spectralNMF: Perform Non-Negative Matrix factorization on spectral data

Description Usage Arguments Value Author(s)

View source: R/spectralNMF.R

Description

Perform Non-Negative Matrix factorization on spectral data

Usage

1
2
spectralNMF(object, rank, method = "PGNMF", initSpectralData = NULL,
  nruns = 10, subsamplingFactor = 3, checkDivergence = TRUE)

Arguments

object

SpectraInTime-class

rank

number of NMF components to be found

method

name of the NMF method to be used. "PGNMF" (default), "HALSacc" and "semiNMF" are methods derived from the hNMF package. All methods from the NMF package are also available.

initSpectralData

this can be a list of spectralData objects, containing the pure component spectra. It can also be either of the NMF factor matrices with initial values

nruns

number of NMF runs. It is recommended to run the NMF analyses multiple times when random seeding is used, to avoid a suboptimal solution

subsamplingFactor

subsampling factor used during NMF analysis

checkDivergence

Boolean indicating whether divergence checking should be performed

Value

Scaled NMF model (in accordance with the NMF package definition)

Author(s)

Nicolas Sauwen


spectralAnalysis documentation built on June 12, 2018, 5:04 p.m.