statsmodels.stats.contingency_tables.SquareTable

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

Methods for analyzing a square contingency table.

Parameters:

table : array-like

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

shift_zeros : boolean

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. 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()
cumulative_log_oddsratios()
cumulative_oddsratios()
fittedvalues()
from_data(data[, shift_zeros])	Construct a Table object from data.
homogeneity([method])	Compare row and column marginal distributions.
independence_probabilities()
local_log_oddsratios()
local_oddsratios()
marginal_probabilities()
resid_pearson()
standardized_resids()
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.

Methods

chi2_contribs()
cumulative_log_oddsratios()
cumulative_oddsratios()
fittedvalues()
from_data(data[, shift_zeros])	Construct a Table object from data.
homogeneity([method])	Compare row and column marginal distributions.
independence_probabilities()
local_log_oddsratios()
local_oddsratios()
marginal_probabilities()
resid_pearson()
standardized_resids()
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.