Version info: Code for this page was tested in R Under development (unstable) (2012-07-05 r59734)
On: 2012-08-08
With: knitr 0.6.3
This code fragment covers a split-plot factorial design with one between subject factor and two within subject factors use the lmer command using a Stata dataset. In this dataset y is the response variable, a is the between subject factor, b and c are within subject factors, and s is the subject identifier. We also view a histogram for each plot to get a bit of a sense of the data.
library(foreign) # to read the Stata file library(lattice) # for the graphics library(lme4) # to fit the model
## Loading required package: methods
## Loading required package: Matrix
## Attaching package: 'lme4'
## The following object(s) are masked from 'package:stats': ## ## AIC, BIC
spf <- read.dta("https://stats.idre.ucla.edu/stat/data/spf2-22.dta") histogram(~y | interaction(a, b, c), data = spf)
m1 <- lmer(y ~ factor(a) * factor(b) * factor(c) + (1 | s), data = spf) anova(m1)
## Analysis of Variance Table ## Df Sum Sq Mean Sq F value ## factor(a) 1 1.0 1.0 2.00 ## factor(b) 1 162.0 162.0 319.56 ## factor(c) 1 24.5 24.5 48.33 ## factor(a):factor(b) 1 6.1 6.1 12.08 ## factor(a):factor(c) 1 10.1 10.1 19.97 ## factor(b):factor(c) 1 8.0 8.0 15.78 ## factor(a):factor(b):factor(c) 1 3.1 3.1 6.16