I'm revising a paper on pollination, where the data are binomially distributed (fruit matures or does not). So I used
glmer with one random effect (individual plant) and one fixed effect (treatment). A reviewer wants to know whether plant had an effect on fruit set -- but I'm having trouble interpreting the
I've read around the web and it seems there can be issues with directly comparing
glmer models, so I'm not doing that. I figured the most straightforward way to answer the question would be to compare the random effect variance (1.449, below) to the total variance, or the variance explained by treatment. But how do I calculate these other variances? They don't seem to be included in the output below. I read something about residual variances not being included for binomial
glmer -- how do I interpret the relative importance of the random effect?
> summary(exclusionM_stem) Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) [glmerMod] Family: binomial ( logit ) Formula: cbind(Fruit_1, Fruit_0) ~ Treatment + (1 | PlantID) AIC BIC logLik deviance df.resid 125.9 131.5 -59.0 117.9 26 Scaled residuals: Min 1Q Median 3Q Max -2.0793 -0.8021 -0.0603 0.6544 1.9216 Random effects: Groups Name Variance Std.Dev. PlantID (Intercept) 1.449 1.204 Number of obs: 30, groups: PlantID, 10 Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) -0.5480 0.4623 -1.185 0.2359 TreatmentD -1.1838 0.3811 -3.106 0.0019 ** TreatmentN -0.3555 0.3313 -1.073 0.2832 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Correlation of Fixed Effects: (Intr) TrtmnD TreatmentD -0.338 TreatmentN -0.399 0.509