statsmodels.sandbox.regression.gmm.GMM.from_formula classmethod GMM.from_formula(formula, data, subset=None, *args, **kwargs) Create a Model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.DataFrame args : extra ar

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

statsmodels.sandbox.regression.gmm.IV2SLS.information IV2SLS.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.tsa.vector_ar.var_model.VARResults.resid_acov VARResults.resid_acov(nlags=1) [source] Compute centered sample autocovariance (including lag 0) Parameters: nlags : int

statsmodels.robust.robust_linear_model.RLMResults.chisq static RLMResults.chisq() [source]

statsmodels.tsa.vector_ar.var_model.VARResults.irf_errband_mc VARResults.irf_errband_mc(orth=False, repl=1000, T=10, signif=0.05, seed=None, burn=100, cum=False) [source] Compute Monte Carlo integrated error bands assuming normally distributed for impulse response functions Parameters: orth: bool, default False : Compute orthoganalized impulse response error bands repl: int : number of Monte Carlo replications to perform T: int, default 10 : number of impulse response periods signif:

statsmodels.genmod.families.family.Family.starting_mu Family.starting_mu(y) [source] Starting value for mu in the IRLS algorithm. Parameters: y : array The untransformed response variable. Returns: mu_0 : array The first guess on the transformed response variable. Notes Only the Binomial family takes a different initial value.

statsmodels.stats.weightstats.CompareMeans class statsmodels.stats.weightstats.CompareMeans(d1, d2) [source] class for two sample comparison The tests and the confidence interval work for multi-endpoint comparison: If d1 and d2 have the same number of rows, then each column of the data in d1 is compared with the corresponding column in d2. Parameters: d1, d2 : instances of DescrStatsW Notes The result for the statistical tests and the confidence interval are independent of the user specifie

statsmodels.duration.hazard_regression.PHReg.predict PHReg.predict(params, cov_params=None, endog=None, exog=None, strata=None, offset=None, pred_type='lhr') [source] Returns predicted values from the proportional hazards regression model. Parameters: params : array-like The proportional hazards model parameters. cov_params : array-like The covariance matrix of the estimated params vector, used to obtain prediction errors if pred_type=?lhr?, otherwise optional. endog : array-like Durat

statsmodels.tsa.arima_model.ARMA.score ARMA.score(params) [source] Compute the score function at params. Notes This is a numerical approximation.