Chain summarization with summary() #9

## Summarize posterior samples for g and 1-s ----

chain_samples = burnin:chainLength
mGs = apply(outchain$GamS[, chain_samples], 1, mean)
sGs = apply(outchain$GamS[, chain_samples], 1, sd)
m1mSS = 1 - apply(outchain$SigS[, chain_samples], 1, mean)
s1mSS = apply(outchain$SigS[, chain_samples], 1, sd)
output = cbind(mGs, sGs, m1mSS, s1mSS)
colnames(output) = c('g Est', 'g SE', '1-s Est', '1-s SE')
rownames(output) = paste('Item', 1:J)
print(output, digits = 3)
                            
## Summarize marginal skill distribution ---- 

# Via posterior samples for latent class proportions
PIoutput = cbind(apply(outchain$PIs, 1, mean), apply(outchain$PIs, 1, sd))
colnames(PIoutput) = c('EST', 'SE')
rownames(PIoutput) = apply(As, 1, paste0, collapse='')
print(PIoutput, digits = 3)

summary(outchain)