| 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 = "carData")
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