statsmodels.stats.proportion.proportions_chisquare_allpairs statsmodels.stats.proportion.proportions_chisquare_allpairs(count, nobs, multitest_method='hs') [source] chisquare test of proportions for all pairs of k samples Performs a chisquare test for proportions for all pairwise comparisons. The alternative is two-sided Parameters: count : integer or array_like the number of successes in nobs trials. nobs : integer the number of trials or observations. prop : float, optional The proba

statsmodels.regression.linear_model.OLS class statsmodels.regression.linear_model.OLS(endog, exog=None, missing='none', hasconst=None, **kwargs) [source] A simple ordinary least squares model. Parameters: endog : array-like 1-d endogenous response variable. The dependent variable. exog : array-like A nobs x k array where nobs is the number of observations and k is the number of regressors. An intercept is not included by default and should be added by the user. See statsmodels.tools.add_

statsmodels.genmod.families.family.Binomial.loglike Binomial.loglike(endog, mu, scale=1.0) [source] Loglikelihood function for Binomial exponential family distribution. Parameters: endog : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional The default is 1. Returns: llf : float The value of the loglikelihood function evaluated at (endog,mu,scale) as defined below. Notes If endog is binary: llf = scale*sum(endog*log(mu/(1-

statsmodels.tsa.stattools.levinson_durbin statsmodels.tsa.stattools.levinson_durbin(s, nlags=10, isacov=False) [source] Levinson-Durbin recursion for autoregressive processes Parameters: s : array_like If isacov is False, then this is the time series. If iasacov is true then this is interpreted as autocovariance starting with lag 0 nlags : integer largest lag to include in recursion or order of the autoregressive process isacov : boolean flag to indicate whether the first argument, s,

statsmodels.stats.sandwich_covariance.cov_cluster statsmodels.stats.sandwich_covariance.cov_cluster(results, group, use_correction=True) [source] cluster robust covariance matrix Calculates sandwich covariance matrix for a single cluster, i.e. grouped variables. Parameters: results : result instance result of a regression, uses results.model.exog and results.resid TODO: this should use wexog instead use_correction : bool If true (default), then the small sample correction factor is used.

statsmodels.stats.diagnostic.CompareJ class statsmodels.stats.diagnostic.CompareJ J-Test for comparing non-nested models Parameters: results_x : Result instance result instance of first model results_z : Result instance result instance of second model attach : bool From description in Greene, section 8.3.3 : produces correct results for Example 8.3, Greene - not checked yet : #currently an exception, but I don?t have clean reload in python session : check what results should be attached

statsmodels.sandbox.regression.gmm.IVGMMResults class statsmodels.sandbox.regression.gmm.IVGMMResults(*args, **kwds) [source] Methods calc_cov_params(moms, gradmoms[, weights, ...]) calculate covariance of parameter estimates compare_j(other) overidentification test for comparing two nested gmm estimates conf_int([alpha, cols, method]) Returns the confidence interval of the fitted parameters. cov_params(**kwds) f_test(r_matrix[, cov_p, scale, invcov]) Compute the F-test for a joint lin

statsmodels.tsa.ar_model.ARResults.llf static ARResults.llf()

statsmodels.tsa.vector_ar.var_model.VARResults.ma_rep VARResults.ma_rep(maxn=10) Compute MA() coefficient matrices Parameters: maxn : int Number of coefficient matrices to compute Returns: coefs : ndarray (maxn x k x k)

statsmodels.sandbox.stats.multicomp.set_remove_subs statsmodels.sandbox.stats.multicomp.set_remove_subs(ssli) [source] remove sets that are subsets of another set from a list of tuples Parameters: ssli : list of tuples each tuple is considered as a set Returns: part : list of tuples new list with subset tuples removed, it is sorted by set-length of tuples. The list contains original tuples, duplicate elements are not removed. Examples >>> set_remove_subs([(0, 1), (1, 2), (1,