Output: Ordered Logit model

Posterior means, posterior standard deviations and 95% credible intervals (Inference->Samples->stats)

*Pr(b.life>0|data) > 0.975

To estimate Pr(b.life>0|data), add the following line: p.life <- step(b.life)

And the Pr(b.life>0|data) is given by the posterior mean of p.life

*c[1] < c[2] < c[3]: thresholds

Sample path (history)

Posterior probability densities (Inference->Samples->density)

(Further, right click on the figure->Margins-> Special…->Smooth -> change from 0.2 to 0.1-> apply all)

Sample autocorrelation function (Inference->Samples->auto corr)

Running quantile plot (Inference->Samples->quantiles)

Scatter plots (Inference->Correlations…)

Prediction : Pr( mental>=2 | ses, life, data)

(Inference-> Compare-> node:mental.pred.0, other: mental.pred.1, axis:life.pred, and click on modelfit)

(Further, right-click on the figure, Titles -> x-axis: # life events, title: Pr(mental>=2|ses, life, data)

Pr(mental>=2|ses=0, life, data) 95% intervals (blue) and median (red)

Pr(mental>=2|ses=1, life, data) median (black dots)

(Inference-> Compare-> node:mental.pred.1, other: mental.pred.0, axis:life.pred, and click on modelfit)

(Further, right-click on the figure, Titles -> x-axis: # life events, title: Pr(mental>=2|ses, life, data)

Pr(mental>=2|ses=1, life, data) 95% intervals (blue) and median (red)

Pr(mental>=2|ses=0, life, data) median (black dots)