statsmodels.discrete.discrete_model.MultinomialModel.predict¶

MultinomialModel.predict(params, exog=None, which='mean', linear=None)[source]¶

Predict response variable of a model given exogenous variables.

Parameters:¶

params : array_like¶

2d array of fitted parameters of the model. Should be in the order returned from the model.

exog : array_like¶

1d or 2d array of exogenous values. If not supplied, the whole exog attribute of the model is used. If a 1d array is given it assumed to be 1 row of exogenous variables. If you only have one regressor and would like to do prediction, you must provide a 2d array with shape[1] == 1.

which : {'mean', 'linear', 'var', 'prob'}, optional¶

Statistic to predict. Default is ‘mean’.

• ’mean’ returns the conditional expectation of endog E(y | x), i.e. exp of linear predictor.

• ’linear’ returns the linear predictor of the mean function.

• ’var’ returns the estimated variance of endog implied by the model.

linear : bool¶

If True, returns the linear predicted values. If False or None, then the statistic specified by which will be returned.

Notes

Column 0 is the base case, the rest conform to the rows of params shifted up one for the base case.