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.iolib.foreign.StataReader.file_label StataReader.file_label() [source] Returns the dataset?s label. Returns: out: string :

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

statsmodels.regression.quantile_regression.QuantReg class statsmodels.regression.quantile_regression.QuantReg(endog, exog, **kwargs) [source] Quantile Regression Estimate a quantile regression model using iterative reweighted least squares. Parameters: endog : array or dataframe endogenous/response variable exog : array or dataframe exogenous/explanatory variable(s) Notes The Least Absolute Deviation (LAD) estimator is a special case where quantile is set to 0.5 (q argument of the fit

statsmodels.tsa.vector_ar.var_model.VARResults.reorder VARResults.reorder(order) [source] Reorder variables for structural specification

statsmodels.sandbox.regression.gmm.IVGMMResults.tvalues static IVGMMResults.tvalues() Return the t-statistic for a given parameter estimate.

statsmodels.regression.quantile_regression.QuantRegResults.cov_HC1 static QuantRegResults.cov_HC1() See statsmodels.RegressionResults