# statsmodels.stats.contingency_tables.Table2x2¶

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

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.
log_oddsratio[source]

The log odds ratio of the table.

Type: float
log_oddsratio_se[source]

The asymptotic standard error of the estimated log odds ratio.

Type: float
oddsratio[source]

The odds ratio of the table.

Type: float
riskratio[source]

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.

Type: float
log_riskratio[source]

The estimated log risk ratio for the table.

Type: float
log_riskratio_se[source]

The standard error of the estimated log risk ratio for the table.

Type: float

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

 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. homogeneity([method]) Compare row and column marginal distributions. 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_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() Returns the standard error for the log odds ratio. log_riskratio() Returns the log od the risk 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. 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. 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() Returns Pearson residuals. riskratio() Returns the risk ratio for a 2x2 table. 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() Returns standardized residuals under independence. 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.