Some functions for working with contingency tables (i.e. cross tabulations).
||Return a list of the marginal sums of the array a.|
||Compute the expected frequencies from a contingency table.|
||Chi-square test of independence of variables in a contingency table.|
Return a list of the marginal sums of the array a.
- a : ndarray
- The array for which to compute the marginal sums.
- margsums : list of ndarrays
- A list of length a.ndim. margsums[k] is the result of summing a over all axes except k; it has the same number of dimensions as a, but the length of each axis except axis k will be 1.
>>> a = np.arange(12).reshape(2, 6) >>> a array([[ 0, 1, 2, 3, 4, 5], [ 6, 7, 8, 9, 10, 11]]) >>> m0, m1 = margins(a) >>> m0 array([, ]) >>> m1 array([[ 6, 8, 10, 12, 14, 16]])
>>> b = np.arange(24).reshape(2,3,4) >>> m0, m1, m2 = margins(b) >>> m0 array([[[ 66]], []]) >>> m1 array([[[ 60], [ 92], ]]) >>> m2 array([[[60, 66, 72, 78]]])
Compute the expected frequencies from a contingency table.
Given an n-dimensional contingency table of observed frequencies, compute the expected frequencies for the table based on the marginal sums under the assumption that the groups associated with each dimension are independent.
- observed : array_like
- The table of observed frequencies. (While this function can handle a 1-D array, that case is trivial. Generally observed is at least 2-D.)
- expected : ndarray of float64
- The expected frequencies, based on the marginal sums of the table. Same shape as observed.
>>> observed = np.array([[10, 10, 20],[20, 20, 20]]) >>> from scipy.stats import expected_freq >>> expected_freq(observed) array([[ 12., 12., 16.], [ 18., 18., 24.]])
chi2_contingency(observed, correction=True, lambda_=None)¶
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. The expected frequencies are computed based on the marginal sums under the assumption of independence; see scipy.stats.contingency.expected_freq. The number of degrees of freedom is (expressed using numpy functions and attributes):
dof = observed.size - sum(observed.shape) + observed.ndim - 1
- 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”.
- correction : bool, optional
- If True, and the degrees of freedom is 1, apply Yates’ correction for continuity. The effect of the correction is to adjust each observed value by 0.5 towards the corresponding expected value.
- lambda_ : float or str, optional.
- By default, the statistic computed in this test is Pearson’s chi-squared statistic . lambda_ allows a statistic from the Cressie-Read power divergence family  to be used instead. See power_divergence for details.
- chi2 : float
- The test statistic.
- p : float
- The p-value of the test
- dof : int
- Degrees of freedom
- expected : ndarray, same shape as observed
- The expected frequencies, based on the marginal sums of the table.
contingency.expected_freq fisher_exact chisquare power_divergence
An often quoted guideline for the validity of this calculation is that the test should be used only if the observed and expected frequency in each cell is at least 5.
This is a test for the independence of different categories of a population. The test is only meaningful when the dimension of observed is two or more. Applying the test to a one-dimensional table will always result in expected equal to observed and a chi-square statistic equal to 0.
This function does not handle masked arrays, because the calculation does not make sense with missing values.
Like stats.chisquare, this function computes a chi-square statistic; the convenience this function provides is to figure out the expected frequencies and degrees of freedom from the given contingency table. If these were already known, and if the Yates’ correction was not required, one could use stats.chisquare. That is, if one calls:
chi2, p, dof, ex = chi2_contingency(obs, correction=False)
then the following is true:
(chi2, p) == stats.chisquare(obs.ravel(), f_exp=ex.ravel(), ddof=obs.size - 1 - dof)
The lambda_ argument was added in version 0.13.0 of scipy.
 “Contingency table”, http://en.wikipedia.org/wiki/Contingency_table  “Pearson’s chi-squared test”, http://en.wikipedia.org/wiki/Pearson%27s_chi-squared_test  Cressie, N. and Read, T. R. C., “Multinomial Goodness-of-Fit Tests”, J. Royal Stat. Soc. Series B, Vol. 46, No. 3 (1984), pp. 440-464.
A two-way example (2 x 3):
>>> from scipy.stats import chi2_contingency >>> obs = np.array([[10, 10, 20], [20, 20, 20]]) >>> chi2_contingency(obs) (2.7777777777777777, 0.24935220877729619, 2, array([[ 12., 12., 16.], [ 18., 18., 24.]]))
Perform the test using the log-likelihood ratio (i.e. the “G-test”) instead of Pearson’s chi-squared statistic.
>>> g, p, dof, expctd = chi2_contingency(obs, lambda_="log-likelihood") >>> g, p (2.7688587616781319, 0.25046668010954165)
A four-way example (2 x 2 x 2 x 2):
>>> obs = np.array( ... [[[[12, 17], ... [11, 16]], ... [[11, 12], ... [15, 16]]], ... [[[23, 15], ... [30, 22]], ... [[14, 17], ... [15, 16]]]]) >>> chi2_contingency(obs) (8.7584514426741897, 0.64417725029295503, 11, array([[[[ 14.15462386, 14.15462386], [ 16.49423111, 16.49423111]], [[ 11.2461395 , 11.2461395 ], [ 13.10500554, 13.10500554]]], [[[ 19.5591166 , 19.5591166 ], [ 22.79202844, 22.79202844]], [[ 15.54012004, 15.54012004], [ 18.10873492, 18.10873492]]]]))