I have computed GLMM using glmer in R. My response variable is species richness and my explanatory variable is grazing treatment (with three categories: cattle, sheep and ungrazed). In the model I have included site as a fixed variable and also a new object with the same number of variations as I have to attempt to account for underdispersal (`obs`): model2<-glmer(VegRichness~Grazing+(1|Site)+(1|obs),family="poisson",data=veg.rich) My output is below and the questions I have about it are: How do I interpret the fixed effects section? Cattle grazing seems to be missing in the oputput, is this because it is somehow incorporated into the intercept? > summary(model2) Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) [glmerMod] Family: poisson ( log ) Formula: VegRichness ~ Grazing + (1 | Site) + (1 | obs) Data: veg.rich AIC BIC logLik deviance df.resid 178.8 185.2 -84.4 168.8 22 Scaled residuals: Min.......... 1Q............ Median.... 3Q......... Max -1.4936... -0.5698..... -0.1928... 0.4923... 1.3646 Random effects: Groups ... Name...... Variance..... Std.Dev. obs......... (Intercept).. 0.00000.... 0.0000 Site......... (Intercept).. 0.03596.... 0.1896 Number of obs: 27, groups: obs, 27; Site, 3 Fixed effects: .......Estimate.... Std. Error..... z value... Pr(>|z|) (Intercept)............ 3.55358....... 0.12309....... 28.869..... < 2e-16 *** GrazingSheep...... 0.01242...... 0.07876........ 0.158....... 0.87467 GrazingUngrazed -0.27526..... 0.08503........ -3.237...... 0.00121 ** --- Signif. codes: 0 x***x 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Correlation of Fixed Effects: (Intr)....GrzngS GrazingShe......................... -0.322 GrzngUngrzd...................... -0.298... 0.466