## -----------------------------------------------------------------------------
library(ClustVarLV)

## -----------------------------------------------------------------------------
data(apples_sh)
# 43 sensory attributes of 12 varieties of apple from southern hemisphere
senso<-apples_sh$senso
# Scores of liking given fy 60 consumers for each of the 12 varieties of apple
pref<-apples_sh$pref

## ---- results="hide"----------------------------------------------------------
resclv_senso <- CLV(X = senso, method = "directional", sX =TRUE)
# option sX=TRUE means that each attribute will be auto-scaled (standard deviation =1)

## ---- fig.width=3, fig.height=3-----------------------------------------------
# Dendrogram of the CLV hierarchical clustering algorithm :
plot(resclv_senso,"dendrogram")
# Graph of the variation of the clustering criterion
plot(resclv_senso,"delta")

## -----------------------------------------------------------------------------
# Summary the CLV results for a partition into 4 groups
summary(resclv_senso,K=4)

## ---- fig.width=4, fig.height=4-----------------------------------------------
# Representation of the group membership for a partition into 4 groups
plot_var(resclv_senso,K=4,label=T,cex.lab=0.8)

## ---- fig.width=6, fig.height=6-----------------------------------------------
plot_var(resclv_senso,K=4,beside=T)

## ---- results="hide"----------------------------------------------------------
# Extract the group membership of each variable
get_partition(resclv_senso,K=4,type="vector")
# or 
get_partition(resclv_senso,K=4,type="matrix")

# Extract the group latent variables 
get_comp(resclv_senso,K=4)

## ---- results="hide"----------------------------------------------------------
res.segext<- CLV(X = pref, Xr = senso, method = "local", sX=TRUE, sXr = TRUE)

print(res.segext)

## ---- fig.width=3, fig.height=3-----------------------------------------------
plot(res.segext,"dendrogram")
plot(res.segext,"delta") 

## -----------------------------------------------------------------------------
table(get_partition(res.segext,K=2),get_partition(res.segext,K=3))

## -----------------------------------------------------------------------------
get_loading(res.segext,K=3)

## -----------------------------------------------------------------------------
res.clvkm.rd<-CLV_kmeans(X = pref, Xr = senso, method = "local", sX=TRUE,
                         sXr = TRUE, clust=3, nstart=100)

## -----------------------------------------------------------------------------
res.clvkm.hc<-CLV_kmeans(X = pref, Xr = senso, method = "local", sX=TRUE,
                        sXr = TRUE, clust=res.segext[[3]]$clusters[1,])

## -----------------------------------------------------------------------------
table(get_partition(res.segext,K=3),get_partition(res.clvkm.hc,K=3)) 

## -----------------------------------------------------------------------------
table(get_partition(res.segext,K=3),get_partition(res.clvkm.rd,K=3)) 

## -----------------------------------------------------------------------------
clvkm_senso_kpone<-CLV_kmeans(X = senso, method = "directional",sX=TRUE, clust=4, strategy="kplusone",rho=0.5)
get_partition(clvkm_senso_kpone,type="matrix")

## -----------------------------------------------------------------------------
sizG0<-NULL
for (r in seq(0,1,0.1)) {
  res<-CLV_kmeans(X = pref, method = "local", sX=TRUE, clust=3, nstart=20, strategy="kplusone",rho=r)
  sizG0<-c(sizG0,sum(get_partition(res)==0))
}
plot(seq(0,1,0.1),sizG0,type="b",xlab="rho",ylab="# var in noise cluster")

## -----------------------------------------------------------------------------
plot_var(CLV_kmeans(X = pref, method = "local", sX=TRUE, clust=3, nstart=20, strategy="kplusone",rho=0.4))