statsmodels.sandbox.regression.gmm.GMMResults.llf static GMMResults.llf()

statsmodels.tsa.arima_model.ARMAResults.remove_data ARMAResults.remove_data() remove data arrays, all nobs arrays from result and model This reduces the size of the instance, so it can be pickled with less memory. Currently tested for use with predict from an unpickled results and model instance. Warning Since data and some intermediate results have been removed calculating new statistics that require them will raise exceptions. The exception will occur the first time an attribute is access

statsmodels.regression.linear_model.GLS class statsmodels.regression.linear_model.GLS(endog, exog, sigma=None, missing='none', hasconst=None, **kwargs) [source] Generalized least squares model with a general covariance structure. Parameters: endog : array-like 1-d endogenous response variable. The dependent variable. exog : array-like A nobs x k array where nobs is the number of observations and k is the number of regressors. An intercept is not included by default and should be added by

statsmodels.discrete.discrete_model.DiscreteResults.cov_params DiscreteResults.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-

statsmodels.tsa.ar_model.ARResults.sigma2 static ARResults.sigma2() [source]

statsmodels.regression.quantile_regression.QuantRegResults.mse static QuantRegResults.mse() [source]

statsmodels.regression.quantile_regression.QuantRegResults.mse_model static QuantRegResults.mse_model() [source]

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

statsmodels.stats.diagnostic.HetGoldfeldQuandt.run HetGoldfeldQuandt.run(y, x, idx=None, split=None, drop=None, alternative='increasing', attach=True) see class docstring

statsmodels.regression.quantile_regression.QuantRegResults.compare_lr_test QuantRegResults.compare_lr_test(restricted, large_sample=False) Likelihood ratio test to test whether restricted model is correct Parameters: restricted : Result instance The restricted model is assumed to be nested in the current model. The result instance of the restricted model is required to have two attributes, residual sum of squares, ssr, residual degrees of freedom, df_resid. large_sample : bool Flag indic