statsmodels.sandbox.regression.gmm.IVGMMResults.pvalues static IVGMMResults.pvalues()

statsmodels.discrete.discrete_model.LogitResults.llf static LogitResults.llf()

statsmodels.stats.weightstats.DescrStatsW.nobs static DescrStatsW.nobs() [source] alias for number of observations/cases, equal to sum of weights

statsmodels.regression.quantile_regression.QuantRegResults.bse static QuantRegResults.bse()

statsmodels.tsa.vector_ar.var_model.VARResults.plotsim VARResults.plotsim(steps=1000) Plot a simulation from the VAR(p) process for the desired number of steps

statsmodels.miscmodels.count.PoissonOffsetGMLE.initialize PoissonOffsetGMLE.initialize()

statsmodels.robust.robust_linear_model.RLMResults.cov_params RLMResults.cov_params(r_matrix=None, column=None, scale=None, cov_p=None, other=None) Returns the variance/covariance matrix. The variance/covariance matrix can be of a linear contrast of the estimates of params or all params multiplied by scale which will usually be an estimate of sigma^2. Scale is assumed to be a scalar. Parameters: r_matrix : array-like Can be 1d, or 2d. Can be used alone or with other. column : array-like, o

statsmodels.stats.sandwich_covariance.cov_hc0 statsmodels.stats.sandwich_covariance.cov_hc0(results) [source] See statsmodels.RegressionResults

statsmodels.discrete.discrete_model.Probit.information Probit.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.duration.hazard_regression.PHRegResults.tvalues static PHRegResults.tvalues() Return the t-statistic for a given parameter estimate.