MultinomialResults.t_test()

statsmodels.discrete.discrete_model.MultinomialResults.t_test

MultinomialResults.t_test(r_matrix, cov_p=None, scale=None, use_t=None)

Compute a t-test for a each linear hypothesis of the form Rb = q

Parameters:

r_matrix : array-like, str, tuple array : If an array is given, a p x k 2d array or length k 1d array specifying the linear restrictions. 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). If q is given, can be either a scalar or a length p row vector. cov_p : array-like, optional An alternative estimate for the parameter covariance matrix. If None is given, self.normalized_cov_params is used. scale : float, optional An optional scale to use. Default is the scale specified by the model fit. use_t : bool, optional If use_t is None, then the default of the model is used. If use_t is True, then the p-values are based on the t distribution. If use_t is False, then the p-values are based on the normal distribution.

Returns:

res : ContrastResults instance The results for the test are attributes of this results instance. The available results have the same elements as the parameter table in summary().

See also

tvalues

individual t statistics

f_test

for F tests

patsy.DesignInfo.linear_constraint

Examples

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>>> import numpy as np >>> import statsmodels.api as sm >>> data = sm.datasets.longley.load() >>> data.exog = sm.add_constant(data.exog) >>> results = sm.OLS(data.endog, data.exog).fit() >>> r = np.zeros_like(results.params) >>> r[5:] = [1,-1] >>> print(r) [ 0.  0.  0.  0.  0.  1. -1.]

r tests that the coefficients on the 5th and 6th independent variable are the same.

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>>> T_test = results.t_test(r) >>> print(T_test) <T contrast: effect=-1829.2025687192481, sd=455.39079425193762, t=-4.0167754636411717, p=0.0015163772380899498, df_denom=9> >>> T_test.effect -1829.2025687192481 >>> T_test.sd 455.39079425193762 >>> T_test.tvalue -4.0167754636411717 >>> T_test.pvalue 0.0015163772380899498

Alternatively, you can specify the hypothesis tests using a string

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>>> from statsmodels.formula.api import ols >>> dta = sm.datasets.longley.load_pandas().data >>> formula = 'TOTEMP ~ GNPDEFL + GNP + UNEMP + ARMED + POP + YEAR' >>> results = ols(formula, dta).fit() >>> hypotheses = 'GNPDEFL = GNP, UNEMP = 2, YEAR/1829 = 1' >>> t_test = results.t_test(hypotheses) >>> print(t_test)