statsmodels.gam.generalized_additive_model.GLMGam.select_penweight¶
- GLMGam.select_penweight(criterion='aic', start_params=None, start_model_params=None, method='basinhopping', **fit_kwds)[source]¶
find alpha by minimizing results criterion
The objective for the minimization can be results attributes like
gcv
,aic
orbic
where the latter are based on effective degrees of freedom.Warning: In many case the optimization might converge to a local optimum or near optimum. Different start_params or using a global optimizer is recommended, default is basinhopping.
- Parameters:
- criterion=’aic’
name of results attribute to be minimized. Default is ‘aic’, other options are ‘gcv’, ‘cv’ or ‘bic’.
- start_params
None
orarray
starting parameters for alpha in the penalization weight minimization. The parameters are internally exponentiated and the minimization is with respect to
exp(alpha)
- start_model_params
None
orarray
starting parameter for the
model._fit_pirls
.- method‘basinhopping’, ‘nm’ or ‘minimize’
‘basinhopping’ and ‘nm’ directly use the underlying scipy.optimize functions basinhopping and fmin. ‘minimize’ provides access to the high level interface, scipy.optimize.minimize.
- fit_kwds
keyword
arguments
additional keyword arguments will be used in the call to the scipy optimizer. Which keywords are supported depends on the scipy optimization function.
- Returns:
- alpha
ndarray
penalization parameter found by minimizing the criterion. Note that this can be only a local (near) optimum.
- fit_res
tuple
results returned by the scipy optimization routine. The parameters in the optimization problem are log(alpha)
- history
dict
history of calls to pirls and contains alpha, the fit criterion and the parameters to which pirls converged to for the given alpha.
- alpha
Notes
In the test cases Nelder-Mead and bfgs often converge to local optima, see also https://github.com/statsmodels/statsmodels/issues/5381.
This does not use any analytical derivatives for the criterion minimization.
Status: experimental, It is possible that defaults change if there is a better way to find a global optimum. API (e.g. type of return) might also change.