statsmodels.genmod.cov_struct.Autoregressive.covariance_matrix Autoregressive.covariance_matrix(endog_expval, index) [source] Returns the working covariance or correlation matrix for a given cluster of data. Parameters: endog_expval: array-like : The expected values of endog for the cluster for which the covariance or correlation matrix will be returned index: integer : The index of the cluster for which the covariane or correlation matrix will be returned Returns: M: matrix : The co

statsmodels.discrete.discrete_model.CountResults.summary2 CountResults.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.sandbox.stats.runs.symmetry_bowker statsmodels.sandbox.stats.runs.symmetry_bowker(table) [source] Test for symmetry of a (k, k) square contingency table This is an extension of the McNemar test to test the Null hypothesis that the contingency table is symmetric around the main diagonal, that is n_{i, j} = n_{j, i} for all i, j Parameters: table : array_like, 2d, (k, k) a square contingency table that contains the count for k categories in rows and columns. Returns: statistic

statsmodels.tsa.vector_ar.var_model.VARResults.cov_params static VARResults.cov_params() [source] Estimated variance-covariance of model coefficients Notes Covariance of vec(B), where B is the matrix [intercept, A_1, ..., A_p] (K x (Kp + 1)) Adjusted to be an unbiased estimator Ref: Lutkepohl p.74-75

statsmodels.robust.robust_linear_model.RLMResults.pvalues static RLMResults.pvalues() [source]

statsmodels.genmod.families.links.cauchy class statsmodels.genmod.families.links.cauchy [source] The Cauchy (standard Cauchy CDF) transform Notes g(p) = scipy.stats.cauchy.ppf(p) cauchy is an alias of CDFLink with dbn=scipy.stats.cauchy Methods deriv(p) Derivative of CDF link deriv2(p) Second derivative of the link function g??(p) inverse(z) The inverse of the CDF link inverse_deriv(z) Derivative of the inverse of the CDF transformation link function

statsmodels.regression.linear_model.OLS.from_formula classmethod OLS.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 a

statsmodels.sandbox.regression.gmm.GMMResults.wald_test GMMResults.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 tuple o

statsmodels.discrete.discrete_model.MultinomialModel.predict MultinomialModel.predict(params, exog=None, linear=False) [source] Predict response variable of a model given exogenous variables. Parameters: params : array-like 2d array of fitted parameters of the model. Should be in the order returned from the model. exog : array-like 1d or 2d array of exogenous values. If not supplied, the whole exog attribute of the model is used. If a 1d array is given it assumed to be 1 row of exogenous

statsmodels.sandbox.distributions.extras.ACSkewT_gen.var ACSkewT_gen.var(*args, **kwds) Variance of the distribution 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) Returns: var : float the variance of the distribution