statsmodels.tsa.arima_model.ARMA.loglike_kalman ARMA.loglike_kalman(params, set_sigma2=True) [source] Compute exact loglikelihood for ARMA(p,q) model by the Kalman Filter.

statsmodels.sandbox.stats.multicomp.GroupsStats.runbasic_old GroupsStats.runbasic_old(useranks=False) [source]

statsmodels.graphics.gofplots.qqplot statsmodels.graphics.gofplots.qqplot(data, dist=, distargs=(), a=0, loc=0, scale=1, fit=False, line=None, ax=None) [source] Q-Q plot of the quantiles of x versus the quantiles/ppf of a distribution. Can take arguments specifying the parameters for dist or fit them automatically. (See fit under Parameters.) Parameters: data : array-like 1d data array dist : A scipy.stats or statsmodels distribution Compare x against dist. The default is scipy.stats.dis

statsmodels.discrete.discrete_model.BinaryResults.bse static BinaryResults.bse()

statsmodels.genmod.generalized_linear_model.GLMResults.llf static GLMResults.llf() [source]

statsmodels.discrete.discrete_model.BinaryResults.f_test BinaryResults.f_test(r_matrix, cov_p=None, scale=1.0, invcov=None) Compute the F-test for a joint linear hypothesis. This is a special case of wald_test that always uses the F distribution. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.median TransfTwo_gen.median(*args, **kwds) Median 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 is 0. scale : array_like, optional Scale parameter, Default is 1. Returns: median : float The median of the distribution. See also stats.distribut

statsmodels.sandbox.distributions.extras.ACSkewT_gen class statsmodels.sandbox.distributions.extras.ACSkewT_gen [source] univariate Skew-T distribution of Azzalini class follows scipy.stats.distributions pattern but with __init__ Methods cdf(x, *args, **kwds) Cumulative distribution function of the given RV. entropy(*args, **kwds) Differential entropy of the RV. est_loc_scale(*args, **kwds) est_loc_scale is deprecated! expect([func, args, loc, scale, lb, ub, ...]) Calculate expected val

statsmodels.genmod.cov_struct.Nested class statsmodels.genmod.cov_struct.Nested(cov_nearest_method='clipped') [source] A nested working dependence structure. A working dependence structure that captures a nested hierarchy of groups, each level of which contributes to the random error term of the model. When using this working covariance structure, dep_data of the GEE instance should contain a n_obs x k matrix of 0/1 indicators, corresponding to the k subgroups nested under the top-level grou

statsmodels.discrete.discrete_model.NegativeBinomial.initialize NegativeBinomial.initialize() Initialize is called by statsmodels.model.LikelihoodModel.__init__ and should contain any preprocessing that needs to be done for a model.