statsmodels.genmod.families.family.InverseGaussian.loglike InverseGaussian.loglike(endog, mu, scale=1.0) [source] Loglikelihood function for inverse Gaussian distribution. Parameters: endog : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional The default is 1. Returns: llf : float The value of the loglikelihood function evaluated at (endog,mu,scale) as defined below. Notes llf = -(1/2.)*sum((endog-mu)**2/(endog*mu**2*sca

statsmodels.tsa.ar_model.ARResults.aic static ARResults.aic() [source]

statsmodels.stats.diagnostic.breaks_cusumolsresid statsmodels.stats.diagnostic.breaks_cusumolsresid(olsresidual, ddof=0) cusum test for parameter stability based on ols residuals Parameters: olsresiduals : ndarray array of residuals from an OLS estimation ddof : int number of parameters in the OLS estimation, used as degrees of freedom correction for error variance. Returns: sup_b : float test statistic, maximum of absolute value of scaled cumulative OLS residuals pval : float Prob

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.sandbox.regression.gmm.NonlinearIVGMM.gmmobjective NonlinearIVGMM.gmmobjective(params, weights) objective function for GMM minimization Parameters: params : array parameter values at which objective is evaluated weights : array weighting matrix Returns: jval : float value of objective function

statsmodels.tsa.arima_model.ARIMAResults.conf_int ARIMAResults.conf_int(alpha=0.05, cols=None, method='default') Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The significance level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return method : string Not Implemented Yet Method to estimate the confidence_interval. ?Defaul

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.fitstart NonlinearIVGMM.fitstart() [source]

statsmodels.genmod.families.links.CDFLink.inverse_deriv CDFLink.inverse_deriv(z) [source] Derivative of the inverse of the CDF transformation link function Parameters: z : array The inverse of the link function at p Returns: The value of the derivative of the inverse of the logit function :

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.var TransfTwo_gen.var(*args, **kwds) Variance of the distribution Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional scale parameter (default=1) Returns: var : float the variance of the distribution

statsmodels.stats.power.NormalIndPower.power NormalIndPower.power(effect_size, nobs1, alpha, ratio=1, alternative='two-sided') [source] Calculate the power of a t-test for two independent sample Parameters: effect_size : float standardized effect size, difference between the two means divided by the standard deviation. effect size has to be positive. nobs1 : int or float number of observations of sample 1. The number of observations of sample two is ratio times the size of sample 1, i.e.