...@r-project.org> Subject: [R] Error in MuMIn "models are not all fitted to the same data" Message-ID: <CAOK+e=Z_0pMEFKdPxZ5Eub+__DYhHFjzGk3Lcqczsa9TimAP4n_w@__mail.gmail.com <mailto:z_0pmefkdpxz5eub%2bdyhhfjzgk3lcqczsa9timap4...@mail.gmail.com>> Content-Type: text/plain 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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