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_zerosboolean

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

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.
from_data(data[, shift_zeros])	Construct a Table object from data.
homogeneity([method])	Compare row and column marginal distributions.
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.
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.