statsmodels.stats.inter_rater.fleiss_kappa statsmodels.stats.inter_rater.fleiss_kappa(table) [source] Fleiss? kappa multi-rater agreement measure Parameters: table : array_like, 2-D assumes subjects in rows, and categories in columns Returns: kappa : float Fleiss?s kappa statistic for inter rater agreement Notes coded from Wikipedia page http://en.wikipedia.org/wiki/Fleiss%27_kappa no variance or tests yet

statsmodels.stats.inter_rater.to_table statsmodels.stats.inter_rater.to_table(data, bins=None) [source] convert raw data with shape (subject, rater) to (rater1, rater2) brings data into correct format for cohens_kappa Parameters: data : array_like, 2-Dim data containing category assignment with subjects in rows and raters in columns. bins : None, int or tuple of array_like If None, then the data is converted to integer categories, 0,1,2,...,n_cat-1. Because of the relabeling only categor

statsmodels.stats.inter_rater.cohens_kappa statsmodels.stats.inter_rater.cohens_kappa(table, weights=None, return_results=True, wt=None) [source] Compute Cohen?s kappa with variance and equal-zero test Parameters: table : array_like, 2-Dim square array with results of two raters, one rater in rows, second rater in columns weights : array_like The interpretation of weights depends on the wt argument. If both are None, then the simple kappa is computed. see wt for the case when wt is not N

statsmodels.stats.inter_rater.aggregate_raters statsmodels.stats.inter_rater.aggregate_raters(data, n_cat=None) [source] convert raw data with shape (subject, rater) to (subject, cat_counts) brings data into correct format for fleiss_kappa bincount will raise exception if data cannot be converted to integer. Parameters: data : array_like, 2-Dim data containing category assignment with subjects in rows and raters in columns. n_cat : None or int If None, then the data is converted to integ

statsmodels.stats.gof.powerdiscrepancy statsmodels.stats.gof.powerdiscrepancy(observed, expected, lambd=0.0, axis=0, ddof=0) [source] Calculates power discrepancy, a class of goodness-of-fit tests as a measure of discrepancy between observed and expected data. This contains several goodness-of-fit tests as special cases, see the describtion of lambd, the exponent of the power discrepancy. The pvalue is based on the asymptotic chi-square distribution of the test statistic. freeman_tukey: D(x|

statsmodels.stats.gof.gof_chisquare_discrete statsmodels.stats.gof.gof_chisquare_discrete(distfn, arg, rvs, alpha, msg) [source] perform chisquare test for random sample of a discrete distribution Parameters: distname : string name of distribution function arg : sequence parameters of distribution alpha : float significance level, threshold for p-value Returns: result : bool 0 if test passes, 1 if test fails Notes originally written for scipy.stats test suite, still needs to be c

statsmodels.stats.gof.gof_binning_discrete statsmodels.stats.gof.gof_binning_discrete(rvs, distfn, arg, nsupp=20) [source] get bins for chisquare type gof tests for a discrete distribution Parameters: rvs : array sample data distname : string name of distribution function arg : sequence parameters of distribution nsupp : integer number of bins. The algorithm tries to find bins with equal weights. depending on the distribution, the actual number of bins can be smaller. Returns: fre

statsmodels.stats.gof.chisquare_effectsize statsmodels.stats.gof.chisquare_effectsize(probs0, probs1, correction=None, cohen=True, axis=0) [source] effect size for a chisquare goodness-of-fit test Parameters: probs0 : array_like probabilities or cell frequencies under the Null hypothesis probs1 : array_like probabilities or cell frequencies under the Alternative hypothesis probs0 and probs1 need to have the same length in the axis dimension. and broadcast in the other dimensions Both pro

statsmodels.stats.diagnostic.recursive_olsresiduals statsmodels.stats.diagnostic.recursive_olsresiduals(olsresults, skip=None, lamda=0.0, alpha=0.95) calculate recursive ols with residuals and cusum test statistic Parameters: olsresults : instance of RegressionResults uses only endog and exog skip : int or None number of observations to use for initial OLS, if None then skip is set equal to the number of regressors (columns in exog) lamda : float weight for Ridge correction to initial

statsmodels.stats.diagnostic.unitroot_adf statsmodels.stats.diagnostic.unitroot_adf(x, maxlag=None, trendorder=0, autolag='AIC', store=False)