sparseBC (version 1.2)

sparseBC-package: 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

References

KM Tan and D Witten (2014) Sparse biclustering of transposable data. Journal of Computational and Graphical Statistics 23(4):985-1008.

See Also

sparseBC matrixBC

Examples

# NOT RUN {
# 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)
# }