statsmodels.discrete.discrete_model.LogitResults.cov_params LogitResults.cov_params(r_matrix=None, column=None, scale=None, cov_p=None, other=None) Returns the variance/covariance matrix. The variance/covariance matrix can be of a linear contrast of the estimates of params or all params multiplied by scale which will usually be an estimate of sigma^2. Scale is assumed to be a scalar. Parameters: r_matrix : array-like Can be 1d, or 2d. Can be used alone or with other. column : array-like,

statsmodels.discrete.discrete_model.MultinomialResults.bse static MultinomialResults.bse() [source]

statsmodels.discrete.discrete_model.ProbitResults.resid_dev static ProbitResults.resid_dev() Deviance residuals Notes Deviance residuals are defined where and is the total number of observations sharing the covariate pattern . For now is always set to 1.

statsmodels.genmod.generalized_linear_model.GLMResults.summary GLMResults.summary(yname=None, xname=None, title=None, alpha=0.05) [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 intervals Returns: smr

statsmodels.stats.sandwich_covariance.cov_white_simple statsmodels.stats.sandwich_covariance.cov_white_simple(results, use_correction=True) [source] heteroscedasticity robust covariance matrix (White) Parameters: results : result instance result of a regression, uses results.model.exog and results.resid TODO: this should use wexog instead Returns: cov : ndarray, (k_vars, k_vars) heteroscedasticity robust covariance matrix for parameter estimates See also cov_hc1, cov_hc2, cov_hc3 N

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

statsmodels.genmod.generalized_linear_model.GLMResults.predict GLMResults.predict(exog=None, transform=True, *args, **kwargs) Call self.model.predict with self.params as the first argument. Parameters: exog : array-like, optional The values for which you want to predict. transform : bool, optional If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can pass a dat

statsmodels.tsa.vector_ar.var_model.VARProcess.forecast VARProcess.forecast(y, steps) [source] Produce linear minimum MSE forecasts for desired number of steps ahead, using prior values y Parameters: y : ndarray (p x k) steps : int Returns: forecasts : ndarray (steps x neqs) Notes Lutkepohl pp 37-38

statsmodels.sandbox.distributions.extras.SkewNorm2_gen.logpdf SkewNorm2_gen.logpdf(x, *args, **kwds) Log of the probability density function at x of the given RV. This uses a more numerically accurate calculation if available. Parameters: x : array_like quantiles arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional

statsmodels.sandbox.regression.gmm.IVGMM.fitgmm IVGMM.fitgmm(start, weights=None, optim_method='bfgs', optim_args=None) estimate parameters using GMM Parameters: start : array_like starting values for minimization weights : array weighting matrix for moment conditions. If weights is None, then the identity matrix is used Returns: paramest : array estimated parameters Notes todo: add fixed parameter option, not here ??? uses scipy.optimize.fmin