# check()


Produce GAM diagnostic plots.


Usage

``` python
check(
    model,
    plots=None,
)
```


Provides the standard suite of residual diagnostics used to assess GAM fit quality, analogous to `mgcv::gam.check()` in R. All requested diagnostics are returned as a single vertically concatenated Altair chart so calling `wk.check(model)` as the last expression in a cell displays inline. Available plots (selected via `plots=`):

- `"qq"`: QQ plot of deviance residuals against theoretical normal quantiles. Systematic curvature away from the reference line suggests the response distribution (family) may be misspecified.
- `"residuals"`: Pearson residuals vs fitted values. A even, patternless scatter around zero is the target; funnel shapes suggest heteroscedasticity (consider a location-scale family), and curvature suggests a missing or under-smoothed term.
- `"histogram"`: Histogram of deviance residuals, for checking overall symmetry and shape.
- `"response"`: Observed response vs fitted values, with a 1:1 reference line, for an overall sense of fit quality and to spot outliers.


## Parameters


`model: GAM`  
A fitted GAM.

`plots: tuple[str, …] | list[str] | None = None`  
Which diagnostic plots to include. Pass a list of names (e.g., `["qq", "residuals"]`) or `None` (default) for all four, in the order `"qq"`, `"residuals"`, `"histogram"`, `"response"`.


## Returns


`altair.VConcatChart`  
All requested diagnostic plots stacked vertically into a single chart.


## Examples


``` python
import numpy as np
from whittaker.gam import GAM
from whittaker.plotting import check

rng = np.random.default_rng(0)
n = 300
x = rng.uniform(0, 1, n)
y = np.sin(2 * np.pi * x) + rng.normal(scale=0.2, size=n)

model = GAM("y ~ s(x)").fit({"x": x, "y": y})
chart = check(model, plots=["qq", "residuals"])
print(type(chart).__name__)
```


    VConcatChart