# statsmodels.genmod.generalized_linear_model.GLMResults.get_prediction¶

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

compute prediction results

Parameters: 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. kwargs (args,) – Some models can take additional arguments or keywords, see the predict method of the model for the details. prediction_results – 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. generalized_linear_model.PredictionResults