How are degrees of freedom calculated for a linear mixed effects model? I was recently running a linear mixed effects model that contained 1 independent variable with 3 levels (1,2, and 3). The random effects included: individual animal ID and year as I expected these variables to account for some of the variability in the modelling procedure and wanted to make sure to account for it. However, to obtain the P-value, I ran a null model containing only the intercept and random effects and compared it via the anova() command to the full model. The degrees of freedom just seem a little odd given that the random effect for individual animal ID contains 171 individuals across 3 years. Any help would be much appreciated!
fit.lmer.model <- lmer(MCP ~ Season +(1|ID)+(1|Year), data=mydataframe,REML=FALSE) summary(fit.lmer.model) fit.lmer.null <- lmer(MCP ~ 1 + (1|ID)+(1|Year), data=mydataframe,REML=FALSE) summary(fit.lmer.null) anova(fit.lmer.null,fit.lmer.model) > anova(fit.lmer.null,fit.lmer.model) Data: mydataframe Models: fit.lmer.null: MCP ~ 1 + (1 | ID) + (1 | Year) fit.lmer.model: MCP ~ Season + (1 | ID) + (1 | Year) Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq) fit.lmer.null 4 2240.0 2252.6 -1116.0 2232.0 fit.lmer.model 5 2238.2 2253.9 -1114.1 2228.2 3.759 1 0.05252 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1