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library(hmmm)
data(relpolbirth)
y<-getnames(relpolbirth,st=12,sep=";")
names<-c("Rel","Pol","Birth")
# variable 1: Religion
# variable 2: Politics
# variable 3: Birthcontrol
#the lower the variable number is the faster the variable sub-script changes in the vectorized table
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# Various hierarchical loglinear models:
# see Table 2.4, pg. 32,
# "Marginal models for dependent, clustered and longitudinal categorical data",
# Bergsma, W., Croon, M. and Hagenaars, J.A.
# Springer, 2009.
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# (Pol,Rel) _||_ Birth
f<-~Rel*Pol+Birth
model1<-loglin.model(lev=c(3,7,4),formula=f,names=names)
# alternatively
# model1<-loglin.model(lev=c(3,7,4),int=list(c(1,2),c(3)),names=names)
mod1<-hmmm.mlfit(y,model1)
print(mod1)
# Pol _||_ Birth|Rel
f<-~Rel*Pol+Rel*Birth
model2<-loglin.model(lev=c(3,7,4),formula=f,names=names)
# alternatively
# model2<-loglin.model(lev=c(3,7,4),int=list(c(1,2),c(1,3)),names=names)
mod2<-hmmm.mlfit(y,model2)
print(mod2)
# Rel _||_ Birth|Pol
f<-~Rel*Pol+Pol*Birth
model3<-loglin.model(lev=c(3,7,4),formula=f,names=names)
# alternatively
# model3<-loglin.model(lev=c(3,7,4),int=list(c(1,2),c(2,3)),names=names)
mod3<-hmmm.mlfit(y,model3)
print(mod3)
# No 3-factor interaction
f<-~Rel*Pol+Rel*Birth+Pol*Birth
model4<-loglin.model(lev=c(3,7,4),formula=f,names=names)
# alternatively
# model4<-loglin.model(lev=c(3,7,4),int=list(c(1,2),c(1,3),c(2,3)),names=names)
mod4<-hmmm.mlfit(y,model4)
print(mod4)
# Rel _||_ Pol|Birth
f<-~Rel*Birth+Pol*Birth
model5<-loglin.model(lev=c(3,7,4),formula=f,names=names)
# alternatively
# model5<-loglin.model(lev=c(3,7,4),int=list(c(1,3),c(2,3)),names=names)
mod5<-hmmm.mlfit(y,model5)
print(mod5)
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