statsmodels.sandbox.distributions.extras.ACSkewT_gen.sf ACSkewT_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 at x

statsmodels.genmod.cov_struct.Independence.update Independence.update(params) [source] Updates the association parameter values based on the current regression coefficients. Parameters: params : array-like Working values for the regression parameters.

statsmodels.genmod.families.links.nbinom class statsmodels.genmod.families.links.nbinom(alpha=1.0) [source] The negative binomial link function. Notes g(p) = log(p/(p + 1/alpha)) nbinom is an alias of NegativeBinomial. nbinom = NegativeBinomial(alpha=1.) Methods deriv(p) Derivative of the negative binomial transform inverse(z) Inverse of the negative binomial transform inverse_deriv(z) Derivative of the inverse of the negative binomial transform

statsmodels.regression.linear_model.yule_walker statsmodels.regression.linear_model.yule_walker(X, order=1, method='unbiased', df=None, inv=False, demean=True) [source] Estimate AR(p) parameters from a sequence X using Yule-Walker equation. Unbiased or maximum-likelihood estimator (mle) See, for example: http://en.wikipedia.org/wiki/Autoregressive_moving_average_model Parameters: X : array-like 1d array order : integer, optional The order of the autoregressive process. Default is 1. met

statsmodels.genmod.families.family.Binomial.loglike Binomial.loglike(endog, mu, scale=1.0) [source] Loglikelihood function for Binomial exponential family 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 If endog is binary: llf = scale*sum(endog*log(mu/(1-

statsmodels.sandbox.distributions.transformed.LogTransf_gen.sf LogTransf_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 evalu

statsmodels.sandbox.stats.multicomp.TukeyHSDResults class statsmodels.sandbox.stats.multicomp.TukeyHSDResults(mc_object, results_table, q_crit, reject=None, meandiffs=None, std_pairs=None, confint=None, df_total=None, reject2=None, variance=None) [source] Results from Tukey HSD test, with additional plot methods Can also compute and plot additional post-hoc evaluations using this results class. Notes halfwidths is only available after call to plot_simultaneous. Other attributes contain infor

statsmodels.stats.weightstats.DescrStatsW.tconfint_mean DescrStatsW.tconfint_mean(alpha=0.05, alternative='two-sided') [source] two-sided confidence interval for weighted mean of data If the data is 2d, then these are separate confidence intervals for each column. Parameters: alpha : float significance level for the confidence interval, coverage is 1-alpha alternative : string This specifies the alternative hypothesis for the test that corresponds to the confidence interval. The alternat

statsmodels.stats.weightstats.DescrStatsW.corrcoef static DescrStatsW.corrcoef() [source] weighted correlation with default ddof assumes variables in columns and observations in rows

statsmodels.stats.weightstats.DescrStatsW.zconfint_mean DescrStatsW.zconfint_mean(alpha=0.05, alternative='two-sided') [source] two-sided confidence interval for weighted mean of data Confidence interval is based on normal distribution. If the data is 2d, then these are separate confidence intervals for each column. Parameters: alpha : float significance level for the confidence interval, coverage is 1-alpha alternative : string This specifies the alternative hypothesis for the test that