Last chance! 50% off unlimited learning
Sale ends in
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.)
nutrition
A data frame with 107 observations and 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.
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 their knowledge of nutrition.'' This is a messy dataset with several empty cells.
# NOT RUN {
nutr.aov <- aov(gain ~ (group + age + race)^2, data = nutrition)
# Summarize predictions for age group 3
nutr.emmGrid <- emmeans(nutr.aov, ~ race * group, at = list(age="3"))
emmip(nutr.emmGrid, race ~ group)
# Hispanics seem exceptional; but this doesn't test out due to very sparse data
cld(nutr.emmGrid, by = "group")
cld(nutr.emmGrid, by = "race")
# }
Run the code above in your browser using DataLab