statsmodels.tools.eval_measures.medianabs statsmodels.tools.eval_measures.medianabs(x1, x2, axis=0) [source] median absolute error 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: medianabs : ndarray or float median absolute difference along given axis. Notes If x1 and x2 have different shapes, then they need to broadcast. This uses numpy.asanyarray to

statsmodels.tools.eval_measures.aic statsmodels.tools.eval_measures.aic(llf, nobs, df_modelwc) [source] Akaike information criterion Parameters: llf : float value of the loglikelihood nobs : int number of observations df_modelwc : int number of parameters including constant Returns: aic : float information criterion References http://en.wikipedia.org/wiki/Akaike_information_criterion

statsmodels.genmod.families.family.Gamma.deviance Gamma.deviance(endog, mu, scale=1.0) [source] Gamma deviance function Parameters: endog : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional An optional scale argument Returns: deviance : float Deviance function as defined below Notes deviance = 2*sum((endog - mu)/mu - log(endog/mu))

statsmodels.miscmodels.count.PoissonOffsetGMLE.nloglikeobs PoissonOffsetGMLE.nloglikeobs(params) [source] Loglikelihood of Poisson model Parameters: params : array-like The parameters of the model. Returns: The log likelihood of the model evaluated at `params` : Notes

statsmodels.tsa.vector_ar.var_model.VARResults.irf_errband_mc VARResults.irf_errband_mc(orth=False, repl=1000, T=10, signif=0.05, seed=None, burn=100, cum=False) [source] Compute Monte Carlo integrated error bands assuming normally distributed for impulse response functions Parameters: orth: bool, default False : Compute orthoganalized impulse response error bands repl: int : number of Monte Carlo replications to perform T: int, default 10 : number of impulse response periods signif:

statsmodels.tsa.tsatools.lagmat2ds statsmodels.tsa.tsatools.lagmat2ds(x, maxlag0, maxlagex=None, dropex=0, trim='forward') [source] generate lagmatrix for 2d array, columns arranged by variables Parameters: x : array_like, 2d 2d data, observation in rows and variables in columns maxlag0 : int for first variable all lags from zero to maxlag are included maxlagex : None or int max lag for all other variables all lags from zero to maxlag are included dropex : int (default is 0) exclude

statsmodels.regression.mixed_linear_model.MixedLMResults class statsmodels.regression.mixed_linear_model.MixedLMResults(model, params, cov_params) [source] Class to contain results of fitting a linear mixed effects model. MixedLMResults inherits from statsmodels.LikelihoodModelResults Parameters: See statsmodels.LikelihoodModelResults : Returns: **Attributes** : model : class instance Pointer to PHreg model instance that called fit. normalized_cov_params : array The sampling covariance

statsmodels.iolib.table.csv2st statsmodels.iolib.table.csv2st(csvfile, headers=False, stubs=False, title=None) [source] Return SimpleTable instance, created from the data in csvfile, which is in comma separated values format. The first row may contain headers: set headers=True. The first column may contain stubs: set stubs=True. Can also supply headers and stubs as tuples of strings.

statsmodels.robust.robust_linear_model.RLMResults.summary2 RLMResults.summary2(xname=None, yname=None, title=None, alpha=0.05, float_format='%.4f') [source] Experimental summary function for 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 tit

statsmodels.regression.linear_model.OLS.score OLS.score(params) Score vector of model. The gradient of logL with respect to each parameter.