statsmodels.discrete.discrete_model.Probit.predict Probit.predict(params, exog=None, linear=False) Predict response variable of a model given exogenous variables. Parameters: params : array-like Fitted parameters of the model. exog : array-like 1d or 2d array of exogenous values. If not supplied, the whole exog attribute of the model is used. linear : bool, optional If True, returns the linear predictor dot(exog,params). Else, returns the value of the cdf at the linear predictor. Ret

statsmodels.duration.hazard_regression.PHReg.initialize PHReg.initialize() Initialize (possibly re-initialize) a Model instance. For instance, the design matrix of a linear model may change and some things must be recomputed.

statsmodels.miscmodels.tmodel.TLinearModel.reduceparams TLinearModel.reduceparams(params)

statsmodels.duration.hazard_regression.PHRegResults.baseline_cumulative_hazard_function static PHRegResults.baseline_cumulative_hazard_function() [source] A list (corresponding to the strata) containing function objects that calculate the cumulative hazard function.

statsmodels.regression.mixed_linear_model.MixedLMResults.bse_fe static MixedLMResults.bse_fe() [source] Returns the standard errors of the fixed effect regression coefficients.

statsmodels.iolib.smpickle.save_pickle statsmodels.iolib.smpickle.save_pickle(obj, fname) [source] Save the object to file via pickling. Parameters: fname : str Filename to pickle to

statsmodels.sandbox.stats.multicomp.GroupsStats.groupvarwithin GroupsStats.groupvarwithin() [source]

statsmodels.tsa.arima_model.ARMAResults.t_test ARMAResults.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

statsmodels.discrete.discrete_model.DiscreteResults.tvalues static DiscreteResults.tvalues() Return the t-statistic for a given parameter estimate.

statsmodels.stats.outliers_influence.OLSInfluence.cov_ratio static OLSInfluence.cov_ratio() [source] (cached attribute) covariance ratio between LOOO and original This uses determinant of the estimate of the parameter covariance from leave-one-out estimates. requires leave one out loop for observations