statsmodels.robust.robust_linear_model.RLM.from_formula classmethod RLM.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 : extr

statsmodels.tsa.vector_ar.var_model.VARResults.test_normality VARResults.test_normality(signif=0.05, verbose=True) [source] Test assumption of normal-distributed errors using Jarque-Bera-style omnibus Chi^2 test Parameters: signif : float Test significance threshold Notes H0 (null) : data are generated by a Gaussian-distributed process

statsmodels.tsa.vector_ar.var_model.VARResults.stderr static VARResults.stderr() [source] Standard errors of coefficients, reshaped to match in size

statsmodels.sandbox.distributions.extras.NormExpan_gen.moment NormExpan_gen.moment(n, *args, **kwds) n?th order non-central moment of distribution. Parameters: n : int, n>=1 Order of moment. arg1, arg2, arg3,... : float The shape parameter(s) for the distribution (see docstring of the instance object for more information). kwds : keyword arguments, optional These can include ?loc? and ?scale?, as well as other keyword arguments relevant for a given distribution.

statsmodels.sandbox.distributions.extras.SkewNorm2_gen.freeze SkewNorm2_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.nonparametric.kde.KDEUnivariate.icdf static KDEUnivariate.icdf() [source] Inverse Cumulative Distribution (Quantile) Function Notes Will not work if fit has not been called. Uses scipy.stats.mstats.mquantiles.

statsmodels.regression.linear_model.GLS.loglike GLS.loglike(params) [source] Returns the value of the Gaussian log-likelihood function at params. Given the whitened design matrix, the log-likelihood is evaluated at the parameter vector params for the dependent variable endog. Parameters: params : array-like The parameter estimates Returns: loglike : float The value of the log-likelihood function for a GLS Model. Notes The log-likelihood function for the normal distribution is Y an

statsmodels.discrete.discrete_model.MNLogit.hessian MNLogit.hessian(params) [source] Multinomial logit Hessian matrix of the log-likelihood Parameters: params : array-like The parameters of the model Returns: hess : ndarray, (J*K, J*K) The Hessian, second derivative of loglikelihood function with respect to the flattened parameters, evaluated at params Notes where equals 1 if j = l and 0 otherwise. The actual Hessian matrix has J**2 * K x K elements. Our Hessian is reshaped to be

statsmodels.genmod.generalized_linear_model.GLMResults.cov_params GLMResults.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-li

statsmodels.regression.quantile_regression.QuantRegResults.condition_number static QuantRegResults.condition_number() Return condition number of exogenous matrix. Calculated as ratio of largest to smallest eigenvalue.