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

statsmodels.genmod.families.links.Logit.deriv2 Logit.deriv2(p) Second derivative of the link function g??(p) implemented through numerical differentiation

statsmodels.stats.outliers_influence.OLSInfluence.influence static OLSInfluence.influence() [source] (cached attribute) influence measure matches the influence measure that gretl reports u * h / (1 - h) where u are the residuals and h is the diagonal of the hat_matrix

statsmodels.iolib.foreign.StataReader.dataset StataReader.dataset(as_dict=False) [source] Returns a Python generator object for iterating over the dataset. Parameters: as_dict : bool, optional If as_dict is True, yield each row of observations as a dict. If False, yields each row of observations as a list. Returns: Generator object for iterating over the dataset. Yields each row of : observations as a list by default. : Notes If missing_values is True during instantiation of StataReade

statsmodels.nonparametric.kernel_regression.KernelCensoredReg.fit KernelCensoredReg.fit(data_predict=None) [source] Returns the marginal effects at the data_predict points.

statsmodels.tools.eval_measures.hqic statsmodels.tools.eval_measures.hqic(llf, nobs, df_modelwc) [source] Hannan-Quinn information criterion (HQC) Parameters: llf : float value of the loglikelihood nobs : int number of observations df_modelwc : int number of parameters including constant Returns: hqic : float information criterion References Wikipedia doesn?t say much

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

statsmodels.sandbox.distributions.extras.ACSkewT_gen.std ACSkewT_gen.std(*args, **kwds) Standard deviation 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: std : float standard deviation of the distribution

statsmodels.tsa.arima_model.ARMAResults.arparams static ARMAResults.arparams() [source]

statsmodels.nonparametric.kde.KDEUnivariate.fit KDEUnivariate.fit(kernel='gau', bw='normal_reference', fft=True, weights=None, gridsize=None, adjust=1, cut=3, clip=(-inf, inf)) [source] Attach the density estimate to the KDEUnivariate class. Parameters: kernel : str The Kernel to be used. Choices are: ?biw? for biweight ?cos? for cosine ?epa? for Epanechnikov ?gau? for Gaussian. ?tri? for triangular ?triw? for triweight ?uni? for uniform bw : str, float The bandwidth to use. Choices are: