## Estimation of the models
m2 <- hlme(Y~Time*X1,mixture=~Time,random=~Time,classmb=~X2+X3,subject='ID',ng=2,
data=data_hlme,B=c(0.11,-0.74,-0.07,20.71,29.39,-1,0.13,2.45,-0.29,4.5,0.36,0.79,0.97))
m3 <- hlme(fixed = Y ~ Time * X1, mixture = ~Time, random = ~Time,subject = "ID",
classmb = ~X2 + X3, ng = 3, data = data_hlme,B=c(-0.21, 0.31, -2.11, -0.81, -0.24,
-0.18, 25.4, 20.09, 30.18, -0.43, -1.1, 0.25, 2.37, -0.29, 2.34, 0.03, 0.74, 0.97))
## Compare the classifications
xclass(m2,m3)
# The 39 subjects in class 2 of m3 come from class 1 of m2.
# In the same way, all the subjects in class 3 come from class 2 of m2.
# Class 1 of m3 mixes subject from class 1 and class 2 of m2.
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