# LOOResult


Result of PSIS-LOO cross-validation on a fitted GAM.


Usage

``` python
LOOResult(
    elpd_loo,
    se_elpd_loo,
    p_loo,
    pointwise,
    pareto_k,
    n_bad_k,
)
```


## Attributes


`elpd_loo: float`  
Expected log predictive density (ELPD_LOO), summed over observations. Higher is better.

`se_elpd_loo: float`  
Approximate standard error of `elpd_loo`, computed as `sqrt(n * var(pointwise))`.

`p_loo: float`  
Effective number of parameters (LOO penalty). Computed as `lpd_full - elpd_loo` where `lpd_full` is the log-likelihood at the posterior mean. Large `p_loo` relative to the actual parameter count suggests model misspecification.

`pointwise: NDArray`  
Per-observation LOO log predictive density values, shape `(n,)`.

`pareto_k: NDArray`  
Per-observation Pareto `k` diagnostic, shape `(n,)`. Values above 0.7 indicate that the PSIS approximation is unreliable for that observation; values above 1.0 indicate the importance weights have infinite variance and LOO is invalid.

`n_bad_k: int`  
Number of observations with `pareto_k > 0.7`.