R: O'Brien Kaiser's Repeated-Measures Dataset with Covariate

obk.long {afex} | R Documentation

## O'Brien Kaiser's Repeated-Measures Dataset with Covariate

### Description

This is the long version of the `OBrienKaiser` dataset from the car pakage adding a random covariate `age`. Originally the dataset ist taken from O'Brien and Kaiser (1985). The description from `OBrienKaiser` says: "These contrived repeated-measures data are taken from O'Brien and Kaiser (1985). The data are from an imaginary study in which 16 female and male subjects, who are divided into three treatments, are measured at a pretest, postest, and a follow-up session; during each session, they are measured at five occasions at intervals of one hour. The design, therefore, has two between-subject and two within-subject factors."

### Usage

    obk.long

### Format

A data frame with 240 rows and 7 variables.

### Source

O'Brien, R. G., & Kaiser, M. K. (1985). MANOVA method for analyzing repeated measures designs: An extensive primer. Psychological Bulletin, 97, 316-333. doi:10.1037/0033-2909.97.2.316

### Examples

    # The dataset is constructed as follows:
    data("OBrienKaiser", package = "car")
    set.seed(1)
    OBrienKaiser2 <- within(OBrienKaiser, {
    		id <- factor(1:nrow(OBrienKaiser))
    		age <- scale(sample(18:35, nrow(OBrienKaiser), replace = TRUE), scale = FALSE)})
    attributes(OBrienKaiser2$age) <- NULL # needed or resahpe2::melt throws an error.
    OBrienKaiser2$age <- as.numeric(OBrienKaiser2$age)
    obk.long <- reshape2::melt(OBrienKaiser2, id.vars = c("id", "treatment", "gender", "age"))
    obk.long[,c("phase", "hour")] <- lapply(as.data.frame(do.call(rbind,
            strsplit(as.character(obk.long$variable), "\\."),)), factor)
    obk.long <- obk.long[,c("id", "treatment", "gender", "age", "phase", "hour", "value")]
    obk.long <- obk.long[order(obk.long$id),]
    rownames(obk.long) <- NULL
    str(obk.long)
    ## 'data.frame':   240 obs. of  7 variables:
    ##  $ id       : Factor w/ 16 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
    ##  $ treatment: Factor w/ 3 levels "control","A",..: 1 1 1 1 1 1 1 1 1 1 ...
    ##  $ gender   : Factor w/ 2 levels "F","M": 2 2 2 2 2 2 2 2 2 2 ...
    ##  $ age      : num  -4.75 -4.75 -4.75 -4.75 -4.75 -4.75 -4.75 -4.75 -4.75 -4.75 ...
    ##  $ phase    : Factor w/ 3 levels "fup","post","pre": 3 3 3 3 3 2 2 2 2 2 ...
    ##  $ hour     : Factor w/ 5 levels "1","2","3","4",..: 1 2 3 4 5 1 2 3 4 5 ...
    ##  $ value    : num  1 2 4 2 1 3 2 5 3 2 ...
    head(obk.long)
    ##    id treatment gender   age phase hour value
    ## 1  1   control      M -4.75   pre    1     1
    ## 2  1   control      M -4.75   pre    2     2
    ## 3  1   control      M -4.75   pre    3     4
    ## 4  1   control      M -4.75   pre    4     2
    ## 5  1   control      M -4.75   pre    5     1
    ## 6  1   control      M -4.75  post    1     3

[Package afex version 0.17-8 Index]