PPCResult
Result of a posterior predictive check on a fitted GAM.
Usage
PPCResult(
y_rep,
observed,
_stats=dict(),
)Attributes
y_rep: NDArray-
Posterior predictive draws on the response scale, shape
(n, n_sim). Each column is one draw from the posterior predictive distribution (a plausible dataset the model could have generated). observed: NDArray-
Observed response values used to fit the model, shape
(n,).
Attributes
| Name | Description |
|---|---|
| stat_names | Names of the computed test statistics. |
stat_names
Names of the computed test statistics.
stat_names: list[str]
Methods
| Name | Description |
|---|---|
| p_value() | Bayesian p-value for a named test statistic. |
| stat() |
Return (observed_value, rep_values) for a named statistic.
|
p_value()
Bayesian p-value for a named test statistic.
Usage
p_value(name)The Bayesian p-value is the proportion of replicated datasets for which the test statistic equals or exceeds the observed value: p = P(T(y^\text{rep}) \ge T(y^\text{obs})).
Values near 0.5 indicate good calibration. Values near 0 or 1 indicate systematic discrepancy between the model and the data.
Parameters
name: str-
One of
"mean","sd","min","max","prop_zero".
stat()
Return (observed_value, rep_values) for a named statistic.
Usage
stat(name)