statisticalrethinkin..

150 5. MULTIVARIATE LINEAR MODELSdata=dcc ,start=list(a=mean(dcc$kcal.per.g) , bn=0 , bm=0 ,sigma=sd(dcc$kcal.per.g)) )precis(m5.7)Mean StdDev 2.5% 97.5%a -1.08 0.47 -2.00 -0.17bn 0.03 0.01 0.01 0.04bm -0.10 0.02 -0.14 -0.05sigma 0.11 0.02 0.08 0.15By incorporating both predictor variables in the regression, the estimated association of bothwith the outcome has increased. e posterior mean for the association of neocortex percenthas increased more than 6-fold, and it’s 95% interval is now entirely above zero. e posteriormean for log body mass is more strongly negative.Let’s plot the intervals for the predicted mean kilocalories, for this new model. Here’sthe code for the relationship between kilocalories and neocortex percent. ese are counterfactualplots, so we’ll use the mean log body mass in this calculation, showing only howpredicted energy varies as a function of neocortex percent:R code5.26mean.log.mass