This observational dataset involves three factors, but where several factor combinations are missing.
It is used as a case study in Milliken and Johnson, Chapter 17, p.202. (You may also find it in the second edition, p.278.)
Usage
nutrition
Format
A data frame with 107 observations on the following 4 variables.
age
a factor with levels 1, 2, 3, 4. Mother's age group.
group
a factor with levels FoodStamps, NoAid. Whether or not the family receives food stamp assistance.
race
a factor with levels Black, Hispanic, White. Mother's race.
gain
a numeric vector (the response variable). Gain score (posttest minus pretest) on knowledge of nutrition.
Details
A survey was conducted by home economists “to study how much lower-socioeconomic-level mothers knew about nutrition and to judge the effect of a training program designed to increase therir knowledge of nutrition.” This is a messy dataset with several empty cells.
Source
Milliken, G. A. and Johnson, D. E. (1984)
Analysis of Messy Data – Volume I: Designed Experiments. Van Nostrand, ISBN 0-534-02713-7.
Examples
require(lsmeans)
nutr.aov <- aov(gain ~ (group + age + race)^2, data = nutrition)
# Summarize predictions for age group 3
nutr.lsm <- lsmeans(nutr.aov, ~ race * group,
at = list(age="3"))
lsmip(nutr.lsm, race ~ group)
# Hispanics seem exceptional; but, this doesn't test out due to very sparse data
cld(nutr.lsm, by = "group")
cld(nutr.lsm, by = "race")