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.

table_orig

The original table is cached as table_orig.

Type

array-like

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

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.