statsmodels.discrete.discrete_model.BinaryResults.wald_test¶
method
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BinaryResults.wald_test(r_matrix, cov_p=None, scale=1.0, invcov=None, use_f=None, df_constraints=None)¶ Compute a Wald-test for a joint linear hypothesis.
- Parameters
- r_matrixarray-like, str, or tuple
array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero.
str : The full hypotheses to test can be given as a string. See the examples.
tuple : A tuple of arrays in the form (R, q),
qcan be either a scalar or a length p row vector.
- cov_parray-like, optional
An alternative estimate for the parameter covariance matrix. If None is given, self.normalized_cov_params is used.
- scalefloat, optional
Deprecated since version 0.10.0.
Default is 1.0 for no scaling.
- invcovarray-like, optional
A q x q array to specify an inverse covariance matrix based on a restrictions matrix.
- use_fbool
If True, then the F-distribution is used. If False, then the asymptotic distribution, chisquare is used. If use_f is None, then the F distribution is used if the model specifies that use_t is True. The test statistic is proportionally adjusted for the distribution by the number of constraints in the hypothesis.
- Returns
- resContrastResults instance
The results for the test are attributes of this results instance.
See also
statsmodels.stats.contrast.ContrastResults,f_test,t_test,patsy.DesignInfo.linear_constraintNotes
The matrix r_matrix is assumed to be non-singular. More precisely,
r_matrix (pX pX.T) r_matrix.T
is assumed invertible. Here, pX is the generalized inverse of the design matrix of the model. There can be problems in non-OLS models where the rank of the covariance of the noise is not full.