statsmodels.miscmodels.tmodel.TLinearModel.information TLinearModel.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.tsa.vector_ar.var_model.VARResults.resid_corr static VARResults.resid_corr() [source] Centered residual correlation matrix

statsmodels.discrete.discrete_model.NegativeBinomialResults.get_margeff NegativeBinomialResults.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

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

statsmodels.tsa.vector_ar.irf.IRAnalysis.cum_effect_stderr IRAnalysis.cum_effect_stderr(orth=False) [source]

statsmodels.sandbox.regression.gmm.LinearIVGMM.gradient_momcond LinearIVGMM.gradient_momcond(params, **kwds) [source]

statsmodels.discrete.discrete_model.ProbitResults.pred_table ProbitResults.pred_table(threshold=0.5) Prediction table Parameters: threshold : scalar Number between 0 and 1. Threshold above which a prediction is considered 1 and below which a prediction is considered 0. Notes pred_table[i,j] refers to the number of times ?i? was observed and the model predicted ?j?. Correct predictions are along the diagonal.

statsmodels.sandbox.distributions.extras.SkewNorm_gen.expect SkewNorm_gen.expect(func=None, args=(), loc=0, scale=1, lb=None, ub=None, conditional=False, **kwds) Calculate expected value of a function with respect to the distribution. The expected value of a function f(x) with respect to a distribution dist is defined as: ubound E[x] = Integral(f(x) * dist.pdf(x)) lbound Parameters: func : callable, optional Function for which integral is calculated. Takes only one argumen

statsmodels.discrete.discrete_model.CountResults.llr static CountResults.llr()

statsmodels.stats.weightstats._zstat_generic2 statsmodels.stats.weightstats._zstat_generic2(value, std_diff, alternative) [source] generic (normal) z-test to save typing can be used as ztest based on summary statistics