statisticalrethinkin..

6.5. USING AIC 209kcal.per.g0.5 0.7 0.90.55 0.65 0.75FIGURE 6.10. Model averaged posteriorpredictive distribution for the primatemilk analysis. e black regressionline and dashed 95% confidenceinterval correspond to the minimum-AICc model, m6.14. e gray line andshaded 95% confidence region correspondto the model averaged predictions.neocortexinformation—which way to zero, usually—but greatly clutter the presentation and generate opticalillusions. Better to remove them and use dotcharts instead of barplots. R’s dotchart function issufficient to produce FIGURE 6.9. It’s usually all you need, but there are more flexible options. Anybook on R graphics will present several.6.5.2. Model averaging. Way back in Chapter 3, we saw how to preserve the uncertaintyabout parameters when simulating predictions from a model. Now we have the analogousproblem of preserving the uncertainty about models. Treating model weights as heuristicplausibilities that each model will perform best in testing, it makes sense to try to preservethese relative plausibilities when generating predictions. And doing so is mechanically verysimilar to the procedure with a single model.To review, let’s simulate and plot counterfactual predictions for the minimum-AICcmodel, m6.14. Here’s the familiar code for simulating the posterior predictive distribution,focusing on counterfactual predictions across the range of neocortex.post