Source code for statsmodels.stats.inter_rater

# -*- coding: utf-8 -*-
"""Inter Rater Agreement

contains
--------
fleiss_kappa
cohens_kappa

aggregate_raters:
    helper function to get data into fleiss_kappa format
to_table:
    helper function to create contingency table, can be used for cohens_kappa

Created on Thu Dec 06 22:57:56 2012
Author: Josef Perktold
License: BSD-3

References
----------
Wikipedia: kappa's initially based on these two pages
    https://en.wikipedia.org/wiki/Fleiss%27_kappa
    https://en.wikipedia.org/wiki/Cohen's_kappa
SAS-Manual : formulas for cohens_kappa, especially variances
see also R package irr

TODO
----
standard errors and hypothesis tests for fleiss_kappa
other statistics and tests,
   in R package irr, SAS has more
inconsistent internal naming, changed variable names as I added more
   functionality
convenience functions to create required data format from raw data
   DONE

"""

from __future__ import division
import numpy as np
from scipy import stats  #get rid of this? need only norm.sf


class ResultsBunch(dict):

    template = '%r'

    def __init__(self, **kwds):
        dict.__init__(self, kwds)
        self.__dict__ = self
        self._initialize()

    def _initialize(self):
        pass

    def __str__(self):
        return self.template % self

def _int_ifclose(x, dec=1, width=4):
    '''helper function for creating result string for int or float

    only dec=1 and width=4 is implemented

    Parameters
    ----------
    x : int or float
        value to format
    dec : 1
        number of decimals to print if x is not an integer
    width : 4
        width of string

    Returns
    -------
    xint : int or float
        x is converted to int if it is within 1e-14 of an integer
    x_string : str
        x formatted as string, either '%4d' or '%4.1f'

    '''
    xint = int(round(x))
    if np.max(np.abs(xint - x)) < 1e-14:
        return xint, '%4d' % xint
    else:
        return x, '%4.1f' % x


[docs]def aggregate_raters(data, n_cat=None): '''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 integer categories, 0,1,2,...,n_cat-1. Because of the relabeling only category levels with non-zero counts are included. If this is an integer, then the category levels in the data are already assumed to be in integers, 0,1,2,...,n_cat-1. In this case, the returned array may contain columns with zero count, if no subject has been categorized with this level. Returns ------- arr : nd_array, (n_rows, n_cat) Contains counts of raters that assigned a category level to individuals. Subjects are in rows, category levels in columns. ''' data = np.asarray(data) n_rows = data.shape[0] if n_cat is None: #I could add int conversion (reverse_index) to np.unique cat_uni, cat_int = np.unique(data.ravel(), return_inverse=True) n_cat = len(cat_uni) data_ = cat_int.reshape(data.shape) else: cat_uni = np.arange(n_cat) #for return only, assumed cat levels data_ = data tt = np.zeros((n_rows, n_cat), int) for idx, row in enumerate(data_): ro = np.bincount(row) tt[idx, :len(ro)] = ro return tt, cat_uni
[docs]def to_table(data, bins=None): '''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 category levels with non-zero counts are included. If this is an integer, then the category levels in the data are already assumed to be in integers, 0,1,2,...,n_cat-1. In this case, the returned array may contain columns with zero count, if no subject has been categorized with this level. If bins are a tuple of two array_like, then the bins are directly used by ``numpy.histogramdd``. This is useful if we want to merge categories. Returns ------- arr : nd_array, (n_cat, n_cat) Contingency table that contains counts of category level with rater1 in rows and rater2 in columns. Notes ----- no NaN handling, delete rows with missing values This works also for more than two raters. In that case the dimension of the resulting contingency table is the same as the number of raters instead of 2-dimensional. ''' data = np.asarray(data) n_rows, n_cols = data.shape if bins is None: #I could add int conversion (reverse_index) to np.unique cat_uni, cat_int = np.unique(data.ravel(), return_inverse=True) n_cat = len(cat_uni) data_ = cat_int.reshape(data.shape) bins_ = np.arange(n_cat+1) - 0.5 #alternative implementation with double loop #tt = np.asarray([[(x == [i,j]).all(1).sum() for j in cat_uni] # for i in cat_uni] ) #other altervative: unique rows and bincount elif np.isscalar(bins): bins_ = np.arange(bins+1) - 0.5 data_ = data else: bins_ = bins data_ = data tt = np.histogramdd(data_, (bins_,)*n_cols) return tt[0], bins_
[docs]def fleiss_kappa(table, method='fleiss'): """Fleiss' and Randolph's kappa multi-rater agreement measure Parameters ---------- table : array_like, 2-D assumes subjects in rows, and categories in columns method : string Method 'fleiss' returns Fleiss' kappa which uses the sample margin to define the chance outcome. Method 'randolph' or 'uniform' (only first 4 letters are needed) returns Randolph's (2005) multirater kappa which assumes a uniform distribution of the categories to define the chance outcome. Returns ------- kappa : float Fleiss's or Randolph's kappa statistic for inter rater agreement Notes ----- no variance or hypothesis tests yet Interrater agreement measures like Fleiss's kappa measure agreement relative to chance agreement. Different authors have proposed ways of defining these chance agreements. Fleiss' is based on the marginal sample distribution of categories, while Randolph uses a uniform distribution of categories as benchmark. Warrens (2010) showed that Randolph's kappa is always larger or equal to Fleiss' kappa. Under some commonly observed condition, Fleiss' and Randolph's kappa provide lower and upper bounds for two similar kappa_like measures by Light (1971) and Hubert (1977). References ---------- Wikipedia https://en.wikipedia.org/wiki/Fleiss%27_kappa Fleiss, Joseph L. 1971. "Measuring Nominal Scale Agreement among Many Raters." Psychological Bulletin 76 (5): 378-82. https://doi.org/10.1037/h0031619. Randolph, Justus J. 2005 "Free-Marginal Multirater Kappa (multirater K [free]): An Alternative to Fleiss' Fixed-Marginal Multirater Kappa." Presented at the Joensuu Learning and Instruction Symposium, vol. 2005 https://eric.ed.gov/?id=ED490661 Warrens, Matthijs J. 2010. "Inequalities between Multi-Rater Kappas." Advances in Data Analysis and Classification 4 (4): 271-86. https://doi.org/10.1007/s11634-010-0073-4. """ table = 1.0 * np.asarray(table) #avoid integer division n_sub, n_cat = table.shape n_total = table.sum() n_rater = table.sum(1) n_rat = n_rater.max() #assume fully ranked assert n_total == n_sub * n_rat #marginal frequency of categories p_cat = table.sum(0) / n_total table2 = table * table p_rat = (table2.sum(1) - n_rat) / (n_rat * (n_rat - 1.)) p_mean = p_rat.mean() if method == 'fleiss': p_mean_exp = (p_cat*p_cat).sum() elif method.startswith('rand') or method.startswith('unif'): p_mean_exp = 1 / n_cat kappa = (p_mean - p_mean_exp) / (1- p_mean_exp) return kappa
[docs]def cohens_kappa(table, weights=None, return_results=True, wt=None): '''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 None If weights is two dimensional, then it is directly used as a weight matrix. For computing the variance of kappa, the maximum of the weights is assumed to be smaller or equal to one. TODO: fix conflicting definitions in the 2-Dim case for wt : None or string If wt and weights are None, then the simple kappa is computed. If wt is given, but weights is None, then the weights are set to be [0, 1, 2, ..., k]. If weights is a one-dimensional array, then it is used to construct the weight matrix given the following options. wt in ['linear', 'ca' or None] : use linear weights, Cicchetti-Allison actual weights are linear in the score "weights" difference wt in ['quadratic', 'fc'] : use linear weights, Fleiss-Cohen actual weights are squared in the score "weights" difference wt = 'toeplitz' : weight matrix is constructed as a toeplitz matrix from the one dimensional weights. return_results : bool If True (default), then an instance of KappaResults is returned. If False, then only kappa is computed and returned. Returns ------- results or kappa If return_results is True (default), then a results instance with all statistics is returned If return_results is False, then only kappa is calculated and returned. Notes ----- There are two conflicting definitions of the weight matrix, Wikipedia versus SAS manual. However, the computation are invariant to rescaling of the weights matrix, so there is no difference in the results. Weights for 'linear' and 'quadratic' are interpreted as scores for the categories, the weights in the computation are based on the pairwise difference between the scores. Weights for 'toeplitz' are a interpreted as weighted distance. The distance only depends on how many levels apart two entries in the table are but not on the levels themselves. example: weights = '0, 1, 2, 3' and wt is either linear or toeplitz means that the weighting only depends on the simple distance of levels. weights = '0, 0, 1, 1' and wt = 'linear' means that the first two levels are zero distance apart and the same for the last two levels. This is the sampe as forming two aggregated levels by merging the first two and the last two levels, respectively. weights = [0, 1, 2, 3] and wt = 'quadratic' is the same as squaring these weights and using wt = 'toeplitz'. References ---------- Wikipedia SAS Manual ''' table = np.asarray(table, float) #avoid integer division agree = np.diag(table).sum() nobs = table.sum() probs = table / nobs freqs = probs #TODO: rename to use freqs instead of probs for observed probs_diag = np.diag(probs) freq_row = table.sum(1) / nobs freq_col = table.sum(0) / nobs prob_exp = freq_col * freq_row[:, None] assert np.allclose(prob_exp.sum(), 1) #print prob_exp.sum() agree_exp = np.diag(prob_exp).sum() #need for kappa_max if weights is None and wt is None: kind = 'Simple' kappa = (agree / nobs - agree_exp) / (1 - agree_exp) if return_results: #variance term_a = probs_diag * (1 - (freq_row + freq_col) * (1 - kappa))**2 term_a = term_a.sum() term_b = probs * (freq_col[:, None] + freq_row)**2 d_idx = np.arange(table.shape[0]) term_b[d_idx, d_idx] = 0 #set diagonal to zero term_b = (1 - kappa)**2 * term_b.sum() term_c = (kappa - agree_exp * (1-kappa))**2 var_kappa = (term_a + term_b - term_c) / (1 - agree_exp)**2 / nobs #term_c = freq_col * freq_row[:, None] * (freq_col + freq_row[:,None]) term_c = freq_col * freq_row * (freq_col + freq_row) var_kappa0 = (agree_exp + agree_exp**2 - term_c.sum()) var_kappa0 /= (1 - agree_exp)**2 * nobs else: if weights is None: weights = np.arange(table.shape[0]) #weights follows the Wikipedia definition, not the SAS, which is 1 - kind = 'Weighted' weights = np.asarray(weights, float) if weights.ndim == 1: if wt in ['ca', 'linear', None]: weights = np.abs(weights[:, None] - weights) / \ (weights[-1] - weights[0]) elif wt in ['fc', 'quadratic']: weights = (weights[:, None] - weights)**2 / \ (weights[-1] - weights[0])**2 elif wt == 'toeplitz': #assume toeplitz structure from scipy.linalg import toeplitz #weights = toeplitz(np.arange(table.shape[0])) weights = toeplitz(weights) else: raise ValueError('wt option is not known') else: rows, cols = table.shape if (table.shape != weights.shape): raise ValueError('weights are not square') #this is formula from Wikipedia kappa = 1 - (weights * table).sum() / nobs / (weights * prob_exp).sum() #TODO: add var_kappa for weighted version if return_results: var_kappa = np.nan var_kappa0 = np.nan #switch to SAS manual weights, problem if user specifies weights #w is negative in some examples, #but weights is scale invariant in examples and rough check of source w = 1. - weights w_row = (freq_col * w).sum(1) w_col = (freq_row[:, None] * w).sum(0) agree_wexp = (w * freq_col * freq_row[:, None]).sum() term_a = freqs * (w - (w_col + w_row[:, None]) * (1 - kappa))**2 fac = 1. / ((1 - agree_wexp)**2 * nobs) var_kappa = term_a.sum() - (kappa - agree_wexp * (1 - kappa))**2 var_kappa *= fac freqse = freq_col * freq_row[:, None] var_kappa0 = (freqse * (w - (w_col + w_row[:, None]))**2).sum() var_kappa0 -= agree_wexp**2 var_kappa0 *= fac kappa_max = (np.minimum(freq_row, freq_col).sum() - agree_exp) / \ (1 - agree_exp) if return_results: res = KappaResults( kind=kind, kappa=kappa, kappa_max=kappa_max, weights=weights, var_kappa=var_kappa, var_kappa0=var_kappa0) return res else: return kappa
_kappa_template = '''\ %(kind)s Kappa Coefficient -------------------------------- Kappa %(kappa)6.4f ASE %(std_kappa)6.4f %(alpha_ci)s%% Lower Conf Limit %(kappa_low)6.4f %(alpha_ci)s%% Upper Conf Limit %(kappa_upp)6.4f Test of H0: %(kind)s Kappa = 0 ASE under H0 %(std_kappa0)6.4f Z %(z_value)6.4f One-sided Pr > Z %(pvalue_one_sided)6.4f Two-sided Pr > |Z| %(pvalue_two_sided)6.4f ''' ''' Weighted Kappa Coefficient -------------------------------- Weighted Kappa 0.4701 ASE 0.1457 95% Lower Conf Limit 0.1845 95% Upper Conf Limit 0.7558 Test of H0: Weighted Kappa = 0 ASE under H0 0.1426 Z 3.2971 One-sided Pr > Z 0.0005 Two-sided Pr > |Z| 0.0010 ''' class KappaResults(ResultsBunch): '''Results for Cohen's kappa Attributes ---------- kappa : cohen's kappa var_kappa : variance of kappa std_kappa : standard deviation of kappa alpha : one-sided probability for confidence interval kappa_low : lower (1-alpha) confidence limit kappa_upp : upper (1-alpha) confidence limit var_kappa0 : variance of kappa under H0: kappa=0 std_kappa0 : standard deviation of kappa under H0: kappa=0 z_value : test statistic for H0: kappa=0, is standard normal distributed pvalue_one_sided : one sided p-value for H0: kappa=0 and H1: kappa>0 pvalue_two_sided : two sided p-value for H0: kappa=0 and H1: kappa!=0 distribution_kappa : asymptotic normal distribution of kappa distribution_zero_null : asymptotic normal distribution of kappa under H0: kappa=0 The confidence interval for kappa and the statistics for the test of H0: kappa=0 are based on the asymptotic normal distribution of kappa. ''' template = _kappa_template def _initialize(self): if 'alpha' not in self: self['alpha'] = 0.025 self['alpha_ci'] = _int_ifclose(100 - 0.025 * 200)[1] self['std_kappa'] = np.sqrt(self['var_kappa']) self['std_kappa0'] = np.sqrt(self['var_kappa0']) self['z_value'] = self['kappa'] / self['std_kappa0'] self['pvalue_one_sided'] = stats.norm.sf(self['z_value']) self['pvalue_two_sided'] = stats.norm.sf(np.abs(self['z_value'])) * 2 delta = stats.norm.isf(self['alpha']) * self['std_kappa'] self['kappa_low'] = self['kappa'] - delta self['kappa_upp'] = self['kappa'] + delta self['distribution_kappa'] = stats.norm(loc=self['kappa'], scale=self['std_kappa']) self['distribution_zero_null'] = stats.norm(loc=0, scale=self['std_kappa0']) def __str__(self): return self.template % self