statsmodels.regression.linear_model.RegressionResults.mse_model static RegressionResults.mse_model() [source]

statsmodels.genmod.families.family.InverseGaussian.predict InverseGaussian.predict(mu) Linear predictors based on given mu values. Parameters: mu : array The mean response variables Returns: lin_pred : array Linear predictors based on the mean response variables. The value of the link function at the given mu.

statsmodels.nonparametric.kernel_regression.KernelCensoredReg.r_squared KernelCensoredReg.r_squared() Returns the R-Squared for the nonparametric regression. Notes For more details see p.45 in [2] The R-Squared is calculated by: where is the mean calculated in fit at the exog points.

statsmodels.sandbox.regression.try_catdata.groupsstats_dummy statsmodels.sandbox.regression.try_catdata.groupsstats_dummy(y, x, nonseq=0) [source]

statsmodels.stats.outliers_influence.variance_inflation_factor statsmodels.stats.outliers_influence.variance_inflation_factor(exog, exog_idx) [source] variance inflation factor, VIF, for one exogenous variable The variance inflation factor is a measure for the increase of the variance of the parameter estimates if an additional variable, given by exog_idx is added to the linear regression. It is a measure for multicollinearity of the design matrix, exog. One recommendation is that if VIF is

statsmodels.tsa.arima_model.ARIMAResults.wald_test ARIMAResults.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 tuple of a

statsmodels.discrete.discrete_model.ProbitResults.resid_response static ProbitResults.resid_response() The response residuals Notes Response residuals are defined to be where .

statsmodels.genmod.generalized_estimating_equations.GEEResults.save GEEResults.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. Not

statsmodels.stats.moment_helpers.se_cov statsmodels.stats.moment_helpers.se_cov(cov) [source] get standard deviation from covariance matrix just a shorthand function np.sqrt(np.diag(cov)) Parameters: cov : array_like, square covariance matrix Returns: std : ndarray standard deviation from diagonal of cov

statsmodels.emplike.descriptive.DescStat statsmodels.emplike.descriptive.DescStat(endog) [source] Returns an instance to conduct inference on descriptive statistics via empirical likelihood. See DescStatUV and DescStatMV for more information. Parameters: endog : ndarray Array of data Returns : DescStat instance If k=1, the function returns a univariate instance, DescStatUV. If k>1, the function returns a multivariate instance, DescStatMV.