statsmodels.discrete.discrete_model.CountResults.wald_test¶
- CountResults.wald_test(r_matrix, cov_p=None, invcov=None, use_f=None, df_constraints=None, scalar=None)¶
Compute a Wald-test for a joint linear hypothesis.
- Parameters:
- r_matrix{array_like,
str
,tuple
} One of:
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),
q
can 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.
- 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.
- df_constraints
int
,optional
The number of constraints. If not provided the number of constraints is determined from r_matrix.
- scalarbool,
optional
Flag indicating whether the Wald test statistic should be returned as a sclar float. The current behavior is to return an array. This will switch to a scalar float after 0.14 is released. To get the future behavior now, set scalar to True. To silence the warning and retain the legacy behavior, set scalar to False.
- r_matrix{array_like,
- Returns:
ContrastResults
The results for the test are attributes of this results instance.
See also
f_test
Perform an F tests on model parameters.
t_test
Perform a single hypothesis test.
statsmodels.stats.contrast.ContrastResults
Test results.
patsy.DesignInfo.linear_constraint
Specify a linear constraint.
Notes
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.