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 : boolean

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’.

Attributes

log_oddsratio	(float) The log odds ratio of the table.
log_oddsratio_se	(float) The asymptotic standard error of the estimated log odds ratio.
oddsratio	(float) The odds ratio of the table.
riskratio	(float) The ratio between the risk in the first row and the risk in the second row. Column 0 is interpreted as containing the number of occurences of the event of interest.
log_riskratio	(float) The estimated log risk ratio for the table.
log_riskratio_se	(float) The standard error of the estimated log risk ratio for the table.

Methods

chi2_contribs()
cumulative_log_oddsratios()
cumulative_oddsratios()
fittedvalues()
from_data(data[, shift_zeros])	Construct a Table object from data.
homogeneity([method])	Compare row and column marginal distributions.
independence_probabilities()
local_log_oddsratios()
local_oddsratios()
log_oddsratio()
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_oddsratio_se()
log_riskratio()
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.
log_riskratio_se()
marginal_probabilities()
oddsratio()
oddsratio_confint([alpha, method])	A confidence interval for the odds ratio.
oddsratio_pvalue([null])	P-value for a hypothesis test about the odds ratio.
resid_pearson()
riskratio()
riskratio_confint([alpha, method])	A confidence interval for the risk ratio.
riskratio_pvalue([null])	p-value for a hypothesis test about the risk ratio.
standardized_resids()
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

chi2_contribs()
cumulative_log_oddsratios()
cumulative_oddsratios()
fittedvalues()
from_data(data[, shift_zeros])	Construct a Table object from data.
homogeneity([method])	Compare row and column marginal distributions.
independence_probabilities()
local_log_oddsratios()
local_oddsratios()
log_oddsratio()
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_oddsratio_se()
log_riskratio()
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.
log_riskratio_se()
marginal_probabilities()
oddsratio()
oddsratio_confint([alpha, method])	A confidence interval for the odds ratio.
oddsratio_pvalue([null])	P-value for a hypothesis test about the odds ratio.
resid_pearson()
riskratio()
riskratio_confint([alpha, method])	A confidence interval for the risk ratio.
riskratio_pvalue([null])	p-value for a hypothesis test about the risk ratio.
standardized_resids()
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.