cosci_is {fusionclust}

Rank the p features in an n by p design matrix

Description

Ranks the p features in an n by p design matrix where n represents the sample size and p is the number of features.

Usage

cosci_is(dat, min.alpha, small.perturbation = 10^(-6))

Arguments

dat

n by p data matrix

min.alpha

the smallest threshold (typically set to 0)

small.perturbation

a small positive number to remove ties. Default value is 10^(-6)

Details

Uses the univariate merging algorithm bmt and produces a score for each feature that reflects its relative importance for clustering.

Value

a p vector of scores

References

1. Banerjee, T., Mukherjee, G. and Radchenko P., Feature Screening in Large Scale Cluster Analysis, Journal of Multivariate Analysis, Volume 161, 2017, Pages 191-212

2. P. Radchenko, G. Mukherjee, Convex clustering via l1 fusion penalization, J. Roy. Statist, Soc. Ser. B (Statistical Methodology) (2017) doi:10.1111/rssb.12226.

See Also

bmt,cosci_is_select

Examples

library(fusionclust)
set.seed(42)
noise<-matrix(rnorm(49000),nrow=1000,ncol=49)
set.seed(42)
signal<-c(rnorm(500,-1.5,1),rnorm(500,1.5,1))
x<-cbind(signal,noise)
scores<- cosci_is(x,0)