sparseBC-package {sparseBC} | R Documentation |
Fit sparse biclustering and matrix-variate normal biclustering
Description
This package is called sparseBC, for "Sparse biclustering". It implements two methods:Sparse biclustering and matrix-variate normal biclustering. All are described in the paper "Sparse biclustering of tranposable data", by KM Tan and D Witten (2014), Journal of Computational and Graphical Statistics.
The main functions are as follows: (1) sparseBC (2) matrixBC
The first function, sparseBC, performs sparse biclustering. matrixBC performs matrix-variate normal biclustering. There are also cross-validation functions for tuning parameter that controls the sparsity level of the estimated mean matrix: sparseBC.BIC and matrixBC.BIC. Function that choose the number of biclusters K and R are also included for sparseBC, called sparseBC.choosekr.
Details
Package: | sparseBC |
Type: | Package |
Version: | 1.1 |
Date: | 2015-02-09 |
License: | GPL (>=2.0) |
LazyLoad: | yes |
The package includes the following functions:
sparseBC : | Perform sparse biclustering |
sparseBC.choosekr : | Cross-validation to select the number of row and column clusters |
sparseBC.BIC : | Select sparsity tuning parameter for sparseBC |
summary.sparseBC : | Display information for the object sparseBC |
image.sparseBC : | Image plot for the estimated bicluster mean matrix |
matrixBC : | Perform matrix-variate normal biclustering |
matrixBC.BIC : | Select sparsity tuning parameter for matrixBC |
Author(s)
Kean Ming Tan
Maintainer: Kean Ming Tan <keanming@u.washington.edu>
References
KM Tan and D Witten (2014) Sparse biclustering of transposable data. Journal of Computational and Graphical Statistics 23(4):985-1008.
See Also
Examples
# An example that violates the assumption of contiguous biclusters
# Create mean matrix and the data matrix
#set.seed(5)
#u<-c(10,9,8,7,6,5,4,3,rep(2,17),rep(0,75))
#v<-c(10,-10,8,-8,5,-5,rep(3,5),rep(-3,5),rep(0,34))
#u<-u/sqrt(sum(u^2))
#v<-v/sqrt(sum(v^2))
#d<-50
#mus<-d*tcrossprod(u,v)
#binaryX<-(mus!=0)*1
#X<-mus+matrix(rnorm(100*50),100,50)
#X<-X-mean(X)
# The number of biclusters are chosen automatically
# Commented out for short run-time
#KR<-sparseBC.choosekr(X,1:6,1:6,0,0.1,trace=TRUE)
#k<-KR$estimated_kr[1]
#r<-KR$estimated_kr[2]
# The value of lambda is chosen automatically
#lambda<-sparseBC.BIC(X,k,r,c(0,10,20,30,40,50))$lambda
# Perform sparse biclustering using the K, R, and lambda chosen
#biclustering<-sparseBC(X,k,r,lambda)
# Display some information on the object sparseBC
#summary(biclustering)
# Plot the estimated mean matrix from sparseBC
#image(biclustering)