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statsmodels.discrete.discrete_model.Poisson.fit_constrained

Poisson.fit_constrained(constraints, start_params=None, **fit_kwds)[source]

fit the model subject to linear equality constraints

The constraints are of the form R params = q where R is the constraint_matrix and q is the vector of constraint_values.

The estimation creates a new model with transformed design matrix, exog, and converts the results back to the original parameterization.

Parameters:

constraints : formula expression or tuple

If it is a tuple, then the constraint needs to be given by two arrays (constraint_matrix, constraint_value), i.e. (R, q). Otherwise, the constraints can be given as strings or list of strings. see t_test for details

start_params : None or array_like

starting values for the optimization. start_params needs to be given in the original parameter space and are internally transformed.

**fit_kwds : keyword arguments

fit_kwds are used in the optimization of the transformed model.

Returns:

results : Results instance

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