statsmodels.regression.linear_model.OLSResults.load classmethod OLSResults.load(fname) load a pickle, (class method) Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. Returns: unpickled instance :

statsmodels.regression.linear_model.RegressionResults.HC2_se static RegressionResults.HC2_se() [source] See statsmodels.RegressionResults

statsmodels.regression.linear_model.OLSResults.pvalues static OLSResults.pvalues()

statsmodels.tsa.vector_ar.var_model.VARResults.llf static VARResults.llf() [source] Compute VAR(p) loglikelihood

statsmodels.genmod.families.links.CLogLog.deriv CLogLog.deriv(p) [source] Derivative of C-Log-Log transform link function Parameters: p : array-like Mean parameters Returns: g?(p) : array The derivative of the CLogLog transform link function Notes g?(p) = - 1 / (log(p) * p)

statsmodels.regression.linear_model.GLS.initialize GLS.initialize()

statsmodels.tsa.arima_model.ARIMAResults.normalized_cov_params ARIMAResults.normalized_cov_params()

statsmodels.discrete.discrete_model.Logit.cov_params_func_l1 Logit.cov_params_func_l1(likelihood_model, xopt, retvals) Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. Returns a full cov_params matrix, with entries corresponding to zero?d values set to np.nan.

statsmodels.miscmodels.tmodel.TLinearModel.score TLinearModel.score(params) Gradient of log-likelihood evaluated at params

statsmodels.tsa.vector_ar.var_model.VAR.initialize VAR.initialize() Initialize (possibly re-initialize) a Model instance. For instance, the design matrix of a linear model may change and some things must be recomputed.