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

Tools Our tool collection contains some convenience functions for users and functions that were written mainly for internal use. Additional to this tools directory, several other subpackages have their own tools modules, for example statsmodels.tsa.tsatools Module Reference Basic tools tools These are basic and miscellaneous tools. The full import path is statsmodels.tools.tools. tools.add_constant(data[, prepend, has_constant]) This appends a column of ones to an array if prepend==False.

statsmodels.miscmodels.tmodel.TLinearModel.score_obs TLinearModel.score_obs(params, **kwds) Jacobian/Gradient of log-likelihood evaluated at params for each observation.

statsmodels.miscmodels.tmodel.TLinearModel.score TLinearModel.score(params) Gradient of log-likelihood evaluated at params

statsmodels.miscmodels.tmodel.TLinearModel.reduceparams TLinearModel.reduceparams(params)

statsmodels.miscmodels.tmodel.TLinearModel.predict TLinearModel.predict(params, exog=None) [source]

statsmodels.miscmodels.tmodel.TLinearModel.nloglikeobs TLinearModel.nloglikeobs(params) [source] Loglikelihood of linear model with t distributed errors. Parameters: params : array The parameters of the model. The last 2 parameters are degrees of freedom and scale. Returns: loglike : array, (nobs,) The log likelihood of the model evaluated at params for each observation defined by self.endog and self.exog. Notes The t distribution is the standard t distribution and not a standardi

statsmodels.miscmodels.tmodel.TLinearModel.nloglike TLinearModel.nloglike(params)

statsmodels.miscmodels.tmodel.TLinearModel.loglikeobs TLinearModel.loglikeobs(params)

statsmodels.miscmodels.tmodel.TLinearModel.loglike TLinearModel.loglike(params) [source]