statsmodels.stats.contingency_tables.SquareTable¶

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

Methods for analyzing a square contingency table.

Parameters:¶

tablearray_like

A square contingency table, or DataFrame that is converted to a square form.

shift_zerosbool

If True and any cell count is zero, add 0.5 to all values in the table.

Attributes:¶

chi2_contribs

Returns the contributions to the chi^2 statistic for independence.

The returned table contains the contribution of each cell to the chi^2 test statistic for the null hypothesis that the rows and columns are independent.

cumulative_log_oddsratios

Returns cumulative log odds ratios.

The cumulative log odds ratios for a contingency table with ordered rows and columns are calculated by collapsing all cells to the left/right and above/below a given point, to obtain a 2x2 table from which a log odds ratio can be calculated.

cumulative_oddsratios

Returns the cumulative odds ratios for a contingency table.

See documentation for cumulative_log_oddsratio.

fittedvalues

Returns fitted cell counts under independence.

The returned cell counts are estimates under a model where the rows and columns of the table are independent.

independence_probabilities

Returns fitted joint probabilities under independence.

The returned table is outer(row, column), where row and column are the estimated marginal distributions of the rows and columns.

local_log_oddsratios

Returns local log odds ratios.

The local log odds ratios are the log odds ratios calculated for contiguous 2x2 sub-tables.

local_oddsratios

Returns local odds ratios.

See documentation for local_log_oddsratios.

marginal_probabilities

Estimate marginal probability distributions for the rows and columns.

rowndarray

Marginal row probabilities

colndarray

Marginal column probabilities

resid_pearson

Returns Pearson residuals.

The Pearson residuals are calculated under a model where the rows and columns of the table are independent.

standardized_resids

Returns standardized residuals under independence.

Notes

These methods should only be used when the rows and columns of the table have the same categories. If table is provided as a Pandas DataFrame, the row and column indices will be extended to create a square table, inserting zeros where a row or column is missing. Otherwise the table should be provided in a square form, with the (implicit) row and column categories appearing in the same order.

Methods

from_data(data[, shift_zeros])	Construct a Table object from data.
homogeneity([method])	Compare row and column marginal distributions.
summary([alpha, float_format])	Produce a summary of the 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.
marginal_probabilities	Estimate marginal probability distributions for the rows and columns.
resid_pearson	Returns Pearson residuals.
standardized_resids	Returns standardized residuals under independence.