data (HairEyeColor)
These data contain two tables, one for men and one for women. If we want to unify all the data in one, we will do the following:
globaltable = HairEyeColor [,, 1] + HairEyeColor [,, 2]
To save the data for men and women in two different tables:
table.men = HairEyeColor [,, 1] table.women = HairEyeColor [,, 2]
library(ca)
data(HairEyeColor)
HairEyeColor
## , , Sex = Male
##
## Eye
## Hair Brown Blue Hazel Green
## Black 32 11 10 3
## Brown 53 50 25 15
## Red 10 10 7 7
## Blond 3 30 5 8
##
## , , Sex = Female
##
## Eye
## Hair Brown Blue Hazel Green
## Black 36 9 5 2
## Brown 66 34 29 14
## Red 16 7 7 7
## Blond 4 64 5 8
tabla.global = HairEyeColor[,,1]+HairEyeColor[,,2]
tabla.hombres = HairEyeColor[,,1]
tabla.mujeres = HairEyeColor[,,2]
#TABLE MEN
tabla.hombres
## Eye
## Hair Brown Blue Hazel Green
## Black 32 11 10 3
## Brown 53 50 25 15
## Red 10 10 7 7
## Blond 3 30 5 8
plot(ca(tabla.hombres))
#TABLE WOMEN
tabla.mujeres
## Eye
## Hair Brown Blue Hazel Green
## Black 36 9 5 2
## Brown 66 34 29 14
## Red 16 7 7 7
## Blond 4 64 5 8
plot(ca(tabla.mujeres))
#TABLE GLOBAL
tabla.global
## Eye
## Hair Brown Blue Hazel Green
## Black 68 20 15 5
## Brown 119 84 54 29
## Red 26 17 14 14
## Blond 7 94 10 16
plot(ca(tabla.global))
Comments
Doing the correspondence analysis I can observe for each table: In Men, the most common are brown hair and hazel eyes. The most significant characteristic for hair color is blonde. You can see two large groups of features, to the left of the graph the dark ones and to the right the light ones.
In women, the most common and least representative are brown hair and the most representative characteristics are blue eyes; and blonde hair.
In both, the most common characteristic is brown hair and the least, blonde hair.