statsmodels.stats.contingency_tables.Table

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

Analyses that can be performed on a two-way contingency table.

Parameters:

table : array-like

A contingency table.

shift_zeros : boolean

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

See also

statsmodels.graphics.mosaicplot.mosaic, scipy.stats.chi2_contingency

Notes

The inference procedures used here are all based on a sampling model in which the units are independent and identically distributed, with each unit being classified with respect to two categorical variables.

References

Definitions of residuals:

https://onlinecourses.science.psu.edu/stat504/node/86

Attributes

marginal_probabilities()
independence_probabilities()
fittedvalues()
resid_pearson()
standardized_resids()
chi2_contribs()
local_oddsratios()
cumulative_log_oddsratios()
cumulative_oddsratios()

table_orig	array-like	The original table is cached as table_orig.
local_logodds_ratios	ndarray	The local log odds ratios are calculated for each 2x2 subtable formed from adjacent rows and columns.

Methods

chi2_contribs()
cumulative_log_oddsratios()
cumulative_oddsratios()
fittedvalues()
from_data(data[, shift_zeros])	Construct a Table object from data.
independence_probabilities()
local_log_oddsratios()
local_oddsratios()
marginal_probabilities()
resid_pearson()
standardized_resids()
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.
independence_probabilities()
local_log_oddsratios()
local_oddsratios()
marginal_probabilities()
resid_pearson()
standardized_resids()
test_nominal_association()	Assess independence for nominal factors.
test_ordinal_association([row_scores, ...])	Assess independence between two ordinal variables.