One of the problems I've always had with mixed models is figuring out data visualizations - of the kind that could end up on a paper or poster - once one has the results.
Right now, I'm working on a Poisson mixed effects model with a formula that looks something like the following:
a <- glmer(counts ~ X + Y + Time + (Y + Time | Site) + offset(log(people))
With something fitted in glm() one could easily use the predict() to get predictions for a new data set, and build something off of that. But with output like this - how would you construct something like a plot of the rate over time with the shifts from X (and likely with a set value of Y)? I think one could predict the fit well enough just from the Fixed effects estimates, but what about the 95% CI?
Is there anything else someone can think of that help visualize results? The results of the model are below:
Random effects: Groups Name Variance Std.Dev. Corr Site (Intercept) 5.3678e-01 0.7326513 time 2.4173e-05 0.0049167 0.250 Y 4.9378e-05 0.0070270 -0.911 0.172 Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) -8.1679391 0.1479849 -55.19 < 2e-16 X 0.4130639 0.1013899 4.07 4.62e-05 time 0.0009053 0.0012980 0.70 0.486 Y 0.0187977 0.0023531 7.99 1.37e-15 Correlation of Fixed Effects: (Intr) Y time X -0.178 time 0.387 -0.305 Y -0.589 0.009 0.085