# SensitivityResult


Result of a smoothing-parameter sensitivity analysis.


Usage

``` python
SensitivityResult(
    multipliers,
    predictions,
    edf_total,
    deviance_explained,
    gcv_scores,
    aic_values,
    smoothing_params,
    baseline_idx,
)
```


Shows how predictions and fit statistics change as the smoothing parameters are scaled by a set of multipliers around their estimated (or fixed) values. Each row corresponds to one multiplier value applied uniformly to all smoothing parameters.


## Attributes


`multipliers: NDArray`  
Multiplier values used, shape `(n_steps,)`.

`predictions: NDArray`  
Fitted values at each multiplier, shape `(n_steps, n_obs)`.

`edf_total: NDArray`  
Total effective degrees of freedom at each step, shape `(n_steps,)`.

`deviance_explained: NDArray`  
Deviance explained at each step, shape `(n_steps,)`.

`gcv_scores: NDArray`  
GCV score at each step, shape `(n_steps,)`.

`aic_values: NDArray`  
AIC at each step, shape `(n_steps,)`.

`smoothing_params: NDArray`  
Actual smoothing parameters used, shape `(n_steps, n_penalties)`.

`baseline_idx: int`  
Index into `multipliers` corresponding to the original fit (multiplier closest to 1).


## Attributes

| Name | Description |
|----|----|
| [baseline_predictions](#baseline_predictions) | Predictions at the baseline (original) smoothing parameters. |
| [n_obs](#n_obs) | Number of observations. |
| [n_steps](#n_steps) | Number of multiplier steps. |

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


### baseline_predictions


Predictions at the baseline (original) smoothing parameters.


`baseline_predictions: NDArray`


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


### n_obs


Number of observations.


`n_obs: int`


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


### n_steps


Number of multiplier steps.


`n_steps: int`


## Methods

| Name | Description |
|----|----|
| [max_abs_change()](#max_abs_change) | Maximum absolute prediction change relative to baseline, per step. |

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


### max_abs_change()


Maximum absolute prediction change relative to baseline, per step.


Usage

``` python
max_abs_change()
```


#### Returns


`NDArray`  
Shape `(n_steps,)`. The entry at `baseline_idx` is zero.