# compare()


Compare multiple fitted GAMs in a summary table.


Usage

``` python
compare(*models)
```


Collects AIC, BIC, deviance explained, adjusted R-squared, EDF, GCV (when available), and scale from each model, sorts by AIC, and computes delta-AIC from the best model.


## Parameters


`*models: GAM`  
Two or more fitted GAM objects.


## Returns


`ComparisonResult`  
Comparison table sorted by AIC (best first).


## Raises


`ValueError`  
If fewer than 2 models are provided.


## Examples


``` python
import numpy as np
import whittaker as wk

rng = np.random.default_rng(0)
x = np.linspace(0, 2 * np.pi, 200)
y = np.sin(x) + rng.normal(0, 0.3, 200)
data = {"x": x, "y": y}

m1 = wk.GAM("y ~ x").fit(data)
m2 = wk.GAM("y ~ s(x, k=5)").fit(data)
m3 = wk.GAM("y ~ s(x, k=15)").fit(data)

print(wk.compare(m1, m2, m3))
```


    Model Comparison (3 models, 200 observations)

      # Formula                               AIC     ΔAIC        BIC  Dev.Expl.   Adj.R²     EDF        GCV
    --- ------------------------------ ---------- -------- ---------- ---------- -------- ------- ----------
      1 y ~ s(x, k=5)                       78.32    +0.00      94.67     86.7%   0.8636     5.0   0.086642
      2 y ~ s(x, k=15)                      79.21    +0.89     103.14     87.0%   0.8646     7.3   0.087059
      3 y ~ x                              332.62  +254.30     339.22     51.1%   0.5065     2.0   0.308909