statsmodels.sandbox.stats.multicomp.GroupsStats class statsmodels.sandbox.stats.multicomp.GroupsStats(x, useranks=False, uni=None, intlab=None) [source] statistics by groups (another version) groupstats as a class with lazy evaluation (not yet - decorators are still missing) written this time as equivalent of scipy.stats.rankdata gs = GroupsStats(X, useranks=True) assert_almost_equal(gs.groupmeanfilter, stats.rankdata(X[:,0]), 15) TODO: incomplete doc strings Methods groupdemean() groupss

statsmodels.regression.quantile_regression.QuantRegResults.rsquared static QuantRegResults.rsquared() [source]

statsmodels.miscmodels.tmodel.TLinearModel.initialize TLinearModel.initialize() [source]

statsmodels.regression.linear_model.RegressionResults.get_robustcov_results RegressionResults.get_robustcov_results(cov_type='HC1', use_t=None, **kwds) [source] create new results instance with robust covariance as default Parameters: cov_type : string the type of robust sandwich estimator to use. see Notes below use_t : bool If true, then the t distribution is used for inference. If false, then the normal distribution is used. kwds : depends on cov_type Required or optional arguments

Distributions This section collects various additional functions and methods for statistical distributions. Empirical Distributions ECDF(x[, side]) Return the Empirical CDF of an array as a step function. StepFunction(x, y[, ival, sorted, side]) A basic step function. Distribution Extras Skew Distributions SkewNorm_gen() univariate Skew-Normal distribution of Azzalini SkewNorm2_gen([momtype, a, b, xtol, ...]) univariate Skew-Normal distribution of Azzalini ACSkewT_gen() univariate Skew-

statsmodels.sandbox.regression.try_catdata.labelmeanfilter statsmodels.sandbox.regression.try_catdata.labelmeanfilter(y, x) [source]

statsmodels.discrete.discrete_model.MultinomialModel.pdf MultinomialModel.pdf(X) The probability density (mass) function of the model.

statsmodels.sandbox.distributions.extras.NormExpan_gen.entropy NormExpan_gen.entropy(*args, **kwds) Differential entropy of the RV. 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=0). scale : array_like, optional Scale parameter (default=1).

statsmodels.tsa.ar_model.ARResults class statsmodels.tsa.ar_model.ARResults(model, params, normalized_cov_params=None, scale=1.0) [source] Class to hold results from fitting an AR model. Parameters: model : AR Model instance Reference to the model that is fit. params : array The fitted parameters from the AR Model. normalized_cov_params : array inv(dot(X.T,X)) where X is the lagged values. scale : float, optional An estimate of the scale of the model. Returns: **Attributes** : aic

statsmodels.genmod.families.links.nbinom.inverse_deriv nbinom.inverse_deriv(z) Derivative of the inverse of the negative binomial transform Parameters: z : array-like Usually the linear predictor for a GLM or GEE model Returns: The value of the inverse of the derivative of the negative binomial : link :