`scipy.stats._multivariate:random_table_gen`

source: /scipy/stats/_multivariate.py :6100

Signature

class random_table_gen ( seed = None )

Members

Summary

Contingency tables from independent samples with fixed marginal sums.

Extended Summary

This is the distribution of random tables with given row and column vector sums. This distribution represents the set of random tables under the null hypothesis that rows and columns are independent. It is used in hypothesis tests of independence.

Because of assumed independence, the expected frequency of each table element can be computed from the row and column sums, so that the distribution is completely determined by these two vectors.

Parameters

row : array_like: Sum of table entries in each row.
col : array_like: Sum of table entries in each column.
seed : {None, int, np.random.RandomState, np.random.Generator}, optional: Used for drawing random variates. If seed is None, the ~np.random.RandomState singleton is used. If seed is an int, a new RandomState instance is used, seeded with seed. If seed is already a RandomState or Generator instance, then that object is used. Default is None.

Methods

logpmf(x): Log-probability of table x to occur in the distribution.
pmf(x): Probability of table x to occur in the distribution.
mean(row, col): Mean table.
rvs(row, col, size=None, method=None, random_state=None): Draw random tables with given row and column vector sums.

Notes

The row and column vectors must be one-dimensional, not empty, and each sum up to the same value. They cannot contain negative or noninteger entries.

Random elements from the distribution are generated either with Boyett's ^[1] or Patefield's algorithm ^[2]. Boyett's algorithm has O(N) time and space complexity, where N is the total sum of entries in the table. Patefield's algorithm has O(K x log(N)) time complexity, where K is the number of cells in the table and requires only a small constant work space. By default, the rvs method selects the fastest algorithm based on the input, but you can specify the algorithm with the keyword method. Allowed values are "boyett" and "patefield".

Examples

from scipy.stats import random_table

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row = [1, 5]
col = [2, 3, 1]

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random_table.mean(row, col)

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Alternatively, the object may be called (as a function) to fix the row and column vector sums, returning a "frozen" distribution.

dist = random_table(row, col)
dist.rvs(random_state=123)

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Aliases

scipy.stats._multivariate.random_table_gen