statsmodels.sandbox.distributions.extras.pdf_mvsk statsmodels.sandbox.distributions.extras.pdf_mvsk(mvsk) [source] Return the Gaussian expanded pdf function given the list of 1st, 2nd moment and skew and Fisher (excess) kurtosis. Parameters: mvsk : list of mu, mc2, skew, kurt distribution is matched to these four moments Returns: pdffunc : function function that evaluates the pdf(x), where x is the non-standardized random variable. Notes Changed so it works only if four arguments are

statsmodels.stats.weightstats.CompareMeans.ttest_ind CompareMeans.ttest_ind(alternative='two-sided', usevar='pooled', value=0) [source] ttest for the null hypothesis of identical means this should also be the same as onewaygls, except for ddof differences Parameters: x1, x2 : array_like, 1-D or 2-D two independent samples, see notes for 2-D case alternative : string The alternative hypothesis, H1, has to be one of the following ?two-sided?: H1: difference in means not equal to value (def

statsmodels.discrete.discrete_model.DiscreteResults class statsmodels.discrete.discrete_model.DiscreteResults(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None) [source] A results class for the discrete dependent variable models. Parameters: model : A DiscreteModel instance params : array-like The parameters of a fitted model. hessian : array-like The hessian of the fitted model. scale : float A scale parameter for the covariance matrix. Returns: *Attributes* : aic : fl

statsmodels.discrete.discrete_model.BinaryModel.cov_params_func_l1 BinaryModel.cov_params_func_l1(likelihood_model, xopt, retvals) Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. Returns a full cov_params matrix, with entries corresponding to zero?d values set to np.nan.

statsmodels.tsa.arima_model.ARIMAResults.conf_int ARIMAResults.conf_int(alpha=0.05, cols=None, method='default') Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The significance level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return method : string Not Implemented Yet Method to estimate the confidence_interval. ?Defaul

statsmodels.tsa.vector_ar.var_model.VAR.from_formula classmethod VAR.from_formula(formula, data, subset=None, *args, **kwargs) Create a Model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.DataFrame args : extra a

statsmodels.duration.hazard_regression.PHRegResults.load classmethod PHRegResults.load(fname) load a pickle, (class method) Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. Returns: unpickled instance :

statsmodels.sandbox.regression.gmm.IVGMM.calc_weightmatrix IVGMM.calc_weightmatrix(moms, weights_method='cov', wargs=(), params=None) calculate omega or the weighting matrix Parameters: moms : array, (nobs, nmoms) moment conditions for all observations evaluated at a parameter value weights_method : string ?cov? If method=?cov? is cov then the matrix is calculated as simple covariance of the moment conditions. see fit method for available aoptions for the weight and covariance matrix wa

statsmodels.genmod.families.links.CDFLink.inverse_deriv CDFLink.inverse_deriv(z) [source] Derivative of the inverse of the CDF transformation link function Parameters: z : array The inverse of the link function at p Returns: The value of the derivative of the inverse of the logit function :

statsmodels.stats.diagnostic.CompareJ.run CompareJ.run(results_x, results_z, attach=True) run J-test for non-nested models Parameters: results_x : Result instance result instance of first model results_z : Result instance result instance of second model attach : bool If true, then the intermediate results are attached to the instance. Returns: tstat : float t statistic for the test that including the fitted values of the first model in the second model has no effect. pvalue : floa