statsmodels.stats.outliers_influence.OLSInfluence.resid_var static OLSInfluence.resid_var() [source] (cached attribute) estimate of variance of the residuals sigma2 = sigma2_OLS * (1 - hii) where hii is the diagonal of the hat matrix

statsmodels.nonparametric.kernel_regression.KernelReg.cv_loo KernelReg.cv_loo(bw, func) [source] The cross-validation function with leave-one-out estimator. Parameters: bw: array_like : Vector of bandwidth values. func: callable function : Returns the estimator of g(x). Can be either _est_loc_constant (local constant) or _est_loc_linear (local_linear). Returns: L: float : The value of the CV function. Notes Calculates the cross-validation least-squares function. This function is mi

statsmodels.sandbox.regression.gmm.IVGMMResults.save IVGMMResults.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 remove_

statsmodels.stats.sandwich_covariance.cov_nw_panel statsmodels.stats.sandwich_covariance.cov_nw_panel(results, nlags, groupidx, weights_func=, use_correction='hac') [source] Panel HAC robust covariance matrix Assumes we have a panel of time series with consecutive, equal spaced time periods. Data is assumed to be in long format with time series of each individual stacked into one array. Panel can be unbalanced. Parameters: results : result instance result of a regression, uses results.mode

statsmodels.sandbox.distributions.transformed.LogTransf_gen.logcdf LogTransf_gen.logcdf(x, *args, **kwds) Log of the cumulative distribution function at x of the given RV. 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 scale parameter (default=1) Returns: logcdf : array

statsmodels.sandbox.distributions.extras.SkewNorm2_gen.std SkewNorm2_gen.std(*args, **kwds) Standard deviation of the distribution. Parameters: 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 scale parameter (default=1) Returns: std : float standard deviation of the distribution

statsmodels.stats.anova.anova_lm statsmodels.stats.anova.anova_lm(*args, **kwargs) [source] ANOVA table for one or more fitted linear models. Parameters: args : fitted linear model results instance One or more fitted linear models scale : float Estimate of variance, If None, will be estimated from the largest model. Default is None. test : str {?F?, ?Chisq?, ?Cp?} or None Test statistics to provide. Default is ?F?. typ : str or int {?I?,?II?,?III?} or {1,2,3} The type of ANOVA test t

statsmodels.stats.proportion.power_ztost_prop statsmodels.stats.proportion.power_ztost_prop(low, upp, nobs, p_alt, alpha=0.05, dist='norm', variance_prop=None, discrete=True, continuity=0, critval_continuity=0) [source] Power of proportions equivalence test based on normal distribution Parameters: low, upp : floats lower and upper limit of equivalence region nobs : int number of observations p_alt : float in (0,1) proportion under the alternative alpha : float in (0,1) significance l

statsmodels.duration.hazard_regression.PHReg.baseline_cumulative_hazard PHReg.baseline_cumulative_hazard(params) [source] Estimate the baseline cumulative hazard and survival functions. Parameters: params : ndarray The model parameters. Returns: A list of triples (time, hazard, survival) containing the time : values and corresponding cumulative hazard and survival : function values for each stratum. : Notes Uses the Nelson-Aalen estimator.

statsmodels.robust.robust_linear_model.RLMResults.normalized_cov_params RLMResults.normalized_cov_params()