Standardized residuals of a contingency table

Computes standardized or adjusted residuals of a (possibly) weighted contingency table

stdres.table(x, y, weights = NULL, na.rm = FALSE,
  na.value = "NAs", digits = 3, residuals = "std")

Arguments

x: the first categorical variable
y: the second categorical variable
weights: numeric vector of weights. If NULL (default), uniform weights (i.e. all equal to 1) are used.
na.rm: logical, indicating whether NA values should be silently removed before the computation proceeds. If FALSE (default), an additional level is added to the variables (see na.value argument).
na.value: character. Name of the level for NA category. Default is "NAs". Only used if na.rm = FALSE.
digits: integer. The number of digits (default is 3). If NULL, the results are not rounded.
residuals: If "std" (default), standardized (i.e. Pearson) residuals are computed. If "adj", adjusted standardized residuals are computed.

Value

A table with the residuals

Note

The adjusted standardized residuals are strictly equivalent to test-values for nominal variables as proposed by Lebart et al (1984).

References

Agresti, A. (2007). An Introduction to Categorical Data Analysis, 2nd ed. New York: John Wiley & Sons.

Rakotomalala R., Comprendre la taille d'effet (effect size), http://eric.univ-lyon2.fr/~ricco/cours/slides/effect_size.pdf

Lebart L., Morineau A. and Warwick K., 1984, *Multivariate Descriptive Statistical Analysis*, John Wiley and sons, New-York.

Author

Nicolas Robette

Examples

data(Movies)
stdres.table(Movies$Country, Movies$ArtHouse)
#>            No    Yes
#> Europe  0.327 -0.337
#> France -5.612  5.772
#> Other  -2.014  2.072
#> USA     8.445 -8.685