def chi2_contingency(observed):
'''
Chi-square test of independence of variables in a contingency table.
This function computes the chi-square statistic and p-value for the hypothesis test of
independence of the observed frequencies in the contingency table observed.
:param observed: (*array_like*) The contingency table. The table contains the observed
frequencies (i.e. number of occurrences) in each category. In the two-dimensional case,
the table is often described as an `R x C table`.
:returns: Chi-square statistic and p-value
'''
if isinstance(observed, list):
observed = MIArray(ArrayUtil.array(observed))
r = StatsUtil.chiSquareTest(observed.asarray())
return r[0], r[1]
def chi2_contingency(observed):
'''
Chi-square test of independence of variables in a contingency table.
This function computes the chi-square statistic and p-value for the hypothesis test of
independence of the observed frequencies in the contingency table observed.
:param observed: (*array_like*) The contingency table. The table contains the observed
frequencies (i.e. number of occurrences) in each category. In the two-dimensional case,
the table is often described as an `R x C table`.
:returns: Chi-square statistic and p-value
'''
if isinstance(observed, list):
observed = MIArray(ArrayUtil.array(observed))
r = StatsUtil.chiSquareTest(observed.asarray())
return r[0], r[1]
def chisquare(f_obs, f_exp=None):
'''
Calculates a one-way chi square test.
The chi square test tests the null hypothesis that the categorical data has the
given frequencies.
:param f_obs: (*array_like*) Observed frequencies in each category.
:param f_exp: (*array_like*) Expected frequencies in each category. By default the categories
are assumed to be equally likely.
:returns: Chi-square statistic and p-value
'''
if isinstance(f_obs, list):
f_obs = MIArray(ArrayUtil.array(f_obs))
if f_exp is None:
n = len(f_obs)
f_exp = minum.ones(n) / n * f_obs.sum()
elif isinstance(f_exp, list):
f_exp = MIArray(ArrayUtil.array(f_exp))
r = StatsUtil.chiSquareTest(f_exp.asarray(), f_obs.asarray())
return r[0], r[1]
def chisquare(f_obs, f_exp=None):
'''
Calculates a one-way chi square test.
The chi square test tests the null hypothesis that the categorical data has the
given frequencies.
:param f_obs: (*array_like*) Observed frequencies in each category.
:param f_exp: (*array_like*) Expected frequencies in each category. By default the categories
are assumed to be equally likely.
:returns: Chi-square statistic and p-value
'''
if isinstance(f_obs, list):
f_obs = MIArray(ArrayUtil.array(f_obs))
if f_exp is None:
n = len(f_obs)
f_exp = minum.ones(n) / n * f_obs.sum()
elif isinstance(f_exp, list):
f_exp = MIArray(ArrayUtil.array(f_exp))
r = StatsUtil.chiSquareTest(f_exp.asarray(), f_obs.asarray())
return r[0], r[1]