statsmodels.stats.outliers_influence.variance_inflation_factor#
- statsmodels.stats.outliers_influence.variance_inflation_factor(exog, exog_idx, *, standardize=True)[source]#
Variance inflation factor, VIF, for one exogenous variable
The variance inflation factor is a measure for the increase of the variance of the parameter estimates if an additional variable, given by exog_idx is added to the linear regression. It is a measure for multicollinearity of the design matrix, exog.
One recommendation is that if VIF is greater than 5, then the explanatory variable given by exog_idx is highly collinear with the other explanatory variables, and the parameter estimates will have large standard errors because of this.
- Parameters:
- exogarray_like
design matrix with all explanatory variables, as for example used in regression
- exog_idx
int index of the exogenous variable in the columns of exog
- standardizebool,
optional If True, standardizes the design matrix columns to mean 0 and standard deviation 1 before computing VIF. This ensures numerical stability for non-linear transformations or micro-scale data. Default is True.
- Returns:
floatvariance inflation factor
See also
statsmodels.stats.multicollinearity.vifVariance inflation factors of all columns, which is much faster than calling this function for each column.
Notes
This function does not save the auxiliary regression.
References