# makeFabiaDataPos: Generation of Bicluster Data In fabia: FABIA: Factor Analysis for Bicluster Acquisition

## Description

makeFabiaDataPos: R implementation of makeFabiaDataPos.

## Usage

 1 2 makeFabiaDataPos(n,l,p,f1,f2,of1,of2,sd_noise,sd_z_noise, mean_z,sd_z,sd_l_noise,mean_l,sd_l) 

## Arguments

 n number of observations. l number of samples. p number of biclusters. f1 nn/f1 max. additional samples are active in a bicluster. f2 n/f2 max. additional observations that form a pattern in a bicluster. of1 minimal active samples in a bicluster. of2 minimal observations that form a pattern in a bicluster. sd_noise Gaussian zero mean noise std on data matrix. sd_z_noise Gaussian zero mean noise std for deactivated hidden factors. mean_z Gaussian mean for activated factors. sd_z Gaussian std for activated factors. sd_l_noise Gaussian zero mean noise std if no observation patterns are present. mean_l Gaussian mean for observation patterns. sd_l Gaussian std for observation patterns.

## Details

Essentially the data generation model is the sum of outer products of sparse 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

and noise free

Y = L Z

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, Y from R^{n \times l}.

Sequentially L_i are generated using n, f2, of2, sd_l_noise, mean_l, sd_l. of2 gives the minimal observations participating in a bicluster to which between 0 and n/f2 observations are added, where the number is uniformly chosen. sd_l_noise gives the noise of observations not participating in the bicluster. mean_l and sd_l determines the Gaussian from which the values are drawn for the observations that participate in the bicluster. "POS": The sign of the mean is fixed.

Sequentially Z_i are generated using l, f1, of1, sd_z_noise, mean_z, sd_z. of1 gives the minimal samples participating in a bicluster to which between 0 and l/f1 samples are added, where the number is uniformly chosen. sd_z_noise gives the noise of samples not participating in the bicluster. mean_z and sd_z determines the Gaussian from which the values are drawn for the samples that participate in the bicluster.

U is the overall Gaussian zero mean noise generated by sd_noise.

Implementation in R.

## Value

 X the noise data from R^{n \times l}. Y the noise free data from R^{n \times l}. ZC list where i-th element gives samples belonging to i-th bicluster. LC list where i-th element gives observations belonging to i-th bicluster.

## Author(s)

Sepp Hochreiter

fabia, fabias, fabiap, fabi, fabiasp, mfsc, nmfdiv, nmfeu, nmfsc, extractPlot, extractBic, plotBicluster, Factorization, projFuncPos, projFunc, estimateMode, makeFabiaData, makeFabiaDataBlocks, makeFabiaDataPos, makeFabiaDataBlocksPos, matrixImagePlot, fabiaDemo, fabiaVersion
  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 #--------------- # TEST #--------------- dat <- makeFabiaDataPos(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[] Y <- dat[] matrixImagePlot(Y) dev.new() matrixImagePlot(X) ## Not run: #--------------- # DEMO #--------------- dat <- makeFabiaDataPos(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[] Y <- dat[] matrixImagePlot(Y) dev.new() matrixImagePlot(X) ## End(Not run)