matrices.CommonEffectsMatrix
matrices.CommonEffectsMatrix(terms)Common-effects matrix.
Parameters
Attributes
design_matrix : np.array-
A 2-dimensional numpy array containing the values of the design matrix.
evaluated : bool-
Indicates if the terms have been evaluated at least once. The terms must have been evaluated before calling
self.evaluate_new_data()because we must know the kind of each term to correctly handle the new data passed and the terms here. terms : dict-
A dictionary that holds all the terms passed at instantiation. The keys are given by the term names.
Methods
| Name | Description |
|---|---|
| as_dataframe | Returns self.design_matrix as a pandas.DataFrame. |
| evaluate | Obtain design matrix for common effects. |
| evaluate_new_data | Evaluates common terms with new data and return a new instance of |
as_dataframe
matrices.CommonEffectsMatrix.as_dataframe()Returns self.design_matrix as a pandas.DataFrame.
evaluate
matrices.CommonEffectsMatrix.evaluate(data, env)Obtain design matrix for common effects.
Uses self.terms inside the data mask provided by data and updates self.design_matrix. This method also sets the values of self.data and self.env.
It also populates the dictionary self.slices …
Parameters
evaluate_new_data
matrices.CommonEffectsMatrix.evaluate_new_data(data)Evaluates common terms with new data and return a new instance of CommonEffectsMatrix.
This method is intended to be used to obtain design matrices for new data and obtain out of sample predictions. Stateful transformations are properly handled if present in any of the terms, which means parameters involved in the transformation are not overwritten with the new data.
Parameters
Returns
new_instance : CommonEffectsMatrix-
A new instance of
CommonEffectsMatrixwhose design matrix is obtained with the values in the new data set.