terms.Variable
terms.Variable(name, level=None, is_response=False)Variable in a model term.
This class and Call are the atomic components of a model term.
Parameters
name : str-
The identifier of the variable.
level : str = None-
The level to use as reference. Allows to use the notation
variable["level"]to indicate which event should be model as success in binary response models. Can only be used with response terms. Defaults toNone. is_response : bool = False-
Indicates whether this variable represents a response. Defaults to
False.
Attributes
| Name | Description |
|---|---|
| labels | Obtain labels of the columns in the design matrix associated with this Variable |
| var_names | Returns the name of the variable as a set. |
Methods
| Name | Description |
|---|---|
| eval_categoric | Finishes evaluation of a categoric variable. |
| eval_new_data | Evaluates the variable with new data. |
| eval_new_data_categoric | Evaluates the variable with new data when variable is categoric. |
| eval_numeric | Finishes evaluation of a numeric variable. |
| set_data | Obtains and stores the final data object related to this variable. |
| set_type | Determines the type of the variable. |
eval_categoric
terms.Variable.eval_categoric(x, spans_intercept)Finishes evaluation of a categoric variable.
Converts the intermediate values in x into a numpy array of shape (n, p), where n is the number of observations and p the number of dummy variables used in the numeric representation of the categorical variable.
Parameters
eval_new_data
terms.Variable.eval_new_data(data_mask)Evaluates the variable with new data.
This method evaluates the variable within a new data mask. If this object is categorical, original encoding is remembered (and checked) when carrying out the new evaluation.
Parameters
Returns
eval_new_data_categoric
terms.Variable.eval_new_data_categoric(x)Evaluates the variable with new data when variable is categoric.
This method also checks the levels observed in the new data frame are included within the set of the levels of the original data set. If not, an error is raised.
x : np.ndarray or pd.Series The intermediate values of the variable.
Returns
eval_numeric
terms.Variable.eval_numeric(x)Finishes evaluation of a numeric variable.
Converts the intermediate values in x into a 1d numpy array.
Parameters
set_data
terms.Variable.set_data(spans_intercept=None)Obtains and stores the final data object related to this variable.
Parameters
spans_intercept : bool = None-
Indicates if the encoding of categorical variables spans the intercept or not. Omitted when the variable is numeric.
set_type
terms.Variable.set_type(data_mask)Determines the type of the variable.
Looks for the name of the variable in data_mask and sets the .kind property to "numeric" or "categoric" depending on the type of the variable. It also stores the result of the intermediate evaluation in self._intermediate_data.