fabia (version 2.18.0)

extractPlot: Plotting of Biclustering Results

Description

extractPlot: Rimplementation of extractPlot.

Usage

extractPlot(fact,thresZ=0.5,ti="",thresL=NULL,Y=NULL,which=c(1,2,3,4,5,6))

Arguments

fact

object of the class Factorization.

thresZ

threshold for sample belonging to bicluster; default 0.5.

thresL

threshold for loading belonging to bicluster (estimated if not given).

ti

plot title; default "".

Y

noise free data matrix.

which

which plot is shown: 1=noise free data (if available), 2=data, 3=reconstructed data, 4=error, 5=absolute factors, 6=absolute loadings; default c(1,2,3,4,5,6).

concept

• biclustering
• sparse coding
• sparse matrix factorization
• non-negative matrix factorization

Details

Essentially the model is the sum of outer products of vectors: $$X = \sum_{i=1}^{p} \lambda_i z_i^T + U$$ where the number of summands $p$ is the number of biclusters. The matrix factorization is $$X = L Z + U$$

Here $\lambda_i$ are from $R^n$, $z_i$ from $R^l$, $L$ from $R^{n \times p}$, $Z$ from $R^{p \times l}$, and $X$, $U$ from $R^{n \times l}$. The hidden dimension $p$ is used for kmeans clustering of $\lambda_i$ and $z_i$.

The $\lambda_i$ and $z_i$ are used to extract the bicluster $i$, where a threshold determines which observations and which samples belong the the bicluster.

The method produces following plots depending what plots are chosen by the "which" variable:

Y: noise free data (if available), X: data, LZ: reconstructed data, LZ-X: error, abs(Z): absolute factors, abs(L): absolute loadings.

Implementation in R.

See Also

fabia, fabias, fabiap, fabi, fabiasp, spfabia, mfsc, nmfdiv, nmfeu, nmfsc, extractPlot, extractBic, plotBicluster, Factorization, projFuncPos, projFunc, estimateMode, makeFabiaData, makeFabiaDataBlocks, makeFabiaDataPos, makeFabiaDataBlocksPos, matrixImagePlot, fabiaDemo, fabiaVersion

Examples

#---------------
# TEST
#---------------

dat <- makeFabiaDataBlocks(n = 100,l= 50,p = 3,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
Y <- dat[[2]]


resEx <- fabia(X,3,0.1,20)

extractPlot(resEx,ti="FABIA",Y=Y)



#---------------
# DEMO1
#---------------

dat <- makeFabiaDataBlocks(n = 1000,l= 100,p = 10,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
Y <- dat[[2]]


resToy <- fabia(X,13,0.01,200)

extractPlot(resToy,ti="FABIA",Y=Y)

#---------------
# DEMO2
#---------------

avail <- require(fabiaData)

if (!avail) {
    message("")
    message("")
    message("#####################################################")
    message("Package 'fabiaData' is not available: please install.")
    message("#####################################################")
} else {

data(Breast_A)

X <- as.matrix(XBreast)

resBreast <- fabia(X,5,0.1,200)

extractPlot(resBreast,ti="FABIA Breast cancer(Veer)")

#sorting of predefined labels
CBreast}