nutrition {lsmeans} | R Documentation

## Nutrition data

### Description

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")

[Package lsmeans version 2.25 Index]