statsmodels.discrete.discrete_model.Logit.hessian Logit.hessian(params) [source] Logit model Hessian matrix of the log-likelihood Parameters: params : array-like The parameters of the model Returns: hess : ndarray, (k_vars, k_vars) The Hessian, second derivative of loglikelihood function, evaluated at params Notes

statsmodels.genmod.generalized_linear_model.GLMResults.remove_data GLMResults.remove_data() [source] remove data arrays, all nobs arrays from result and model This reduces the size of the instance, so it can be pickled with less memory. Currently tested for use with predict from an unpickled results and model instance. Warning Since data and some intermediate results have been removed calculating new statistics that require them will raise exceptions. The exception will occur the first time

Weight Functions Andrew?s Wave Hampel 17A Huber?s t Least Squares Ramsay?s Ea Trimmed Mean Tukey?s Biweight

statsmodels.sandbox.regression.gmm.GMM.momcond_mean GMM.momcond_mean(params) [source] mean of moment conditions,

statsmodels.regression.linear_model.OLS.initialize OLS.initialize()

statsmodels.genmod.families.links.Link class statsmodels.genmod.families.links.Link [source] A generic link function for one-parameter exponential family. Link does nothing, but lays out the methods expected of any subclass. Methods deriv(p) Derivative of the link function g?(p). deriv2(p) Second derivative of the link function g??(p) inverse(z) Inverse of the link function. inverse_deriv(z) Derivative of the inverse link function g^(-1)(z).

statsmodels.stats.gof.chisquare_effectsize statsmodels.stats.gof.chisquare_effectsize(probs0, probs1, correction=None, cohen=True, axis=0) [source] effect size for a chisquare goodness-of-fit test Parameters: probs0 : array_like probabilities or cell frequencies under the Null hypothesis probs1 : array_like probabilities or cell frequencies under the Alternative hypothesis probs0 and probs1 need to have the same length in the axis dimension. and broadcast in the other dimensions Both pro

statsmodels.tsa.vector_ar.var_model.VARResults.mean VARResults.mean() Mean of stable process Lutkepohl eq. 2.1.23

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

statsmodels.tools.tools.unsqueeze statsmodels.tools.tools.unsqueeze(data, axis, oldshape) [source] Unsqueeze a collapsed array >>> from numpy import mean >>> from numpy.random import standard_normal >>> x = standard_normal((3,4,5)) >>> m = mean(x, axis=1) >>> m.shape (3, 5) >>> m = unsqueeze(m, 1, x.shape) >>> m.shape (3, 1, 5) >>>