| Title: | Clustering and Feature Screening using L1 Fusion Penalty |
| Version: | 1.0.0 |
| Description: | Provides the Big Merge Tracker and COSCI algorithms for convex clustering and feature screening using L1 fusion penalty. See Radchenko, P. and Mukherjee, G. (2017) <doi:10.1111/rssb.12226> and T.Banerjee et al. (2017) <doi:10.1016/j.jmva.2017.08.001> for more details. |
| Depends: | R (≥ 3.4.1) |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | bbmle, stats, graphics |
| RoxygenNote: | 6.0.1 |
| URL: | https://github.com/trambakbanerjee/fusionclust |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2017-09-19 01:43:57 UTC; Jolly |
| Author: | Trambak Banerjee [aut, cre], Gourab Mukherjee [aut], Peter Radchenko [aut] |
| Maintainer: | Trambak Banerjee <trambakb@usc.edu> |
| Repository: | CRAN |
| Date/Publication: | 2017-09-19 08:21:58 UTC |
Big Merge Tracker
Description
Solves an L1 relaxed univariate clustering criterion and returns a
sequence of \lambda values where the clusters merge
Usage
bmt(x, alpha = 0.1, small.perturbation = 10^(-6))
Arguments
x |
observation vector |
alpha |
merging threshold. Default is 0.1 |
small.perturbation |
a small positive number to remove ties. Default is 10^(-6) |
Details
solves a convex relaxation of the univariate clustering criterion given by equation
(2) in the referenced paper and generates a sequence of cluster merges and corresponding
\lambda values. See algorithm 1 in the referenced paper for more details.
Value
path - number of clusters on the big merge path
lambda.path - sequence of lambda where clusters merge
index - cluster index at the point where clusters merge
merge - merge points
split - split points
prob - merging proportion
boundaries - cluster boundaries
References
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
Examples
library(fusionclust)
set.seed(42)
x<- c(rnorm(1000,-2,1), rnorm(1000,2,1))
out<- bmt(x)
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
Banerjee, T., Mukherjee, G. and Radchenko P., Feature Screening in Large Scale Cluster Analysis, Journal of Multivariate Analysis, Volume 161, 2017, Pages 191-212
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
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)
Use a data driven approach to select the features
Description
Once you have the feature scores from cosci_is, you can select the features
based on a pre-defined threshold,
using table A.10 in the paper[1] to determine an appropriate threshold or,
using a data driven approach described in the references to select the features and obtain an implicit threshold value.
cosci_is_select implements option 3.
Usage
cosci_is_select(score, gamma)
Arguments
score |
a p vector of scores |
gamma |
what proportion of the p features is noise? If your sample size n is smaller than 100, setting gamma = 0.85 is recommended. Otherwise set gamma = 0.9 |
Details
Converts the problem of screening out features with lower scores into a problem in large scale multiple testing and uses the procedure described in reference [2] to determine the signal features.
Value
a vector of selected features
References
Banerjee, T., Mukherjee, G. and Radchenko P., Feature Screening in Large Scale Cluster Analysis, Journal of Multivariate Analysis, Volume 161, 2017, Pages 191-212
T. Cai, W. Sun, W., Optimal screening and discovery of sparse signals with applications to multistage high throughput studies, J. Roy.Statist. Soc. Ser. B (Statistical Methodology) 79, no. 1 (2017) 197-223
See Also
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)
features<-cosci_is_select(scores,0.9)
No.of clusters
Description
Estimates the number of clusters from the bmt run
Usage
nclust(bmt_output, prob_threshold = 0.5)
Arguments
bmt_output |
output from the |
prob_threshold |
probability threshold. Default is 0.5. Do not change it unless you know what you are doing. See the referenced paper |
Details
Estimates the number of clusters as the number of big merges + 1. The probability threshold is an adjustment that renders this estimation process more robust to sampling fluctuations. If the sum of the sample frequencies for the two merging clusters in the last big merge is less than 50 percent, we do not report any merges and thus are left with just 1 cluster. See the referenced paper for more details.
Value
The number of clusters
References
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
Examples
library(fusionclust)
set.seed(42)
x<- c(rnorm(1000,-2,1), rnorm(1000,2,1))
out<- bmt(x)
k<- nclust(out)