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

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.tools.eval_measures.hqic_sigma statsmodels.tools.eval_measures.hqic_sigma(sigma2, nobs, df_modelwc, islog=False) [source] Hannan-Quinn information criterion (HQC) Parameters: sigma2 : float estimate of the residual variance or determinant of Sigma_hat in the multivariate case. If islog is true, then it is assumed that sigma is already log-ed, for example logdetSigma. nobs : int number of observations df_modelwc : int number of parameters including constant Returns: hqic

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.bic_sigma statsmodels.tools.eval_measures.bic_sigma(sigma2, nobs, df_modelwc, islog=False) [source] Bayesian information criterion (BIC) or Schwarz criterion Parameters: sigma2 : float estimate of the residual variance or determinant of Sigma_hat in the multivariate case. If islog is true, then it is assumed that sigma is already log-ed, for example logdetSigma. nobs : int number of observations df_modelwc : int number of parameters including constant

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

statsmodels.tools.eval_measures.bias statsmodels.tools.eval_measures.bias(x1, x2, axis=0) [source] bias, mean 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: bias : ndarray or float bias, or mean difference along given axis. Notes If x1 and x2 have different shapes, then they need to broadcast. This uses numpy.asanyarray to convert the input. Wh

statsmodels.tools.eval_measures.aic_sigma statsmodels.tools.eval_measures.aic_sigma(sigma2, nobs, df_modelwc, islog=False) [source] Akaike information criterion Parameters: sigma2 : float estimate of the residual variance or determinant of Sigma_hat in the multivariate case. If islog is true, then it is assumed that sigma is already log-ed, for example logdetSigma. nobs : int number of observations df_modelwc : int number of parameters including constant Returns: aic : float inform

statsmodels.tools.eval_measures.aicc_sigma statsmodels.tools.eval_measures.aicc_sigma(sigma2, nobs, df_modelwc, islog=False) [source] Akaike information criterion (AIC) with small sample correction Parameters: sigma2 : float estimate of the residual variance or determinant of Sigma_hat in the multivariate case. If islog is true, then it is assumed that sigma is already log-ed, for example logdetSigma. nobs : int number of observations df_modelwc : int number of parameters including con

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