# Load the library foreign
library(foreign)

# Read in the tutorial Stata data set 
tutorial = read.dta("http://www.bristol.ac.uk/cmm/media/runmlwin/tutorial.dta")

# Generate a boy dummy variable
tutorial$boy <- 1 - tutorial$girl

# Generate the passexam binary response variable
tutorial$passexam <- tutorial$normexam>0

# Load the library R2MLwiN
library(R2MLwiN)

# Specify the MLwiN directory
mlwin ="C:/Program Files (x86)/MLwiN v2.26/"

# Declare the levels in the model hierarchy 
levID = c('school','student')

# Specify the continuous response model
formula = "normexam ~ (0 | cons + standlrt + girl) + (2 | cons + standlrt) + (1 | girl + boy)"

# Set the level-1 covariance between the boy and girl residual errors to zero
smat = c(2,1)

# Specify estimation by IGLS
estoptions = list(EstM=0)

# Fit the model
mymodel = runMLwiN(formula, levID, D="Normal", tutorial, estoptions, MLwiNPath=mlwin, workdir = tempdir())







# Specify the binary response model
formula = "logit(passexam, cons) ~ (0 | cons + standlrt + girl) + (2 | cons + standlrt)"

# Specify estiamtion by MCMC
estoptions = list(EstM=1)

# Fit the model
mymodel = runMLwiN(formula, levID, D="Binomial", tutorial, estoptions, MLwiNPath=mlwin, workdir = tempdir())