nmf is a S4 generic defines the main
interface to run NMF algorithms within the framework
defined in package NMF. It has many methods that
facilitates applying, developing and testing NMF
algorithms. The package vignette vignette('NMF') contains an
introduction to the interface, through a sample data
analysis.
nmf(x, rank, method, ...) ## S3 method for class 'matrix,numeric,NULL':
nmf(x, rank, method,
seed = NULL, model = NULL, ...)
## S3 method for class 'matrix,numeric,list':
nmf(x, rank, method, ...,
.parameters = list())
## S3 method for class 'matrix,numeric,function':
nmf(x, rank, method,
seed, model = "NMFstd", ..., name,
objective = "euclidean", mixed = FALSE)
## S3 method for class 'matrix,NMF,ANY':
nmf(x, rank, method, seed,
...)
## S3 method for class 'matrix,NULL,ANY':
nmf(x, rank, method, seed,
...)
## S3 method for class 'matrix,matrix,ANY':
nmf(x, rank, method, seed,
model = list(), ...)
## S3 method for class 'formula,ANY,ANY':
nmf(x, rank, method, ...,
model = NULL)
## S3 method for class 'matrix,numeric,NMFStrategy':
nmf(x, rank,
method, seed = nmf.getOption("default.seed"),
rng = NULL, nrun = if (length(rank) > 1) 30 else 1,
model = NULL, .options = list(),
.pbackend = nmf.getOption("pbackend"),
.callback = NULL, ...)
nmf,matrix,matrix,ANY.If
nmf methods are passed to the
function that effectively implements the algorithm that
fits an NMF model on xmethod, and be lists of named values
that are passed to the corresponding method.method [only used when
method is a function].method [only used when method is a
function]. It may be either 'euclidean' or 'KL' for
specify
method support
mixed-sign target matrices, i.e. that may contain
negative values [only used when method is a
function].The seeding method may be specified in the following way:
[object Object],[
It is used to instantiate the object that inherits from
class
rank is a numeric vector with
more than one element, in which case a default of 30 runs
per value of the rank are p It can be a list containing named options with
their values, or, in the case only boolean/integer
options need to be set, a character string that specifies
which options are turned on/o
foreach parallel backend to register and/or
use when running in parallel mode. See options p
and P in argument .options for how to
enable keep.all=FALSE
(default). It allows to pass a callback function that is
called after each run when performing multiple runs (i.e.
with nrun>1). This is useful for example if one is
also interested in savNMFfit nrun > 1
and method is not list, this method returns
an object of class NMFfitX nrun >
1 and method is a list, this method
returns an object of class NMFList The algorithms that benefit from this optimization are: 'brunet', 'lee', 'nsNMF' and 'offset'. However there still exists plain R versions for these methods, which implement the updates as standard matrix products. These are accessible by adding the prefix '.R#' to their name: '.R#brunet', '.R#lee', '.R#nsNMF' and '.R#offset'.
The seeding method to use in combination with the
algorithm can be passed to interface nmf through
argument seed. The seeding seeding methods
available in registry are listed by the function
nmfSeed (see list therein).
Detailed examples of how to specify the seeding method and its parameters can be found in the Examples section of this man page and in the package's vignette.
nmf function has multiple methods that compose
a very flexible interface allowing to: The workhorse method is
nmf,matrix,numeric,NMFStrategy, which is
eventually called by all other methods. The other methods
provides convenient ways of specifying the NMF
algorithm(s), the factorization rank, or the seed to be
used. Some allow to directly run NMF algorithms on
different types of objects, such as data.frame or
ExpressionSet objects.
Lee DD and Seung H (2001). "Algorithms for non-negative
matrix factorization." _Advances in neural information
processing systems_.
Wang G, Kossenkov AV and Ochs MF (2006). "LS-NMF: a
modified non-negative matrix factorization algorithm
utilizing uncertainty estimates." _BMC bioinformatics_,
*7*, pp. 175. ISSN 1471-2105,
Pascual-Montano A, Carazo JM, Kochi K, Lehmann D and Pascual-marqui RD (2006). "Nonsmooth nonnegative matrix factorization (nsNMF)." _IEEE Trans. Pattern Anal. Mach. Intell_, *28*, pp. 403-415.
Badea L (2008). "Extracting gene expression profiles
common to colon and pancreatic adenocarcinoma using
simultaneous nonnegative matrix factorization." _Pacific
Symposium on Biocomputing. Pacific Symposium on
Biocomputing_, *290*, pp. 267-78. ISSN 1793-5091,
Kim H and Park H (2007). "Sparse non-negative matrix
factorizations via alternating non-negativity-constrained
least squares for microarray data analysis."
_Bioinformatics (Oxford, England)_, *23*(12), pp.
1495-502. ISSN 1460-2059,
Van Benthem M and Keenan MR (2004). "Fast algorithm for
the solution of large-scale non-negativity-constrained
least squares problems." _Journal of Chemometrics_,
*18*(10), pp. 441-450. ISSN 0886-9383,
nmfAlgorithm# roxygen generated flag
options(R_CHECK_RUNNING_EXAMPLES_=TRUE)
# Only basic calls are presented in this manpage.
# Many more examples are provided in the demo file nmf.R
demo('nmf')
# random data
x <- rmatrix(20,10)
# run default algorithm with rank 2
res <- nmf(x, 2)
# specify the algorithm
res <- nmf(x, 2, 'lee')
# get verbose message on what is going on
res <- nmf(x, 2, .options='v')
# more messages
res <- nmf(x, 2, .options='v2')
# even more
res <- nmf(x, 2, .options='v3')
# and so on ...Run the code above in your browser using DataLab