3.11.10.5.1. statsmodels.stats.inter_rater.KappaResults

class statsmodels.stats.inter_rater.KappaResults(**kwds)[source]

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.  
__init__(**kwds)

3.11.10.5.1.1. Methods

__init__(**kwds)
clear(() -> None.  Remove all items from D.)
copy(() -> a shallow copy of D)
fromkeys(...) v defaults to None.
get((k[,d]) -> D[k] if k in D, ...)
has_key((k) -> True if D has a key k, else False)
items(() -> list of D’s (key, value) pairs, ...)
iteritems(() -> an iterator over the (key, ...)
iterkeys(() -> an iterator over the keys of D)
itervalues(...)
keys(() -> list of D’s keys)
pop((k[,d]) -> v, ...) If key is not found, d is returned if given, otherwise KeyError is raised
popitem(() -> (k, v), ...) 2-tuple; but raise KeyError if D is empty.
setdefault((k[,d]) -> D.get(k,d), ...)
update(([E, ...) If E present and has a .keys() method, does: for k in E: D[k] = E[k]
values(() -> list of D’s values)
viewitems(...)
viewkeys(...)
viewvalues(...)

3.11.10.5.1.2. Attributes

template