# PartialDependenceResult


Partial dependence data for one smooth term.


Usage

``` python
PartialDependenceResult(
    term,
    x,
    effect,
    se,
    lower,
    upper,
    edf,
    level,
)
```


Contains the evaluation grid, estimated effect, standard errors, and confidence bounds for a single smooth term. This is the data underlying [partial_effects()](partial_effects.md#whittaker.partial_effects) plots, exposed as arrays for custom plotting or downstream analysis.


## Attributes


`term: str`  
Term label (e.g. `"s(x)"`).

`x: dict[str, NDArray]`  
Evaluation grid. For 1-D smooths, a single key mapping to a 1-D array. For 2-D smooths, two keys mapping to 1-D marginal grids (use `np.meshgrid` to expand).

`effect: NDArray`  
Estimated partial effect at each grid point, shape `(n_grid,)`.

`se: NDArray`  
Standard errors, shape `(n_grid,)`.

`lower: NDArray`  
Lower confidence bound, shape `(n_grid,)`.

`upper: NDArray`  
Upper confidence bound, shape `(n_grid,)`.

`edf: float`  
Effective degrees of freedom for this term.

`level: float`  
Confidence level used for the bounds.


## Attributes

| Name | Description |
|----|----|
| [n_grid](#n_grid) | Number of evaluation points. |

------------------------------------------------------------------------


### n_grid


Number of evaluation points.


`n_grid: int`