statsmodels.stats.contingency_tables.SquareTable

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

Methods for analyzing a square contingency table.

Parameters
  • table (array-like) – A square contingency table, or DataFrame that is converted to a square form.

  • shift_zeros (boolean) – If True and any cell count is zero, add 0.5 to all values in the table.

  • methods should only be used when the rows and columns of the (These) –

  • have the same categories. If table is provided as a (table) –

  • DataFrame, the row and column indices will be extended to (Pandas) –

  • a square table, inserting zeros where a row or column is (create) –

  • Otherwise the table should be provided in a square form, (missing.) –

  • the (implicit) row and column categories appearing in the (with) –

  • order. (same) –

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.

marginal_probabilities()

Estimate marginal probability distributions for the rows and columns.

resid_pearson()

Returns Pearson residuals.

standardized_resids()

Returns standardized residuals under independence.

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.