extractPlot {fabia} | R Documentation

## Plotting of Biclustering Results

### Description

`extractPlot`: R implementation 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).

### Details

Essentially the model is the sum of outer products of vectors:

X = ∑_{i=1}^{p} λ_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 λ_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 λ_i and z_i .

The λ_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.

### Value

Returns corresponding plots

### Author(s)

Sepp Hochreiter

### 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)

    ## Not run:
    #---------------
    # 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
    }

    ## End(Not run)

[Package fabia version 2.38.0 Index]