statsmodels.nonparametric.kde.KDEUnivariate.entropy static KDEUnivariate.entropy() [source] Returns the differential entropy evaluated at the support Notes Will not work if fit has not been called. 1e-12 is added to each probability to ensure that log(0) is not called.

statsmodels.genmod.generalized_linear_model.GLMResults.tvalues static GLMResults.tvalues() Return the t-statistic for a given parameter estimate.

statsmodels.sandbox.regression.try_catdata.cat2dummy statsmodels.sandbox.regression.try_catdata.cat2dummy(y, nonseq=0) [source]

Generalized Estimating Equations Generalized Estimating Equations estimate generalized linear models for panel, cluster or repeated measures data when the observations are possibly correlated withing a cluster but uncorrelated across clusters. It supports estimation of the same one-parameter exponential families as Generalized Linear models (GLM). See Module Reference for commands and arguments. Examples The following illustrates a Poisson regression with exchangeable correlation within cluste

statsmodels.discrete.discrete_model.DiscreteResults.pvalues static DiscreteResults.pvalues()

statsmodels.regression.linear_model.OLSResults.eigenvals static OLSResults.eigenvals() Return eigenvalues sorted in decreasing order.

statsmodels.sandbox.regression.gmm.LinearIVGMM.fititer LinearIVGMM.fititer(start, maxiter=2, start_invweights=None, weights_method='cov', wargs=(), optim_method='bfgs', optim_args=None) iterative estimation with updating of optimal weighting matrix stopping criteria are maxiter or change in parameter estimate less than self.epsilon_iter, with default 1e-6. Parameters: start : array starting value for parameters maxiter : int maximum number of iterations start_weights : array (nmoms, nmo

statsmodels.graphics.gofplots.ProbPlot.theoretical_quantiles static ProbPlot.theoretical_quantiles() [source]

statsmodels.discrete.discrete_model.LogitResults.llr_pvalue static LogitResults.llr_pvalue()

statsmodels.discrete.discrete_model.MultinomialResults.load classmethod MultinomialResults.load(fname) load a pickle, (class method) Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. Returns: unpickled instance :