Nothing
############################################################################
# MLwiN User Manual
#
# 2 Introduction to Multilevel Modelling . . . . . . . . . . . . . . . . 9
#
# Rasbash, J., Steele, F., Browne, W. J. and Goldstein, H. (2012).
# A User's Guide to MLwiN, v2.26. Centre for Multilevel Modelling,
# University of Bristol.
############################################################################
# R script to replicate all analyses using R2MLwiN
#
# Zhang, Z., Charlton, C., Parker, R, Leckie, G., and Browne, W.J.
# Centre for Multilevel Modelling, 2012
# http://www.bristol.ac.uk/cmm/software/R2MLwiN/
############################################################################
library(R2MLwiN)
# MLwiN folder
mlwin <- getOption("MLwiN_path")
while (!file.access(mlwin, mode = 1) == 0) {
cat("Please specify the root MLwiN folder or the full path to the MLwiN executable:\n")
mlwin <- scan(what = character(0), sep = "\n")
mlwin <- gsub("\\", "/", mlwin, fixed = TRUE)
}
options(MLwiN_path = mlwin)
# 2.1 The tutorial data set . . . . . . . . . . . . . . . . . . . . . . . .9
# 2.2 Opening the worksheet and looking at the data . . . . . . . . . . . 10
data(tutorial, package = "R2MLwiN")
summary(tutorial)
head(tutorial)
# 2.3 Comparing two groups . . . . . . . . . . . . . . . . . . . . . . . .13
tab <- cbind(tapply(tutorial$normexam, tutorial$sex, length), tapply(tutorial$normexam, tutorial$sex, mean), tapply(tutorial$normexam,
tutorial$sex, sd))
tab <- rbind(tab, c(length(tutorial$normexam), mean(tutorial$normexam), sd(tutorial$normexam)))
colnames(tab) <- c("N", "Mean", "SD")
rownames(tab)[3] <- "Total"
tab
t.test(normexam ~ sex, data = tutorial, var.equal = TRUE)
(mymodel1 <- runMLwiN(normexam ~ 1 + sex + (1 | student), data = tutorial))
# 2.4 Comparing more than two groups: Fixed effects models . . . . . . . .20
mean_normexam <- aggregate(normexam ~ school, mean, data = tutorial)$normexam
hist(mean_normexam)
mymodel2 <- runMLwiN(normexam ~ 1 + (1 | student), data = tutorial)
tutorial$school <- relevel(tutorial$school, 65)
(mymodel3 <- runMLwiN(normexam ~ 1 + school + (1 | student), data = tutorial))
aov(normexam ~ school, data = tutorial)
if (!require(lmtest)) {
warning("lmtest package required to use lrtest() function")
} else {
lrtest(mymodel2, mymodel3)
}
(mymodel4 <- runMLwiN(normexam ~ 1 + school + schgend + (1 | student), data = tutorial))
# 2.5 Comparing means: Random effects or multilevel model . . . . . . . .28
tutorial$school <- as.numeric(levels(tutorial$school))[tutorial$school]
(mymodel5 <- runMLwiN(normexam ~ 1 + (1 | school) + (1 | student), data = tutorial))
(mymodel6 <- runMLwiN(normexam ~ 1 + schgend + (1 | school) + (1 | student), data = tutorial))
# Chapter learning outcomes . . . . . . . . . . . . . . . . . . . . . 35
############################################################################
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.