SimpleRegressor

class pyoptex.analysis.estimators.simple_model.SimpleRegressor(factors=(), Y2X=<function identityY2X>, random_effects=(), conditional=False)

A simple linear regressor implementing the RegressionMixin and ConditionalRegressionMixin interfaces.

Permits to fit a simple model provided in Y2X with optionally random effects.

Note

It includes all parameters and attributes from RegressionMixin and ConditionalRegressionMixin

__init__(factors=(), Y2X=<function identityY2X>, random_effects=(), conditional=False)

Simple regression model.

Parameters

factorslist(Factor)

A list of factors to be used during fitting. It contains the categorical encoding, continuous normalization, etc.

Y2Xfunc(Y)

The function to transform a design matrix Y to a model matrix X.

random_effectslist(str)

The names of any random effect columns. Every random effect is interpreted as a string column and encoded using effect encoding.

conditionalbool

Whether to create a conditional model or not.

Methods

SimpleRegressor.fit(X, y)	Fits the data.
SimpleRegressor.formula([labels])	Creates the prediction formula of the fit for the encoded and normalized data.
SimpleRegressor.get_metadata_routing()	Get metadata routing of this object.
SimpleRegressor.get_params([deep])	Get parameters for this estimator.
SimpleRegressor.model_formula(model)	Creates the prediction formula of the fit for the encoded and normalized data.
SimpleRegressor.pred_var(X)	Prediction variances for the new values specified in X.
SimpleRegressor.predict(X)	Predict on new data after fitting.
SimpleRegressor.preprocess_fit(X, y)	Preprocesses before fitting the data.
SimpleRegressor.preprocess_predict(X)	Preprocessing the incoming data before prediction.
SimpleRegressor.score(X, y[, sample_weight])	Return coefficient of determination on test data.
SimpleRegressor.set_params(**params)	Set the parameters of this estimator.
SimpleRegressor.set_score_request(*[, ...])	Configure whether metadata should be requested to be passed to the score method.
SimpleRegressor.summary()	Generates a summary of the fit in case it was stored during training in the fit_ attribute.

Attributes

SimpleRegressor.M_	Alias for information_matrix
SimpleRegressor.Minv_	Alias for inv_information_matrix
SimpleRegressor.V_	Alias for obs_cov
SimpleRegressor.Vinv_	Alias for inv_obs_cov
SimpleRegressor.information_matrix	The information matrix of the fitted data.
SimpleRegressor.inv_information_matrix	The inverse of the information matrix.
SimpleRegressor.inv_obs_cov	The inverse of the observation covariance matrix.
SimpleRegressor.is_fitted	Checks whether the regressor has been fitted.
SimpleRegressor.obs_cov	The observation covariance matrix \(V = var(Y)\).
SimpleRegressor.total_var	The total variance on the normalized y-values.