Table 6.
Posterior summary matrices of the simulation due to sampling importance resampling algorithm using the same function
| Logistic model (k =1) | |||||||
|---|---|---|---|---|---|---|---|
| Parameter | Mean | SD | MCSE | ESS | LB | Median | UB |
| Beta[1] | 62.73 | 6.34 | 0.06 | 10000 | 50.08 | 62.84 | 75.23 |
| Beta[2] | -17.30 | 1.81 | 0.02 | 10000 | -20.88 | -17.33 | -13.66 |
| Log.sigma | -0.14 | 0.10 | 0.00 | 10000 | -0.32 | -0.14 | 0.06 |
| Deviance | 283.20 | 2.46 | 0.02 | 10000 | 280.38 | 282.56 | 289.75 |
| LP | -160.93 | 1.23 | 0.01 | 10000 | -164.20 | -160.61 | -159.52 |
| Sigma | 0.88 | 0.09 | 0.00 | 10000 | 0.72 | 0.87 | 1.06 |
| Weibull model (k =30) | |||||||
| Parameter | Mean | SD | MCSE | ESS | LB | Median | UB |
| Beta[1] | 65.18 | 5.86 | 0.06 | 10000 | 53.79 | 65.20 | 76.77 |
| Beta[2] | -17.83 | 1.67 | 0.02 | 10000 | -21.16 | -17.83 | -14.58 |
| Log.sigma | 0.25 | 0.09 | 0.00 | 10000 | 0.08 | 0.25 | 0.43 |
| Deviance | 278.35 | 2.40 | 0.02 | 10000 | 275.57 | 277.72 | 284.52 |
| LP | -158.50 | 1.20 | 0.01 | 10000 | -161.59 | -158.19 | -157.11 |
| Sigma | 1.29 | 0.11 | 0.00 | 10000 | 1.09 | 1.29 | 1.54 |