statsmodels.discrete.discrete_model.NegativeBinomialResults.llnull static NegativeBinomialResults.llnull()

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

statsmodels.stats.proportion.binom_tost statsmodels.stats.proportion.binom_tost(count, nobs, low, upp) [source] exact TOST test for one proportion using binomial distribution Parameters: count : integer or array_like the number of successes in nobs trials. nobs : integer the number of trials or observations. low, upp : floats lower and upper limit of equivalence region Returns: pvalue : float p-value of equivalence test pval_low, pval_upp : floats p-values of lower and upper one-

statsmodels.sandbox.regression.gmm.GMM.score GMM.score(params, weights, epsilon=None, centered=True) [source]

statsmodels.tsa.arima_model.ARIMAResults.mafreq static ARIMAResults.mafreq() Returns the frequency of the MA roots. This is the solution, x, to z = abs(z)*exp(2j*np.pi*x) where z are the roots.

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

statsmodels.iolib.table.SimpleTable.reverse SimpleTable.reverse() L.reverse() ? reverse IN PLACE

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

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

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.