statsmodels.sandbox.distributions.transformed.LogTransf_gen.freeze LogTransf_gen.freeze(*args, **kwds) Freeze the distribution for the given arguments. Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution. Should include all the non-optional arguments, may include loc and scale. Returns: rv_frozen : rv_frozen instance The frozen distribution.

statsmodels.sandbox.distributions.transformed.LogTransf_gen.interval LogTransf_gen.interval(alpha, *args, **kwds) Confidence interval with equal areas around the median. Parameters: alpha : array_like of float Probability that an rv will be drawn from the returned range. Each value should be in the range [0, 1]. arg1, arg2, ... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information). loc : array_like, optional location parame

statsmodels.sandbox.distributions.transformed.LogTransf_gen.fit LogTransf_gen.fit(data, *args, **kwds) Return MLEs for shape, location, and scale parameters from data. MLE stands for Maximum Likelihood Estimate. Starting estimates for the fit are given by input arguments; for any arguments not provided with starting estimates, self._fitstart(data) is called to generate such. One can hold some parameters fixed to specific values by passing in keyword arguments f0, f1, ..., fn (for shape param

statsmodels.sandbox.distributions.transformed.LogTransf_gen.expect LogTransf_gen.expect(func=None, args=(), loc=0, scale=1, lb=None, ub=None, conditional=False, **kwds) Calculate expected value of a function with respect to the distribution. The expected value of a function f(x) with respect to a distribution dist is defined as: ubound E[x] = Integral(f(x) * dist.pdf(x)) lbound Parameters: func : callable, optional Function for which integral is calculated. Takes only one

statsmodels.sandbox.distributions.transformed.LogTransf_gen.est_loc_scale LogTransf_gen.est_loc_scale(*args, **kwds) est_loc_scale is deprecated! This function is deprecated, use self.fit_loc_scale(data) instead.

statsmodels.sandbox.distributions.transformed.LogTransf_gen.entropy LogTransf_gen.entropy(*args, **kwds) Differential entropy of the RV. Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information). loc : array_like, optional Location parameter (default=0). scale : array_like, optional Scale parameter (default=1).

statsmodels.sandbox.distributions.transformed.LogTransf_gen.cdf LogTransf_gen.cdf(x, *args, **kwds) Cumulative distribution function of the given RV. Parameters: x : array_like quantiles arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional scale parameter (default=1) Returns: cdf : ndarray Cumulative distributi

statsmodels.discrete.discrete_model.LogitResults.summary2 LogitResults.summary2(yname=None, xname=None, title=None, alpha=0.05, float_format='%.4f') Experimental function to summarize regression results Parameters: xname : List of strings of length equal to the number of parameters Names of the independent variables (optional) yname : string Name of the dependent variable (optional) title : string, optional Title for the top table. If not None, then this replaces the default title alp

statsmodels.discrete.discrete_model.LogitResults.t_test LogitResults.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 tuple of

statsmodels.discrete.discrete_model.LogitResults.wald_test LogitResults.wald_test(r_matrix, cov_p=None, scale=1.0, invcov=None, use_f=None) Compute a Wald-test for a joint linear hypothesis. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. 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 tu