4.7.15.1.1. statsmodels.tools.transform_model.StandardizeTransform

class statsmodels.tools.transform_model.StandardizeTransform(data, ddof=1, const_idx=None, demean=True)

class to reparameterize a model for standardized exog

Parameters:

data : array_like

data that is standardized along axis=0

ddof : None or int

degrees of freedom for calculation of standard deviation. default is 1, in contrast to numpy.std

const_idx : None or int

If None, then the presence of a constant is detected if the standard deviation of a column is equal to zero. A constant column is not transformed. If this is an integer, then the corresponding column will not be transformed.

demean : bool, default is True

If demean is true, then the data will be demeaned, otherwise it will only be rescaled.

Notes

Warning: Not all options are tested and it is written for one use case. API changes are expected.

This can be used to transform only the design matrix, exog, in a model, which is required in some discrete models when the endog cannot be rescaled or demeaned. The transformation is full rank and does not drop the constant.

__init__(data, ddof=1, const_idx=None, demean=True)

4.7.15.1.1.1. Methods

__init__(data[, ddof, const_idx, demean])
transform(data)	standardize the data using the stored transformation
transform_params(params)	Transform parameters of the standardized model to the original model