statsmodels.stats.diagnostic.CompareCox.run CompareCox.run(results_x, results_z, attach=True) run 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 If true, then the intermediate results are attached to the instance. Returns: tstat : float t statistic for the test that including the fitted values of the first model in the second model has no effect. pvalue

statsmodels.robust.norms.RamsayE.weights RamsayE.weights(z) [source] Ramsay?s Ea weighting function for the IRLS algorithm The psi function scaled by z Parameters: z : array-like 1d array Returns: weights : array weights(z) = exp(-a*|z|)

statsmodels.genmod.families.family.Poisson.starting_mu Poisson.starting_mu(y) Starting value for mu in the IRLS algorithm. Parameters: y : array The untransformed response variable. Returns: mu_0 : array The first guess on the transformed response variable. Notes Only the Binomial family takes a different initial value.

statsmodels.genmod.generalized_estimating_equations.GEEResults.plot_isotropic_dependence GEEResults.plot_isotropic_dependence(ax=None, xpoints=10, min_n=50) [source] Create a plot of the pairwise products of within-group residuals against the corresponding time differences. This plot can be used to assess the possible form of an isotropic covariance structure. Parameters: ax : Matplotlib axes instance An axes on which to draw the graph. If None, new figure and axes objects are created xpo

statsmodels.genmod.families.family.Gaussian.starting_mu Gaussian.starting_mu(y) Starting value for mu in the IRLS algorithm. Parameters: y : array The untransformed response variable. Returns: mu_0 : array The first guess on the transformed response variable. Notes Only the Binomial family takes a different initial value.

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

statsmodels.tsa.arima_process.arma_generate_sample statsmodels.tsa.arima_process.arma_generate_sample(ar, ma, nsample, sigma=1, distrvs=, burnin=0) [source] Generate a random sample of an ARMA process Parameters: ar : array_like, 1d coefficient for autoregressive lag polynomial, including zero lag ma : array_like, 1d coefficient for moving-average lag polynomial, including zero lag nsample : int length of simulated time series sigma : float standard deviation of noise distrvs : func

statsmodels.sandbox.distributions.transformed.LogTransf_gen.median LogTransf_gen.median(*args, **kwds) Median 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 is 0. scale : array_like, optional Scale parameter, Default is 1. Returns: median : float The median of the distribution. See also stats.distribut

statsmodels.genmod.families.family.Gamma.resid_dev Gamma.resid_dev(endog, mu, scale=1.0) [source] Gamma deviance residuals Parameters: endog : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional An optional argument to divide the residuals by scale Returns: resid_dev : array Deviance residuals as defined below Notes resid_dev is defined

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