statsmodels.sandbox.distributions.transformed.TransfTwo_gen.freeze TransfTwo_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.TransfTwo_gen.est_loc_scale TransfTwo_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.TransfTwo_gen.expect TransfTwo_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.TransfTwo_gen.fit TransfTwo_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.TransfTwo_gen.entropy TransfTwo_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.TransfTwo_gen.cdf TransfTwo_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.tools.tools.unsqueeze statsmodels.tools.tools.unsqueeze(data, axis, oldshape) [source] Unsqueeze a collapsed array >>> from numpy import mean >>> from numpy.random import standard_normal >>> x = standard_normal((3,4,5)) >>> m = mean(x, axis=1) >>> m.shape (3, 5) >>> m = unsqueeze(m, 1, x.shape) >>> m.shape (3, 1, 5) >>>

statsmodels.tools.tools.recipr0 statsmodels.tools.tools.recipr0(X) [source] Return the reciprocal of an array, setting all entries equal to 0 as 0. It does not assume that X should be positive in general.

statsmodels.tools.tools.recipr statsmodels.tools.tools.recipr(X) [source] Return the reciprocal of an array, setting all entries less than or equal to 0 to 0. Therefore, it presumes that X should be positive in general.

statsmodels.tools.tools.isestimable statsmodels.tools.tools.isestimable(C, D) [source] True if (Q, P) contrast C is estimable for (N, P) design D From an Q x P contrast matrix C and an N x P design matrix D, checks if the contrast C is estimable by looking at the rank of vstack([C,D]) and verifying it is the same as the rank of D. Parameters: C : (Q, P) array-like contrast matrix. If C has is 1 dimensional assume shape (1, P) D: (N, P) array-like : design matrix Returns: tf : bool Tr