statsmodels.tools.eval_measures.hqic statsmodels.tools.eval_measures.hqic(llf, nobs, df_modelwc) [source] Hannan-Quinn information criterion (HQC) Parameters: llf : float value of the loglikelihood nobs : int number of observations df_modelwc : int number of parameters including constant Returns: hqic : float information criterion References Wikipedia doesn?t say much

statsmodels.tools.eval_measures.iqr statsmodels.tools.eval_measures.iqr(x1, x2, axis=0) [source] interquartile range of error rounded index, no interpolations this could use newer numpy function instead Parameters: x1, x2 : array_like The performance measure depends on the difference between these two arrays. axis : int axis along which the summary statistic is calculated Returns: mse : ndarray or float mean squared error along given axis. Notes If x1 and x2 have different shapes,

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.robust.robust_linear_model.RLMResults.conf_int RLMResults.conf_int(alpha=0.05, cols=None, method='default') Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The significance level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return method : string Not Implemented Yet Method to estimate the confidence_interval.