The dataframe passed to `fit` and `predict` will have a column with the specified name to be used as a regressor. When standardize='auto', the regressor will be standardized unless it is binary. The regression coefficient is given a prior with the specified scale parameter. Decreasing the prior scale will add additional regularization. If no prior scale is provided, holidays.prior.scale will be used. Mode can be specified as either 'additive' or 'multiplicative'. If not specified, m$seasonality.mode will be used. 'additive' means the effect of the regressor will be added to the trend, 'multiplicative' means it will multiply the trend.
add_regressor(m, name, prior.scale = NULL, standardize = "auto", mode = NULL)Prophet object.
String name of the regressor
Float scale for the normal prior. If not provided, holidays.prior.scale will be used.
Bool, specify whether this regressor will be standardized prior to fitting. Can be 'auto' (standardize if not binary), True, or False.
Optional, 'additive' or 'multiplicative'. Defaults to m$seasonality.mode.
The prophet model with the regressor added.