statsmodels.discrete.discrete_model.MultinomialResults.get_margeff MultinomialResults.get_margeff(at='overall', method='dydx', atexog=None, dummy=False, count=False) Get marginal effects of the fitted model. Parameters: at : str, optional Options are: ?overall?, The average of the marginal effects at each observation. ?mean?, The marginal effects at the mean of each regressor. ?median?, The marginal effects at the median of each regressor. ?zero?, The marginal effects at zero for each regr

statsmodels.sandbox.distributions.extras.SkewNorm2_gen.mean SkewNorm2_gen.mean(*args, **kwds) Mean 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=0) scale : array_like, optional scale parameter (default=1) Returns: mean : float the mean of the distribution

statsmodels.discrete.discrete_model.MNLogit.initialize MNLogit.initialize() Preprocesses the data for MNLogit.

statsmodels.iolib.table.SimpleTable.extend SimpleTable.extend() L.extend(iterable) ? extend list by appending elements from the iterable

statsmodels.sandbox.tsa.fftarma.ArmaFft.impulse_response ArmaFft.impulse_response(nobs=None) get the impulse response function (MA representation) for ARMA process Parameters: ma : array_like, 1d moving average lag polynomial ar : array_like, 1d auto regressive lag polynomial nobs : int number of observations to calculate Returns: ir : array, 1d impulse response function with nobs elements Notes This is the same as finding the MA representation of an ARMA(p,q). By reversing the r

statsmodels.regression.quantile_regression.QuantRegResults.summary QuantRegResults.summary(yname=None, xname=None, title=None, alpha=0.05) [source] Summarize the Regression Results Parameters: yname : string, optional Default is y xname : list of strings, optional Default is var_## for ## in p the number of regressors title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Retu

statsmodels.sandbox.stats.multicomp.set_partition statsmodels.sandbox.stats.multicomp.set_partition(ssli) [source] extract a partition from a list of tuples this should be correctly called select largest disjoint sets. Begun and Gabriel 1981 don?t seem to be bothered by sets of accepted hypothesis with joint elements, e.g. maximal_accepted_sets = { {1,2,3}, {2,3,4} } This creates a set partition from a list of sets given as tuples. It tries to find the partition with the largest sets. That i

statsmodels.sandbox.regression.gmm.IVRegressionResults.normalized_cov_params IVRegressionResults.normalized_cov_params()

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

statsmodels.sandbox.distributions.extras.SkewNorm_gen.logsf SkewNorm_gen.logsf(x, *args, **kwds) Log of the survival function of the given RV. Returns the log of the ?survival function,? defined as (1 - cdf), evaluated at x. Parameters: x : array_like quantiles 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 s