GLMResults.get_prediction(exog=None, exposure=None, offset=None, transform=True, linear=False, row_labels=None)[source]

compute prediction results


exog : array-like, optional

The values for which you want to predict.

transform : bool, optional

If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can pass a data structure that contains x1 and x2 in their original form. Otherwise, you’d need to log the data first.

weights : array_like, optional

Weights interpreted as in WLS, used for the variance of the predicted residual.

args, kwargs :

Some models can take additional arguments or keywords, see the predict method of the model for the details.


prediction_results : instance

The prediction results instance contains prediction and prediction variance and can on demand calculate confidence intervals and summary tables for the prediction of the mean and of new observations.