GAMLS#

class sknormod.gam.GAMLS(mu_formula='y ~ 1', sigma_formula='~ 1')#

Generalized Additive Model with Location-Scale (GAMLSS) implemented using the mgcv package in R.

Parameters:

mu_formulastr, default=”y ~ 1”: The formula specifying the model for the mean response. It should be a valid formula according to R’s formula syntax.
sigma_formulastr, default=”~ 1”: The formula specifying the model for the scale (standard deviation) of the response.

Examples

>>> from sknormod.gam import GAMLS
>>> import numpy as np
>>> X = np.arange(100).reshape(100, 1)
>>> y = np.random.normal(0, 1, 100)
>>> gamls = GAMLS(mu_formula="y ~ 1", sigma_formula="~ 1")
>>> gamls.fit(X, y)
GAMLS()

Attributes:

fitted_model_R object: The fitted GAMLSS model obtained from R’s mgcv package.
_logpdffunction: Function for calculating the logarithm of the probability density function (PDF) of the distribution.
_ppffunction: Function for calculating the percent point function (inverse of the cumulative distribution function) of the distribution.
_cdffunction: Function for calculating the cumulative distribution function (CDF) of the distribution.

Methods

`fit`(X, y)	Fit the GAMLSS model.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`predict_distr_p`(X, p)	Predict the percentiles of the distribution.
`predict_distr_params`(X)	Predict distribution parameters for given input samples.
`score`(X, y)	Compute the logarithmic score of the predicted distribution parameters against the true values.
`set_params`(**params)	Set the parameters of this estimator.
`transform_to_p`(X, y)	Transform the target values to percentiles.
`transform_to_z`(X, y)	Transform the target values to z-scores.

fit(X, y)#

Fit the GAMLSS model.

Parameters:

Xarray-like or sparse matrix of shape (n_samples, n_features): The input samples.
yarray-like of shape (n_samples,): The target values.

Returns:

selfobject: Fitted estimator.

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)#

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

predict_distr_p(X, p)#

Predict the percentiles of the distribution.

Parameters:

Xarray-like or sparse matrix of shape (n_samples, n_features): The input samples.
parray-like of shape (n_percentiles,): The percentiles to predict.

Returns:

percentileslist of floats: The predicted percentiles.

predict_distr_params(X)#

Predict distribution parameters for given input samples.

Parameters:

Xarray-like or sparse matrix of shape (n_samples, n_features): The input samples.

Returns:

paramsarray-like of shape (n_samples, 2): Predicted distribution parameters where each row represents [mu, sigma].

score(X, y)#: Compute the logarithmic score of the predicted distribution parameters against the true values.

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

transform_to_p(X, y)#: Transform the target values to percentiles.

transform_to_z(X, y)#: Transform the target values to z-scores.

Examples using `sknormod.gam.GAMLS`#

From LM to GAMLSS

GAMLS#

Examples using sknormod.gam.GAMLS#

Examples using `sknormod.gam.GAMLS`#