statsmodels.discrete.discrete_model.BinaryResults.cov_params BinaryResults.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.sandbox.stats.multicomp.fdrcorrection0 statsmodels.sandbox.stats.multicomp.fdrcorrection0(pvals, alpha=0.05, method='indep', is_sorted=False) pvalue correction for false discovery rate This covers Benjamini/Hochberg for independent or positively correlated and Benjamini/Yekutieli for general or negatively correlated tests. Both are available in the function multipletests, as method=`fdr_bh`, resp. fdr_by. Parameters: pvals : array_like set of p-values of the individual tests.

statsmodels.tsa.vector_ar.var_model.VARResults.forecast_cov VARResults.forecast_cov(steps=1) [source] Compute forecast covariance matrices for desired number of steps Parameters: steps : int Returns: covs : ndarray (steps x k x k) Notes Ref: Lutkepohl pp. 96-97

statsmodels.duration.hazard_regression.PHReg.baseline_cumulative_hazard_function PHReg.baseline_cumulative_hazard_function(params) [source] Returns a function that calculates the baseline cumulative hazard function for each stratum. Parameters: params : ndarray The model parameters. Returns: A dict mapping stratum names to the estimated baseline : cumulative hazard function. :

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

statsmodels.sandbox.distributions.transformed.LogTransf_gen.moment LogTransf_gen.moment(n, *args, **kwds) n?th order non-central moment of distribution. Parameters: n : int, n>=1 Order of moment. arg1, arg2, arg3,... : float The shape parameter(s) for the distribution (see docstring of the instance object for more information). kwds : keyword arguments, optional These can include ?loc? and ?scale?, as well as other keyword arguments relevant for a given distribution.

statsmodels.stats.weightstats.DescrStatsW.std_ddof DescrStatsW.std_ddof(ddof=0) [source] standard deviation of data with given ddof Parameters: ddof : int, float degrees of freedom correction, independent of attribute ddof Returns: std : float, ndarray standard deviation with denominator sum_weights - ddof

statsmodels.genmod.families.links.CLogLog class statsmodels.genmod.families.links.CLogLog [source] The complementary log-log transform CLogLog inherits from Logit in order to have access to its _clean method for the link and its derivative. Notes CLogLog is untested. Methods deriv(p) Derivative of C-Log-Log transform link function deriv2(p) Second derivative of the link function g??(p) inverse(z) Inverse of C-Log-Log transform link function inverse_deriv(z) Derivative of the inverse of t

statsmodels.sandbox.stats.multicomp.MultiComparison.tukeyhsd MultiComparison.tukeyhsd(alpha=0.05) [source] Tukey?s range test to compare means of all pairs of groups Parameters: alpha : float, optional Value of FWER at which to calculate HSD. Returns: results : TukeyHSDResults instance A results class containing relevant data and some post-hoc calculations

statsmodels.sandbox.stats.multicomp.MultiComparison.getranks MultiComparison.getranks() [source] convert data to rankdata and attach This creates rankdata as it is used for non-parametric tests, where in the case of ties the average rank is assigned.