statsmodels.sandbox.distributions.transformed.TransfTwo_gen.entropy TransfTwo_gen.entropy(*args, **kwds) Differential entropy of the RV. 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).

statsmodels.genmod.families.family.NegativeBinomial.resid_dev NegativeBinomial.resid_dev(endog, mu, scale=1.0) [source] Negative Binomial Deviance Residual Parameters: endog : array-like endog is the response variable mu : array-like mu is the fitted value of the model scale : float, optional An optional argument to divide the residuals by scale Returns: resid_dev : array The array of deviance residuals Notes resid_dev = sign(endog-mu) * sqrt(piecewise) where piecewise is defined

statsmodels.sandbox.tsa.movstat.movvar statsmodels.sandbox.tsa.movstat.movvar(x, windowsize=3, lag='lagged') [source] moving window variance Parameters: x : array time series data windsize : int window size lag : ?lagged?, ?centered?, or ?leading? location of window relative to current position Returns: mk : array moving variance, with same shape as x

statsmodels.stats.power.FTestPower.plot_power FTestPower.plot_power(dep_var='nobs', nobs=None, effect_size=None, alpha=0.05, ax=None, title=None, plt_kwds=None, **kwds) plot power with number of observations or effect size on x-axis Parameters: dep_var : string in [?nobs?, ?effect_size?, ?alpha?] This specifies which variable is used for the horizontal axis. If dep_var=?nobs? (default), then one curve is created for each value of effect_size. If dep_var=?effect_size? or alpha, then one cur

statsmodels.genmod.families.family.Family class statsmodels.genmod.families.family.Family(link, variance) [source] The parent class for one-parameter exponential families. Parameters: link : a link function instance Link is the linear transformation function. See the individual families for available links. variance : a variance function Measures the variance as a function of the mean probabilities. See the individual families for the default variance function. See also Link Functions

statsmodels.stats.diagnostic.CompareCox class statsmodels.stats.diagnostic.CompareCox 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 Formulas from Greene, section 8.3.4 translated to code : produces correct results for Example 8.3, Greene : Methods run(results_x, results_z[, attach]) run Cox test for non-nested models

statsmodels.tsa.vector_ar.dynamic.DynamicVAR.coefs static DynamicVAR.coefs() [source] Return dynamic regression coefficients as WidePanel

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

statsmodels.discrete.discrete_model.DiscreteResults.summary DiscreteResults.summary(yname=None, xname=None, title=None, alpha=0.05, yname_list=None) [source] Summarize the Regression Results Parameters: yname : string, optional Default is y xname : list of strings, optional Default is var_## for ## in p the number of regressors title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence interv

statsmodels.discrete.discrete_model.NegativeBinomialResults.initialize NegativeBinomialResults.initialize(model, params, **kwd)