statsmodels.discrete.discrete_model.ProbitResults.summary2 ProbitResults.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 a

statsmodels.discrete.discrete_model.MultinomialResults.initialize MultinomialResults.initialize(model, params, **kwd)

statsmodels.genmod.families.links.identity.deriv identity.deriv(p) Derivative of the power transform Parameters: p : array-like Mean parameters Returns: g?(p) : array Derivative of power transform of p Notes g?(p) = power * p`**(`power - 1)

statsmodels.tsa.vector_ar.var_model.VARProcess.mse VARProcess.mse(steps) [source] Compute theoretical forecast error variance matrices Parameters: steps : int Number of steps ahead Returns: forc_covs : ndarray (steps x neqs x neqs) Notes

statsmodels.stats.weightstats.ztost statsmodels.stats.weightstats.ztost(x1, low, upp, x2=None, usevar='pooled', ddof=1.0) [source] Equivalence test based on normal distribution Parameters: x1 : array_like one sample or first sample for 2 independent samples low, upp : float equivalence interval low < m1 - m2 < upp x1 : array_like or None second sample for 2 independent samples test. If None, then a one-sample test is performed. usevar : string, ?pooled? If pooled, then the stan

statsmodels.discrete.discrete_model.MNLogit.score MNLogit.score(params) [source] Score matrix for multinomial logit model log-likelihood Parameters: params : array The parameters of the multinomial logit model. Returns: score : ndarray, (K * (J-1),) The 2-d score vector, i.e. the first derivative of the loglikelihood function, of the multinomial logit model evaluated at params. Notes for In the multinomial model the score matrix is K x J-1 but is returned as a flattened array to

statsmodels.regression.linear_model.OLSResults.cov_HC2 static OLSResults.cov_HC2() See statsmodels.RegressionResults

statsmodels.genmod.families.links.Log class statsmodels.genmod.families.links.Log [source] The log transform Notes call and derivative call a private method _clean to trim the data by machine epsilon so that p is in (0,1). log is an alias of Log. Methods deriv(p) Derivative of log transform link function deriv2(p) Second derivative of the link function g??(p) inverse(z) Inverse of log transform link function inverse_deriv(z) Derivative of the inverse of the log transform link function

Generalized Method of Moments gmm statsmodels.gmm contains model classes and functions that are based on estimation with Generalized Method of Moments. Currently the general non-linear case is implemented. An example class for the standard linear instrumental variable model is included. This has been introduced as a test case, it works correctly but it does not take the linear structure into account. For the linear case we intend to introduce a specific implementation which will be faster and n

statsmodels.iolib.summary2.Summary.add_df Summary.add_df(df, index=True, header=True, float_format='%.4f', align='r') [source] Add the contents of a DataFrame to summary table Parameters: df : DataFrame header: bool : Reproduce the DataFrame column labels in summary table index: bool : Reproduce the DataFrame row labels in summary table float_format: string : Formatting to float data columns align : string Data alignment (l/c/r)