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# MLwiN User Manual
#
# 6 Contextual Effects . . . . . . . . . . . . . . . . . . . . . . . . .79
#
# 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.
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# 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/
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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)
data(tutorial, package = "R2MLwiN")
(mymodel1 <- runMLwiN(normexam ~ 1 + standlrt + (1 + standlrt | school) + (1 | student), data = tutorial))
# 6.1 The impact of school gender on girls' achievement . . . . . . . . . 80
(mymodel2 <- runMLwiN(normexam ~ 1 + standlrt + sex + schgend + (1 + standlrt | school) + (1 | student), data = tutorial))
(mymodel3 <- runMLwiN(normexam ~ 1 + standlrt + sex + schgend + schgend:standlrt + (1 + standlrt | school) + (1 | student),
estoptions = list(startval = list(FP.b = mymodel2@FP, FP.v = mymodel2@FP.cov, RP.b = mymodel2@RP, RP.v = mymodel2@RP.cov)),
data = tutorial))
# 6.2 Contextual effects of school intake ability averages . . . . . . . .83
(mymodel4 <- runMLwiN(normexam ~ 1 + standlrt + sex + schgend + schav + (1 + standlrt | school) + (1 | student), data = tutorial))
(mymodel5 <- runMLwiN(normexam ~ 1 + standlrt + sex + schgend + schav + standlrt:schav + (1 + standlrt | school) +
(1 | student), data = tutorial))
pred <- predict(mymodel5, params = c("FP_schavhigh", "FP_standlrt:schavhigh"), se.fit = TRUE)
hilodiff <- pred$fit
hilodiff_se <- pred$se.fit
hilodiff_lo <- hilodiff - 1.96 * hilodiff_se
hilodiff_hi <- hilodiff + 1.96 * hilodiff_se
highdata <- as.data.frame(cbind(mymodel5@data$schavhigh, mymodel5@data[["standlrt:schavhigh"]], hilodiff, hilodiff_lo,
hilodiff_hi)[order(mymodel5@data[["standlrt:schavhigh"]]), ])
colnames(highdata) <- c("schavhigh", "standlrt:schavhigh", "hilodiff", "hilodiff_lo", "hilodiff_hi")
highdata <- highdata[highdata$schavhigh == 1, ]
plot(highdata[["standlrt:schavhigh"]], highdata$hilodiff, type = "l")
if (!require(lattice)) {
warning("package lattice required to run this example")
} else {
xyplot(hilodiff ~ `standlrt:schavhigh`, panel = function(x, y, subscripts) {
panel.xyplot(x, y, type = "l")
panel.xyplot(x, highdata$hilodiff_hi, type = "l", lty = 2)
panel.xyplot(x, highdata$hilodiff_lo, type = "l", lty = 2)
}, data = highdata)
}
# Chapter learning outcomes . . . . . . . . . . . . . . . . . . . . . 87
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