statsmodels.discrete.discrete_model.MNLogit.loglikeobs MNLogit.loglikeobs(params) [source] Log-likelihood of the multinomial logit model for each observation. Parameters: params : array-like The parameters of the multinomial logit model. Returns: loglike : ndarray (nobs,) The log likelihood for each observation of the model evaluated at params. See Notes Notes for observations where if individual i chose alternative j and 0 if not.

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.varcorrection_unequal statsmodels.sandbox.stats.multicomp.varcorrection_unequal(var_all, nobs_all, df_all) [source] return joint variance from samples with unequal variances and unequal sample sizes 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 : float joint variance. dfjoint

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.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.stats.weightstats.DescrStatsW.get_compare DescrStatsW.get_compare(other, weights=None) [source] return an instance of CompareMeans with self and other Parameters: other : array_like or instance of DescrStatsW If array_like then this creates an instance of DescrStatsW with the given weights. weights : None or array weights are only used if other is not an instance of DescrStatsW Returns: cm : instance of CompareMeans the instance has self attached as d1 and other as d2.

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

statsmodels.sandbox.stats.multicomp.MultiComparison.kruskal MultiComparison.kruskal(pairs=None, multimethod='T') [source] pairwise comparison for kruskal-wallis test This is just a reimplementation of scipy.stats.kruskal and does not yet use a multiple comparison correction.

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