## ------------------------------------------------------------------------
# Generate bimodal data
set.seed(42)
x<- c(rnorm(1000,-2,1), rnorm(1000,2,1))

# Run Big Merge Tracker on x
library("fusionclust")
bmt_output<-bmt(x)

## ------------------------------------------------------------------------
# Estimate the number of clusters - k
k<- nclust(bmt_output)
k

## ------------------------------------------------------------------------
# Generate n by p=50 design matrix with 2 signals and 48 noise features
n<-1000
features<-1:50
set.seed(42)
noise<- matrix(rnorm(48000,0,1),nrow=1000,ncol=48)

# signal 1 ~ mixture of Normals
set.seed(42)
s1<- c(rnorm(n/2,-1.5,1),rnorm(n/2,1.5,1))

# signal 2 ~ mixture of Log Normal and Normal
set.seed(42)
s2<- c(rlnorm(n/2,0.2,0.35),rnorm(n/2,4,0.5))

# put it all together
x<- cbind(s1,s2,noise)

## ----fig1, fig.height = 4, fig.width = 6, fig.align = "center"-----------
library("fusionclust")
scores<- cosci_is(x,0)

# plot the scores and see which features have higher scores
plot(features,scores,type="p",col="red")

## ------------------------------------------------------------------------
features<-cosci_is_select(scores,0.9)
features$selected

## ------------------------------------------------------------------------
imp.thresh<- min(scores[features$selected])
imp.thresh