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# linc_ig = alpha_g + beta_g * educ_i for individual i in cluster g | |
# want to estimate alpha_g and beta_g for each cluster | |
male_fit <- map2stan( # create output called male_fit | |
alist( | |
linc ~ dnorm( mu , sigma ), # likelihood: log(income) has normal distribution | |
mu <- alpha_attract[ornt] + # mean mu is linearly related to alpha_clusterName + beta_clusterName * education | |
beta_attract[ornt]*educ, | |
c(alpha_attract,beta_attract)[ornt] ~ dmvnorm2( c(alpha,beta) , sigma_attract , Rho ), # alpha_clusterName and beta_clusterName are jointly normaly distributed with means alpha and beta and standard deviations sigma_clusterName and correlation Rho | |
alpha ~ dnorm(11.235,1), # prior: alpha is normally distributed with mean log(75738) and standard deviation 1 | |
beta ~ dnorm(0.2,1), # prior: beta is normally distributed with mean 0.2 and standard deviation 1 | |
sigma ~ dcauchy(0,2), # prior: sigma is distrubted half cauchy with centre 0 and scale 2 | |
sigma_attract ~ dcauchy(0,2), # prior: sigma_clusterName are half cauchy distributed with centres 0 and scales 2 | |
Rho ~ dlkjcorr(2) # prior: Rho is distributed LKJcorr with scale parameter 2 | |
) , | |
data=d , warmup=1000 , iter=5000 , chains=1 , cores=1 ) # keep it simple for debugging |
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