TABLE 2.
Model choice parameters for the Scots pine data
Diameter (×105)
|
Height (×105)
|
|||||||
---|---|---|---|---|---|---|---|---|
Model for y | Gm | Pm | Dm | AIC | Gm | Pm | Dm | AIC |
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46.45 | 47.75 | 94.20 | 0.18492 | 84.01 | 86.59 | 170.6 | 0.09909 |
![]() |
41.67 | 46.95 | 88.63 | 0.18378 | 61.10 | 79.86 | 140.9 | 0.09498 |
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41.61 | 46.07 | 87.68 | 0.18361 | 49.04 | 78.95 | 128.0 | 0.09473 |
A lower posterior predictive loss statistic indicates a better model.
is a goodness-of-fit term, whereas
is a penalty term. AIC is the standard Akaike's information criterion (calculated as number of fixed-effect classes plus number of variance components).