statsmodels.sandbox.distributions.transformed.ExpTransf_gen.freeze ExpTransf_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.regression.gmm.GMM.from_formula classmethod GMM.from_formula(formula, data, subset=None, *args, **kwargs) Create a Model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.DataFrame args : extra ar

statsmodels.stats.moment_helpers.corr2cov statsmodels.stats.moment_helpers.corr2cov(corr, std) [source] convert correlation matrix to covariance matrix given standard deviation Parameters: corr : array_like, 2d correlation matrix, see Notes std : array_like, 1d standard deviation Returns: cov : ndarray (subclass) covariance matrix Notes This function does not convert subclasses of ndarrays. This requires that multiplication is defined elementwise. np.ma.array are allowed, but not m

statsmodels.duration.hazard_regression.PHRegResults.cov_params PHRegResults.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-lik

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

statsmodels.discrete.discrete_model.MultinomialModel class statsmodels.discrete.discrete_model.MultinomialModel(endog, exog, **kwargs) [source] Methods cdf(X) The cumulative distribution function of the model. cov_params_func_l1(likelihood_model, xopt, ...) Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. fit([start_params, method, maxiter, ...]) Fit the model using maximum likelihood. fit_regularized([start_p

statsmodels.tools.numdiff.approx_hess3 statsmodels.tools.numdiff.approx_hess3(x, f, epsilon=None, args=(), kwargs={}) [source] Calculate Hessian with finite difference derivative approximation Parameters: x : array_like value at which function derivative is evaluated f : function function of one array f(x, *args, **kwargs) epsilon : float or array-like, optional Stepsize used, if None, then stepsize is automatically chosen according to EPS**(1/4)*x. args : tuple Arguments for functio

statsmodels.genmod.generalized_linear_model.GLMResults.normalized_cov_params GLMResults.normalized_cov_params()

statsmodels.nonparametric.kernel_density.KDEMultivariate class statsmodels.nonparametric.kernel_density.KDEMultivariate(data, var_type, bw=None, defaults=) [source] Multivariate kernel density estimator. This density estimator can handle univariate as well as multivariate data, including mixed continuous / ordered discrete / unordered discrete data. It also provides cross-validated bandwidth selection methods (least squares, maximum likelihood). Parameters: data: list of ndarrays or 2-D nda

statsmodels.robust.norms.HuberT.psi HuberT.psi(z) [source] The psi function for Huber?s t estimator The analytic derivative of rho Parameters: z : array-like 1d array Returns: psi : array psi(z) = z for |z| <= t psi(z) = sign(z)*t for |z| > t