statsmodels.stats.contingency_tables.Table2x2

class statsmodels.stats.contingency_tables.Table2x2(table, shift_zeros=True)

Analyses that can be performed on a 2x2 contingency table.

Parameters:

table : array_like

A 2x2 contingency table

shift_zeros : bool

If true, 0.5 is added to all cells of the table if any cell is equal to zero.

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.

Note that for the risk ratio, the analysis is not symmetric with respect to the rows and columns of the contingency table. The two rows define population subgroups, column 0 is the number of ‘events’, and column 1 is the number of ‘non-events’.

Methods

from_data(data[, shift_zeros])	Construct a Table object from data.
homogeneity([method])	Compare row and column marginal distributions.
log_oddsratio_confint([alpha, method])	A confidence level for the log odds ratio.
log_oddsratio_pvalue([null])	P-value for a hypothesis test about the log odds ratio.
log_riskratio_confint([alpha, method])	A confidence interval for the log risk ratio.
log_riskratio_pvalue([null])	p-value for a hypothesis test about the log risk ratio.
oddsratio_confint([alpha, method])	A confidence interval for the odds ratio.
oddsratio_pvalue([null])	P-value for a hypothesis test about the odds ratio.
riskratio_confint([alpha, method])	A confidence interval for the risk ratio.
riskratio_pvalue([null])	p-value for a hypothesis test about the risk ratio.
summary([alpha, float_format, method])	Summarizes results for a 2x2 table 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

from_data(data[, shift_zeros])	Construct a Table object from data.
homogeneity([method])	Compare row and column marginal distributions.
log_oddsratio_confint([alpha, method])	A confidence level for the log odds ratio.
log_oddsratio_pvalue([null])	P-value for a hypothesis test about the log odds ratio.
log_riskratio_confint([alpha, method])	A confidence interval for the log risk ratio.
log_riskratio_pvalue([null])	p-value for a hypothesis test about the log risk ratio.
oddsratio_confint([alpha, method])	A confidence interval for the odds ratio.
oddsratio_pvalue([null])	P-value for a hypothesis test about the odds ratio.
riskratio_confint([alpha, method])	A confidence interval for the risk ratio.
riskratio_pvalue([null])	p-value for a hypothesis test about the risk ratio.
summary([alpha, float_format, method])	Summarizes results for a 2x2 table 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.

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.
log_oddsratio	Returns the log odds ratio for a 2x2 table.
log_oddsratio_se	Returns the standard error for the log odds ratio.
log_riskratio	Returns the log of the risk ratio.
log_riskratio_se	Returns the standard error of the log of the risk ratio.
marginal_probabilities	Estimate marginal probability distributions for the rows and columns.
oddsratio	Returns the odds ratio for a 2x2 table.
resid_pearson	Returns Pearson residuals.
riskratio	Returns the risk ratio for a 2x2 table.
standardized_resids	Returns standardized residuals under independence.