statsmodels.tsa.arima_model.ARIMAResults.t_test ARIMAResults.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 i

statsmodels.miscmodels.tmodel.TLinearModel.jac TLinearModel.jac(*args, **kwds) jac is deprecated, use score_obs instead! Use score_obs method. jac will be removed in 0.7. Jacobian/Gradient of log-likelihood evaluated at params for each observation.

statsmodels.tsa.vector_ar.var_model.VARResults class statsmodels.tsa.vector_ar.var_model.VARResults(endog, endog_lagged, params, sigma_u, lag_order, model=None, trend='c', names=None, dates=None) [source] Estimate VAR(p) process with fixed number of lags Parameters: endog : array endog_lagged : array params : array sigma_u : array lag_order : int model : VAR model instance trend : str {?nc?, ?c?, ?ct?} names : array-like List of names of the endogenous variables in order of appearance in e

statsmodels.discrete.discrete_model.Probit.fit_regularized Probit.fit_regularized(start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=1, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=0.0001, qc_tol=0.03, **kwargs) Fit the model using a regularized maximum likelihood. The regularization method AND the solver used is determined by the argument method. Parameters: start_params : array-like, optional Initial guess of the solution for t

statsmodels.iolib.summary2.Summary.as_latex Summary.as_latex() [source] Generate LaTeX Summary Table

statsmodels.sandbox.stats.runs.runstest_2samp statsmodels.sandbox.stats.runs.runstest_2samp(x, y=None, groups=None, correction=True) [source] Wald-Wolfowitz runstest for two samples This tests whether two samples come from the same distribution. Parameters: x : array_like data, numeric, contains either one group, if y is also given, or both groups, if additionally a group indicator is provided y : array_like (optional) data, numeric groups : array_like group labels or indicator the dat

statsmodels.stats.weightstats.DescrStatsW class statsmodels.stats.weightstats.DescrStatsW(data, weights=None, ddof=0) [source] descriptive statistics and tests with weights for case weights Assumes that the data is 1d or 2d with (nobs, nvars) observations in rows, variables in columns, and that the same weight applies to each column. If degrees of freedom correction is used, then weights should add up to the number of observations. ttest also assumes that the sum of weights corresponds to th

statsmodels.genmod.cov_struct.GlobalOddsRatio.summary GlobalOddsRatio.summary() [source]