statisticalrethinkin..

350 13. MULTILEVEL MODELS)sigma_actor ~ dcauchy(0,1),sigma_block ~ dcauchy(0,1)) ,data=list(pulled.left=d$pulled.left,prosoc.left=d$prosoc.left,condition=d$condition,actor=as.integer(d$actor),block_num=as.integer(d$block)) ,start=list(a=0,bp=0,bpc=0,sigma_actor=1,sigma_block=1,aj=rep(0,7),ak=rep(0,6)),warmup=1000 , iter=1e4Looking at estimates:R code13.17precis(m13.4)Mean StdDev lower 0.95 upper 0.95a 0.27 0.63 -0.99 1.52bp 0.77 0.25 0.31 1.26bpc -0.09 0.28 -0.63 0.44sigma_actor 2.16 0.84 0.94 3.76sigma_block 0.23 0.19 0.01 0.56aj[1] -0.98 0.66 -2.22 0.41aj[2] 4.22 1.51 1.73 7.31aj[3] -1.28 0.67 -2.65 -0.01aj[4] -1.28 0.66 -2.59 0.06aj[5] -0.98 0.66 -2.32 0.32aj[6] -0.03 0.66 -1.35 1.25aj[7] 1.50 0.71 0.15 2.93ak[1] -0.17 0.23 -0.68 0.18ak[2] 0.04 0.19 -0.34 0.46ak[3] 0.06 0.19 -0.31 0.49ak[4] 0.01 0.19 -0.39 0.40ak[5] -0.03 0.19 -0.44 0.33ak[6] 0.12 0.21 -0.26 0.57A lot of variation among chimpanzees (actor), as you saw in Chapter 11. But not much byexperiment block (block).13.4.1. Marginal posterior predictions. Simulating over varying intercepts.R code13.18post