Sandbox This sandbox contains code that is for various resons not ready to be included in statsmodels proper. It contains modules from the old stats.models code that have not been tested, verified and updated to the new statsmodels structure: cox survival model, mixed effects model with repeated measures, generalized additive model and the formula framework. The sandbox also contains code that is currently being worked on until it fits the pattern of statsmodels or is sufficiently tested. All s

statsmodels.tsa.arima_model.ARIMAResults.pvalues static ARIMAResults.pvalues()

statsmodels.stats.weightstats.DescrStatsW.var static DescrStatsW.var() [source] variance with default degrees of freedom correction

statsmodels.discrete.discrete_model.BinaryResults.wald_test BinaryResults.wald_test(r_matrix, cov_p=None, scale=1.0, invcov=None, use_f=None) Compute a Wald-test for a joint linear hypothesis. Parameters: r_matrix : array-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

statsmodels.tsa.vector_ar.irf.IRAnalysis.fevd_table IRAnalysis.fevd_table() [source]

statsmodels.tsa.vector_ar.irf.IRAnalysis.cov IRAnalysis.cov(orth=False) [source] Compute asymptotic standard errors for impulse response coefficients Notes Lutkepohl eq 3.7.5

statsmodels.discrete.discrete_model.DiscreteResults.get_margeff DiscreteResults.get_margeff(at='overall', method='dydx', atexog=None, dummy=False, count=False) [source] Get marginal effects of the fitted model. Parameters: at : str, optional Options are: ?overall?, The average of the marginal effects at each observation. ?mean?, The marginal effects at the mean of each regressor. ?median?, The marginal effects at the median of each regressor. ?zero?, The marginal effects at zero for each r

statsmodels.duration.hazard_regression.PHReg.loglike PHReg.loglike(params) [source] Returns the log partial likelihood function evaluated at params.

statsmodels.regression.linear_model.OLSResults.ssr static OLSResults.ssr()

statsmodels.stats.power.NormalIndPower.solve_power NormalIndPower.solve_power(effect_size=None, nobs1=None, alpha=None, power=None, ratio=1.0, alternative='two-sided') [source] solve for any one parameter of the power of a two sample z-test for z-test the keywords are: effect_size, nobs1, alpha, power, ratio exactly one needs to be None, all others need numeric values Parameters: effect_size : float standardized effect size, difference between the two means divided by the standard deviatio