statsmodels.gam.generalized_additive_model.GLMGam¶
-
class
statsmodels.gam.generalized_additive_model.
GLMGam
(endog, exog=None, smoother=None, alpha=0, family=None, offset=None, exposure=None, missing='none', **kwargs)[source]¶ Generalized Additive Models (GAM)
This inherits from GLM.
Warning: Not all inherited methods might take correctly account of the penalization. Not all options including offset and exposure have been verified yet.
- Parameters
- endogarray_like
The response variable.
- exogarray_like or
None
This explanatory variables are treated as linear. The model in this case is a partial linear model.
- smoother
instance
of
additive
smoother
class
Examples of smoother instances include Bsplines or CyclicCubicSplines.
- alpha
float
orlist
of
floats
Penalization weights for smooth terms. The length of the list needs to be the same as the number of smooth terms in the
smoother
.- family
instance
of
GLM
family
See GLM.
- offset
None
or array_like See GLM.
- exposure
None
or array_like See GLM.
- missing‘none’
Missing value handling is not supported in this class.
- **kwargs
Extra keywords are used in call to the super classes.
Notes
Status: experimental. This has full unit test coverage for the core results with Gaussian and Poisson (without offset and exposure). Other options and additional results might not be correctly supported yet. (Binomial with counts, i.e. with n_trials, is most likely wrong in pirls. User specified var or freq weights are most likely also not correct for all results.)
Methods
estimate_scale
(mu)Estimate the dispersion/scale.
estimate_tweedie_power
(mu[, method, low, high])Tweedie specific function to estimate scale and the variance parameter.
fit
([start_params, maxiter, method, tol, …])estimate parameters and create instance of GLMGamResults class
fit_constrained
(constraints[, start_params])fit the model subject to linear equality constraints
fit_regularized
([method, alpha, …])Return a regularized fit to a linear regression model.
from_formula
(formula, data[, subset, drop_cols])Create a Model from a formula and dataframe.
get_distribution
(params[, scale, exog, …])Return a random number generator for the predictive distribution.
hessian
(params[, pen_weight])Hessian of model at params
hessian_factor
(params[, scale, observed])Weights for calculating Hessian
hessian_numdiff
(params[, pen_weight])hessian based on finite difference derivative
information
(params[, scale])Fisher information matrix.
Initialize a generalized linear model.
loglike
(params[, pen_weight])Log-likelihood of model at params
loglike_mu
(mu[, scale])Evaluate the log-likelihood for a generalized linear model.
loglikeobs
(params[, pen_weight])Log-likelihood of model observations at params
predict
(params[, exog, exposure, offset, linear])Return predicted values for a design matrix
score
(params[, pen_weight])Gradient of model at params
score_factor
(params[, scale])weights for score for each observation
score_numdiff
(params[, pen_weight, method])score based on finite difference derivative
score_obs
(params[, pen_weight])Gradient of model observations at params
score_test
(params_constrained[, …])score test for restrictions or for omitted variables
select_penweight
([criterion, start_params, …])find alpha by minimizing results criterion
select_penweight_kfold
([alphas, …])find alphas by k-fold cross-validation
Properties
Names of endogenous variables.
Names of exogenous variables.