HairEyeColor {datasets} | R Documentation |
Distribution of hair and eye color and sex in 592 statistics students.
HairEyeColor
A 3-dimensional array resulting from cross-tabulating 592 observations on 3 variables. The variables and their levels are as follows:
No | Name | Levels |
1 | Hair | Black, Brown, Red, Blond |
2 | Eye | Brown, Blue, Hazel, Green |
3 | Sex | Male, Female |
The Hair x Eye table comes from a survey of students at
the University of Delaware reported by Snee (1974). The split by
Sex
was added by Friendly (1992a) for didactic purposes.
This data set is useful for illustrating various techniques for the analysis of contingency tables, such as the standard chi-squared test or, more generally, log-linear modelling, and graphical methods such as mosaic plots, sieve diagrams or association plots.
http://www.datavis.ca/sas/vcd/catdata/haireye.sas
Snee (1974) gives the two-way table aggregated over Sex
. The
Sex
split of the ‘Brown hair, Brown eye’ cell was
changed to agree with that used by Friendly (2000).
Snee, R. D. (1974). Graphical display of two-way contingency tables. The American Statistician, 28, 9–12. doi: 10.2307/2683520.
Friendly, M. (1992a). Graphical methods for categorical data. SAS User Group International Conference Proceedings, 17, 190–200. http://datavis.ca/papers/sugi/sugi17.pdf
Friendly, M. (1992b). Mosaic displays for loglinear models. Proceedings of the Statistical Graphics Section, American Statistical Association, pp. 61–68. http://www.datavis.ca/papers/asa92.html
Friendly, M. (2000). Visualizing Categorical Data. SAS Institute, ISBN 1-58025-660-0.
chisq.test
,
loglin
,
mosaicplot
require(graphics) ## Full mosaic mosaicplot(HairEyeColor) ## Aggregate over sex (as in Snee's original data) x <- apply(HairEyeColor, c(1, 2), sum) x mosaicplot(x, main = "Relation between hair and eye color")