PNMFKL: Projective nonnegative matrix factorization based on...

In richardbeare/pNMF: Projective non-negative matrix factorization

Description

Projective nonnegative matrix factorization based on I-divergence (non-nomalized KL-divergence)

Usage

PNMFKL(
  X,
  nmfMod,
  tol = 1e-05,
  maxIter = 5000,
  verboseN = FALSE,
  zerotol = 1e-10
)

Arguments

X	Input data matrix
nmfMod	NMF model from the NMF package
tol	tolerance for stopping criteria
maxIter	Maximum number of iterations
verbose	Print status messages

Value

Fitted NMF model, as defined in NMF package.

Examples

library(NMF)
NMF::setNMFMethod("PNMFKL", pNMF::PNMFKL)
mkD <- function(NOISE=TRUE) {
  n <- 1000 # rows
  counts <- c(30, 10, 20, 10, 15, 15) # samples
  syntheticNMF(n=n, r=counts, offset = NULL, noise = NOISE,
               factors = FALSE, seed = 99)
}
k<-mkD()
estim <- nmf(k, 6, method="PNMFKL", nrun=1)

## Not run: 
V.random <- randomize(k)
estim.r2 <- nmf(k, 2:20, method="PNMFKL", nrun=30)
estim.r2.random <- nmf(V.random, 2:20,  method="PNMF", nrun=30)

## End(Not run)

richardbeare/pNMF documentation built on June 30, 2020, 6:33 p.m.