statsmodels.stats.contingency_tables.SquareTable

class statsmodels.stats.contingency_tables.SquareTable(table, shift_zeros=True)

Methods for analyzing a square contingency table.

Parameters

tablearray_like

A square contingency table, or DataFrame that is converted to a square form.

shift_zerosbool

If True and any cell count is zero, add 0.5 to all values in the table.

These methods should only be used when the rows and columns of the

table have the same categories. If `table` is provided as a

Pandas DataFrame, the row and column indices will be extended to

create a square table, inserting zeros where a row or column is

missing. Otherwise the table should be provided in a square form,

with the (implicit) row and column categories appearing in the

same order.

Methods

from_data(data[, shift_zeros])	Construct a Table object from data.
homogeneity([method])	Compare row and column marginal distributions.
summary([alpha, float_format])	Produce a summary of the analysis.
symmetry([method])	Test for symmetry of a joint distribution.
test_nominal_association()	Assess independence for nominal factors.
test_ordinal_association([row_scores, …])	Assess independence between two ordinal variables.

Properties

chi2_contribs	Returns the contributions to the chi^2 statistic for independence.
cumulative_log_oddsratios	Returns cumulative log odds ratios.
cumulative_oddsratios	Returns the cumulative odds ratios for a contingency table.
fittedvalues	Returns fitted cell counts under independence.
independence_probabilities	Returns fitted joint probabilities under independence.
local_log_oddsratios	Returns local log odds ratios.
local_oddsratios	Returns local odds ratios.
marginal_probabilities	Estimate marginal probability distributions for the rows and columns.
resid_pearson	Returns Pearson residuals.
standardized_resids	Returns standardized residuals under independence.