The interest is the group-level effect for “corrZ” per “rsfm” level. Below is the model summary:
Family: categorical
Links: mu1 = logit; mu2 = logit; mu3 = logit
Formula: Y ~ corrZ + (1 + corrZ | rsfm)
Data: dat (Number of observations: 959066)
Samples: 4 chains, each with iter = 1000; warmup = 500; thin = 1; total post-warmup samples = 2000
Group-Level Effects:
~rsfm (Number of levels: 247)
Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
sd(mu1_Intercept) 0.00 0.00 0.00 0.01 2000 1.0
sd(mu1_corrZ) 0.22 0.05 0.11 0.32 430 1.0
sd(mu2_Intercept) 0.00 0.00 0.00 0.01 2000 1.0
sd(mu2_corrZ) 0.05 0.03 0.00 0.13 838 1.0
sd(mu3_Intercept) 0.00 0.00 0.00 0.01 2000 1.0
sd(mu3_corrZ) 0.15 0.07 0.01 0.28 234 1.03
cor(mu1_Intercept,mu1_corrZ) -0.08 0.58 -0.95 0.94 27 1.10
cor(mu2_Intercept,mu2_corrZ) -0.01 0.58 -0.95 0.94 1205 1.0
cor(mu3_Intercept,mu3_corrZ) 0.04 0.57 -0.92 0.95 260 1.01
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
mu1_Intercept -3.13 0.01 -3.14 -3.12 2000 1.00
mu2_Intercept -3.04 0.01 -3.05 -3.03 2000 1.00
mu3_Intercept -3.46 0.01 -3.47 -3.45 2000 1.00
mu1_corrZ -0.13 0.03 -0.19 -0.07 2000 1.00
mu2_corrZ -0.05 0.03 -0.10 -0.00 2000 1.00
mu3_corrZ 0.03 0.03 -0.04 0.09 2000 1.00