statsmodels.stats.contingency_tables.Table

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

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

table_orig (array-like) The original table is cached as table_orig.

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.
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.
test_nominal_association() Assess independence for nominal factors.
test_ordinal_association([row_scores, …]) Assess independence between two ordinal variables.