statsmodels.genmod.generalized_estimating_equations.GEEResults.get_distribution

GEEResults.get_distribution(exog=None, exposure=None, offset=None, var_weights=1.0, n_trials=1.0)

Return a instance of the predictive distribution.

Parameters:

• scale (scalar) – The scale parameter.

• exog (array_like) – The predictor variable matrix.

• offset (array_like or None) – Offset variable for predicted mean.

• exposure (array_like or None) – Log(exposure) will be added to the linear prediction.

• var_weights (array_like) – 1d array of variance (analytic) weights. The default is None.

• n_trials (int) – Number of trials for the binomial distribution. The default is 1 which corresponds to a Bernoulli random variable.

Returns:

Instance of a scipy frozen distribution based on estimated parameters. Use the rvs method to generate random values.

Return type:

gen

Notes

Due to the behavior of scipy.stats.distributions objects, the returned random number generator must be called with gen.rvs(n) where n is the number of observations in the data set used to fit the model. If any other value is used for n, misleading results will be produced.