statsmodels.stats.contingency_tables.Table

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

A two-way contingency table

Parameters:

tablearray_like

A contingency table.

shift_zerosbool

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

Attributes:

table_origarray_like

The original table is cached as table_orig.

Methods

from_data(data[, shift_zeros])	Construct a Table object from data
test_nominal_association()	Assess independence for nominal factors
test_ordinal_association([row_scores, ...])	Assess independence between two ordinal variables

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

Methods

from_data(data[, shift_zeros])	Construct a Table object from data
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