| | Mean | Std. dev. | MCSE | Median | [95% Cred. interval] |
|---|
Equal-tailed |
d.v.: IMTG | | | | | |
i.v.: IFN-alpha 2 | − 0.0151632 | 0.004142 | 0.000122 | − 0.0151286 | − 0.0229845 to 0.0069051 |
Cons | 4.46095 | 0.5110447 | 0.016827 | 4.460191 | 3.442408 to 5.436929 |
Sigma2 | 0.8045077 | 0.1360474 | 0.003159 | 0.7905886 | 0.5719823 to 1.09947 |
- Default priors are used for model parameters. Simulations introduce an additional level of uncertainty to the accuracy of the estimates. Monte Carlo standard error (MCSE), which is the standard error of the posterior mean estimate, measures the simulation accuracy
- Using Bayesian linear regression we asked which parts (if any) of its fit to the data was it confident about, and which parts were very uncertain (perhaps based entirely on the priors). Looking at the ratio of MCSE to Std. dev. (in this case 0.000122/0.004142 = 0.029) we have 2.9%, i.e., minus than 5% (significant auto correlation)
- Little of the posterior variability is due to simulation, thus the model is valid
- d.v. dependent variable, i.v. independent variable
