statsmodels.sandbox.regression.gmm.LinearIVGMM.fitstart LinearIVGMM.fitstart()

statsmodels.sandbox.regression.gmm.IVRegressionResults.eigenvals static IVRegressionResults.eigenvals() Return eigenvalues sorted in decreasing order.

statsmodels.sandbox.distributions.extras.SkewNorm2_gen.logcdf SkewNorm2_gen.logcdf(x, *args, **kwds) Log of the cumulative distribution function at x of the given RV. 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 scale parameter (default=1) Returns: logcdf : array_like

statsmodels.regression.linear_model.GLS.information GLS.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.stats.weightstats.DescrStatsW.sum_weights static DescrStatsW.sum_weights() [source]

statsmodels.regression.linear_model.OLSResults.uncentered_tss static OLSResults.uncentered_tss()

statsmodels.sandbox.regression.gmm.LinearIVGMM.momcond_mean LinearIVGMM.momcond_mean(params) mean of moment conditions,

statsmodels.regression.linear_model.RegressionResults.pvalues static RegressionResults.pvalues()

statsmodels.sandbox.regression.gmm.IVRegressionResults.uncentered_tss static IVRegressionResults.uncentered_tss()

statsmodels.tools.tools.categorical statsmodels.tools.tools.categorical(data, col=None, dictnames=False, drop=False) [source] Returns a dummy matrix given an array of categorical variables. Parameters: data : array A structured array, recarray, or array. This can be either a 1d vector of the categorical variable or a 2d array with the column specifying the categorical variable specified by the col argument. col : ?string?, int, or None If data is a structured array or a recarray, col can