I am trying to understand when to use a random effect and when it is unnecessary. Ive been told a rule of thumb is if you have 4 or more groups/individuals which I do (15 individual moose). Some of those moose were experimented on 2 or 3 times for a total of 29 trials. I want to know if they behave differently when they are in higher risk landscapes than not. So, I thought I would set the individual as a random effect. However, I am now being told that there is no need to include the individual as a random effect because there is not a lot of variation in their response. What I can't figure out is how to test if there really is something being accounted for when setting individual as a random effect. Maybe an initial question is: What test/diagnostic can I do to figure out if Individual is a good explanatory variable and should it be a fixed effect - qq plots? histograms? scatter plots? And what would I look for in those patterns.
I ran the model with the individual as a random effect and without, but then I read http://glmm.wikidot.com/faq where they state:
do not compare lmer models with the corresponding lm fits, or glmer/glm; the log-likelihoods are not commensurate (i.e., they include different additive terms)
And here I assume this means you can't compare between a model with random effect or without. But I wouldn't really know what I should compare between them anyway.
In my model with the Random effect I also was trying to look at the output to see what kind of evidence or significance the RE has
lmer(Velocity ~ D.CPC.min + FD.CPC + (1|ID), REML = FALSE, family = gaussian, data = tv) Linear mixed model fit by maximum likelihood Formula: Velocity ~ D.CPC.min + FD.CPC + (1 | ID) Data: tv AIC BIC logLik deviance REMLdev -13.92 -7.087 11.96 -23.92 15.39 Random effects: Groups Name Variance Std.Dev. ID (Intercept) 0.00000 0.00000 Residual 0.02566 0.16019 Number of obs: 29, groups: ID, 15 Fixed effects: Estimate Std. Error t value (Intercept) 3.287e-01 5.070e-02 6.483 D.CPC.min -1.539e-03 3.546e-04 -4.341 FD.CPC 1.153e-04 1.789e-05 6.446 Correlation of Fixed Effects: (Intr) D.CPC. D.CPC.min -0.010 FD.CPC -0.724 -0.437
You see that my variance and SD from the individual ID as the random effect = 0. How is that possible? What does 0 mean? Is that right? Then my friend who said "since there is no variation using ID as random effect is unnecessary" is correct? So, then would I use it as a fixed effect? But wouldn't the fact that there is so little variation mean it isn't going to tell us much anyway?