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

statsmodels.discrete.discrete_model.Logit.initialize Logit.initialize() Initialize is called by statsmodels.model.LikelihoodModel.__init__ and should contain any preprocessing that needs to be done for a model.

statsmodels.tsa.arima_model.ARIMAResults.fittedvalues static ARIMAResults.fittedvalues()

statsmodels.sandbox.regression.try_catdata.labelmeanfilter_str statsmodels.sandbox.regression.try_catdata.labelmeanfilter_str(ys, x) [source]

statsmodels.robust.norms.RobustNorm.rho RobustNorm.rho(z) [source] The robust criterion estimator function. Abstract method: -2 loglike used in M-estimator

statsmodels.iolib.summary2.Summary.as_text Summary.as_text() [source] Generate ASCII Summary Table

statsmodels.regression.linear_model.RegressionResults.t_test RegressionResults.t_test(r_matrix, cov_p=None, scale=None, use_t=None) Compute a t-test for a each linear hypothesis of the form Rb = q Parameters: r_matrix : array-like, str, tuple array : If an array is given, a p x k 2d array or length k 1d array specifying the linear restrictions. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples. tuple : A

statsmodels.sandbox.distributions.transformed.squaretg statsmodels.sandbox.distributions.transformed.squaretg = Distribution based on a non-monotonic (u- or hump-shaped transformation) the constructor can be called with a distribution class, and functions that define the non-linear transformation. and generates the distribution of the transformed random variable Note: the transformation, it?s inverse and derivatives need to be fully specified: func, funcinvplus, funcinvminus, derivplus, der

statsmodels.tsa.arima_model.ARIMA.initialize ARIMA.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.

statsmodels.tsa.arima_model.ARIMA.information ARIMA.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.