statsmodels.stats.power.FTestAnovaPower.power FTestAnovaPower.power(effect_size, nobs, alpha, k_groups=2) [source] Calculate the power of a F-test for one factor ANOVA. Parameters: effect_size : float standardized effect size, mean divided by the standard deviation. effect size has to be positive. nobs : int or float sample size, number of observations. alpha : float in interval (0,1) significance level, e.g. 0.05, is the probability of a type I error, that is wrong rejections if the N

statsmodels.duration.hazard_regression.PHRegResults.martingale_residuals static PHRegResults.martingale_residuals() [source] The martingale residuals.

statsmodels.genmod.families.family.Poisson class statsmodels.genmod.families.family.Poisson(link=) [source] Poisson exponential family. Parameters: link : a link instance, optional The default link for the Poisson family is the log link. Available links are log, identity, and sqrt. See statsmodels.family.links for more information. See also statsmodels.genmod.families.family.Family, Link Functions Attributes Poisson.link a link instance The link function of the Poisson instance. Poisso

statsmodels.stats.outliers_influence.OLSInfluence.resid_studentized_external static OLSInfluence.resid_studentized_external() [source] (cached attribute) studentized residuals using LOOO variance this uses sigma from leave-one-out estimates requires leave one out loop for observations

statsmodels.genmod.families.links.inverse_power.deriv2 inverse_power.deriv2(p) Second derivative of the link function g??(p) implemented through numerical differentiation

statsmodels.sandbox.distributions.extras.SkewNorm2_gen.cdf SkewNorm2_gen.cdf(x, *args, **kwds) Cumulative distribution function of the given RV. Parameters: x : array_like quantiles 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: cdf : ndarray Cumulative distribution fu

statsmodels.sandbox.distributions.extras.NormExpan_gen.sf NormExpan_gen.sf(x, *args, **kwds) Survival function (1-cdf) at x of the given RV. Parameters: x : array_like quantiles 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: sf : array_like Survival function evaluated

statsmodels.sandbox.distributions.extras.NormExpan_gen.freeze NormExpan_gen.freeze(*args, **kwds) Freeze the distribution for the given arguments. Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution. Should include all the non-optional arguments, may include loc and scale. Returns: rv_frozen : rv_frozen instance The frozen distribution.

statsmodels.tsa.vector_ar.var_model.VARResults.test_whiteness VARResults.test_whiteness(nlags=10, plot=True, linewidth=8) [source] Test white noise assumption. Sample (Y) autocorrelations are compared with the standard bounds. Parameters: plot : boolean, default True Plot autocorrelations with 2 / sqrt(T) bounds

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