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.sandbox.stats.multicomp.MultiComparison.allpairtest MultiComparison.allpairtest(testfunc, alpha=0.05, method='bonf', pvalidx=1) [source] run a pairwise test on all pairs with multiple test correction The statistical test given in testfunc is calculated for all pairs and the p-values are adjusted by methods in multipletests. The p-value correction is generic and based only on the p-values, and does not take any special structure of the hypotheses into account. Parameters: testfun

statsmodels.regression.linear_model.OLSResults.save OLSResults.save(fname, remove_data=False) save a pickle of this instance Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. remove_data : bool If False (default), then the instance is pickled without changes. If True, then all arrays with length nobs are set to None before pickling. See the remove_data method. In some cases not all arrays will be set to None. Notes If remove_dat

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.isf ExpTransf_gen.isf(q, *args, **kwds) Inverse survival function at q of the given RV. Parameters: q : array_like upper tail probability 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: x : ndarray or scalar Qu

statsmodels.genmod.cov_struct.Exchangeable.covariance_matrix Exchangeable.covariance_matrix(expval, index) [source] Returns the working covariance or correlation matrix for a given cluster of data. Parameters: endog_expval: array-like : The expected values of endog for the cluster for which the covariance or correlation matrix will be returned index: integer : The index of the cluster for which the covariane or correlation matrix will be returned Returns: M: matrix : The covariance o

statsmodels.genmod.cov_struct.GlobalOddsRatio.observed_crude_oddsratio GlobalOddsRatio.observed_crude_oddsratio() [source] To obtain the crude (global) odds ratio, first pool all binary indicators corresponding to a given pair of cut points (c,c?), then calculate the odds ratio for this 2x2 table. The crude odds ratio is the inverse variance weighted average of these odds ratios. Since the covariate effects are ignored, this OR will generally be greater than the stratified OR.

statsmodels.genmod.families.family.Binomial.fitted Binomial.fitted(lin_pred) Fitted values based on linear predictors lin_pred. Parameters: lin_pred : array Values of the linear predictor of the model. dot(X,beta) in a classical linear model. Returns: mu : array The mean response variables given by the inverse of the link function.

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.from_formula classmethod NonlinearIVGMM.from_formula(formula, data, subset=None, *args, **kwargs) Create a Model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.Data

statsmodels.sandbox.stats.multicomp.distance_st_range statsmodels.sandbox.stats.multicomp.distance_st_range(mean_all, nobs_all, var_all, df=None, triu=False) [source] pairwise distance matrix, outsourced from tukeyhsd CHANGED: meandiffs are with sign, studentized range uses abs q_crit added for testing TODO: error in variance calculation when nobs_all is scalar, missing 1/n

statsmodels.discrete.discrete_model.CountResults.load classmethod CountResults.load(fname) load a pickle, (class method) Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. Returns: unpickled instance :