This vignette
provides a quick demo of the functionalities of the fusionclust
package.
This example demonstrates how to estimate the number of clusters using bmt
and nclust
.
# 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.
# Estimate the number of clusters - k k<- nclust(bmt_output) k
This example demonstrates feature ranking and screening using cosci_is
and cosci_is_select
.
# 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
.
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
.
features<-cosci_is_select(scores,0.9) features$selected
You can also get an implicit threshold value.
imp.thresh<- min(scores[features$selected]) imp.thresh
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