statsmodels.sandbox.stats.multicomp.varcorrection_pairs_unequal statsmodels.sandbox.stats.multicomp.varcorrection_pairs_unequal(var_all, nobs_all, df_all) [source] return joint variance from samples with unequal variances and unequal sample sizes for all pairs something is wrong Parameters: var_all : array_like The variance for each sample nobs_all : array_like The number of observations for each sample df_all : array_like degrees of freedom for each sample Returns: varjoint : array

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.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.sandbox.distributions.transformed.TransfTwo_gen.pdf TransfTwo_gen.pdf(x, *args, **kwds) Probability density 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: pdf : ndarray Probability density

statsmodels.genmod.generalized_estimating_equations.GEEMargins.summary GEEMargins.summary(alpha=0.05) [source] Returns a summary table for marginal effects Parameters: alpha : float Number between 0 and 1. The confidence intervals have the probability 1-alpha. Returns: Summary : SummaryTable A SummaryTable instance

statsmodels.sandbox.stats.multicomp.GroupsStats.groupdemean GroupsStats.groupdemean() [source]

statsmodels.genmod.cov_struct.Autoregressive class statsmodels.genmod.cov_struct.Autoregressive(dist_func=None) [source] An autoregressive working dependence structure. The dependence is defined in terms of the time component of the parent GEE class. Time represents a potentially multidimensional index from which distances between pairs of observations can be determined. The correlation between two observations in the same cluster is dep_params^distance, where dep_params is the autocorrelati

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

statsmodels.emplike.descriptive.DescStatUV.ci_var DescStatUV.ci_var(lower_bound=None, upper_bound=None, sig=0.05) [source] Returns the confidence interval for the variance. Parameters: lower_bound : float The minimum value the lower confidence interval can take. The p-value from test_var(lower_bound) must be lower than 1 - significance level. Default is .99 confidence limit assuming normality upper_bound : float The maximum value the upper confidence interval can take. The p-value from t

statsmodels.emplike.descriptive.DescStatUV.ci_skew DescStatUV.ci_skew(sig=0.05, upper_bound=None, lower_bound=None) [source] Returns the confidence interval for skewness. Parameters: sig : float The significance level. Default is .05 upper_bound : float Maximum value of skewness the upper limit can be. Default is .99 confidence limit assuming normality. lower_bound : float Minimum value of skewness the lower limit can be. Default is .99 confidence level assuming normality. Returns: