statsmodels.stats.diagnostic.compare_cox statsmodels.stats.diagnostic.compare_cox = Cox Test for non-nested models Parameters: results_x : Result instance result instance of first model results_z : Result instance result instance of second model attach : bool Formulas from Greene, section 8.3.4 translated to code : produces correct results for Example 8.3, Greene :

statsmodels.miscmodels.count.PoissonGMLE.from_formula classmethod PoissonGMLE.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

statsmodels.sandbox.descstats.sign_test statsmodels.sandbox.descstats.sign_test(samp, mu0=0) Signs test. Parameters: samp : array-like 1d array. The sample for which you want to perform the signs test. mu0 : float See Notes for the definition of the sign test. mu0 is 0 by default, but it is common to set it to the median. Returns: M, p-value : See also scipy.stats.wilcoxon Notes The signs test returns M = (N(+) - N(-))/2 where N(+) is the number of values above mu0, N(-) is the num

statsmodels.tsa.vector_ar.var_model.VARProcess.long_run_effects VARProcess.long_run_effects() [source] Compute long-run effect of unit impulse

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.stats.diagnostic.compare_j statsmodels.stats.diagnostic.compare_j = J-Test for comparing non-nested models Parameters: results_x : Result instance result instance of first model results_z : Result instance result instance of second model attach : bool From description in Greene, section 8.3.3 : produces correct results for Example 8.3, Greene - not checked yet : #currently an exception, but I don?t have clean reload in python session : check what results should be attached

statsmodels.regression.mixed_linear_model.MixedLMResults.random_effects static MixedLMResults.random_effects() [source] Returns the conditional means of all random effects given the data. Returns: random_effects : DataFrame A DataFrame with the distinct group values as the index and the conditional means of the random effects in the columns.

statsmodels.iolib.summary2.Summary.add_array Summary.add_array(array, align='r', float_format='%.4f') [source] Add the contents of a Numpy array to summary table Parameters: array : numpy array (2D) float_format: string : Formatting to array if type is float align : string Data alignment (l/c/r)

statsmodels.miscmodels.count.PoissonGMLE.loglike PoissonGMLE.loglike(params)

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.fit_loc_scale TransfTwo_gen.fit_loc_scale(data, *args) Estimate loc and scale parameters from data using 1st and 2nd moments. Parameters: data : array_like Data to fit. arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information). Returns: Lhat : float Estimated location parameter for the data. Shat : float Estimated scale parameter for the data.