---
title: "demo-fusionclust"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{demo-fusionclust}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

This `vignette` provides a quick demo of the functionalities of the `fusionclust` package. 

## Example 1 - track the big merges and estimate the number of clusters

This example demonstrates how to estimate the number of clusters using `bmt` and `nclust`.

```{r}
# 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)
```

Now, estimate the number of clusters.

```{r}
# Estimate the number of clusters - k
k<- nclust(bmt_output)
k
```

## Example 2 - rank and screen the p features in a clustering problem 

This example demonstrates feature ranking and screening using `cosci_is` and `cosci_is_select`. 

```{r}
# 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)
```

Now, conduct feature ranking using `cosci_is`.

```{r 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")
```

Screen out the noise features using `cosci_is_select`.

```{r}
features<-cosci_is_select(scores,0.9)
features$selected
```

You can also get an implicit threshold value.

```{r}
imp.thresh<- min(scores[features$selected])
imp.thresh
```