statsmodels.duration.hazard_regression.PHReg class statsmodels.duration.hazard_regression.PHReg(endog, exog, status=None, entry=None, strata=None, offset=None, ties='breslow', missing='drop', **kwargs) [source] Fit the Cox proportional hazards regression model for right censored data. Parameters: endog : array-like The observed times (event or censoring) exog : 2D array-like The covariates or exogeneous variables status : array-like The censoring status values; status=1 indicates that

statsmodels.distributions.empirical_distribution.StepFunction class statsmodels.distributions.empirical_distribution.StepFunction(x, y, ival=0.0, sorted=False, side='left') [source] A basic step function. Values at the ends are handled in the simplest way possible: everything to the left of x[0] is set to ival; everything to the right of x[-1] is set to y[-1]. Parameters: x : array-like y : array-like ival : float ival is the value given to the values to the left of x[0]. Default is 0. so

statsmodels.distributions.empirical_distribution.ECDF class statsmodels.distributions.empirical_distribution.ECDF(x, side='right') [source] Return the Empirical CDF of an array as a step function. Parameters: x : array-like Observations side : {?left?, ?right?}, optional Default is ?right?. Defines the shape of the intervals constituting the steps. ?right? correspond to [a, b) intervals and ?left? to (a, b]. Returns: Empirical CDF as a step function. : Examples >>> import nu

Distributions This section collects various additional functions and methods for statistical distributions. Empirical Distributions ECDF(x[, side]) Return the Empirical CDF of an array as a step function. StepFunction(x, y[, ival, sorted, side]) A basic step function. Distribution Extras Skew Distributions SkewNorm_gen() univariate Skew-Normal distribution of Azzalini SkewNorm2_gen([momtype, a, b, xtol, ...]) univariate Skew-Normal distribution of Azzalini ACSkewT_gen() univariate Skew-

statsmodels.discrete.discrete_model.DiscreteResults.wald_test DiscreteResults.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

statsmodels.discrete.discrete_model.DiscreteResults.t_test DiscreteResults.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 tup

statsmodels.discrete.discrete_model.DiscreteResults.summary2 DiscreteResults.summary2(yname=None, xname=None, title=None, alpha=0.05, float_format='%.4f') [source] Experimental function to summarize regression results Parameters: xname : List of strings of length equal to the number of parameters Names of the independent variables (optional) yname : string Name of the dependent variable (optional) title : string, optional Title for the top table. If not None, then this replaces the def

statsmodels.discrete.discrete_model.DiscreteResults.summary DiscreteResults.summary(yname=None, xname=None, title=None, alpha=0.05, yname_list=None) [source] Summarize the Regression Results Parameters: yname : string, optional Default is y xname : list of strings, optional Default is var_## for ## in p the number of regressors title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence interv

statsmodels.discrete.discrete_model.DiscreteResults.save DiscreteResults.save(fname, remove_data=False) save a pickle of this instance Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. remove_data : bool If False (default), then the instance is pickled without changes. If True, then all arrays with length nobs are set to None before pickling. See the remove_data method. In some cases not all arrays will be set to None. Notes If

statsmodels.discrete.discrete_model.DiscreteResults.remove_data DiscreteResults.remove_data() remove data arrays, all nobs arrays from result and model This reduces the size of the instance, so it can be pickled with less memory. Currently tested for use with predict from an unpickled results and model instance. Warning Since data and some intermediate results have been removed calculating new statistics that require them will raise exceptions. The exception will occur the first time an att