NMFOffset-class: NMF Model - Nonnegative Matrix Factorization with Offset
In renozao/NMF: Algorithms and Framework for Nonnegative Matrix Factorization (NMF)

Description Usage Arguments Details Creating objects from the Class Initialize method Methods (by generic) References See Also Examples

This class implements the Nonnegative Matrix Factorization with Offset model, required by the NMF with Offset algorithm.

## S4 method for signature 'NMFOffset'
show(object)

## S4 method for signature 'NMFOffset'
initialize(.Object, ..., offset)

## S4 method for signature 'NMFOffset'
offset(object)

## S4 method for signature 'NMFOffset'
fitted(object, W, H, offset = object@offset)

`object`	an instance of class `NMFOffset`.
`.Object`	an object of class `NMFOffset` that serves as a template to initialize the object to be created.
`...`	other arguments passed as object slot values.
`offset`	offset vector
`W`	a optional matrix to use in the computation as the basis matrix in place of `basis(object)`. It must be compatible with the coefficient matrix used in the computation. That is, number of columns in `W` = number of rows in the mixture coefficient matrix `coef(object)` or `H` (if provided).
`H`	a optional matrix to use in the computation as the coefficient matrix in place of `coef(object)`. It must be compatible with the basis matrix used in the computation. That is, number of rows in `H` = number of columns in the basis matrix `basis(object)` or `W` (if provided).

The NMF with Offset algorithm is defined by Badea (2008) as a modification of the euclidean based NMF algorithm from Lee2001 (see section Details and references below). It aims at obtaining 'cleaner' factor matrices, by the introduction of an offset matrix, explicitly modelling a feature specific baseline – constant across samples.

Object of class NMFOffset can be created using the standard way with operator new

However, as for all NMF model classes – that extend class NMF, objects of class NMFOffset should be created using factory method nmfModel :

new('NMFOffset')

nmfModel(model='NMFOffset')

nmfModel(model='NMFOffset', W=w, offset=rep(1, nrow(w)))

See nmfModel for more details on how to use the factory method.

The initialize method for NMFOffset objects tries to correct the initial value passed for slot offset, so that it is consistent with the dimensions of the NMF model: it will pad the offset vector with NA values to get the length equal to the number of rows in the basis matrix.

rnmf(x = NMFOffset,target = numeric): Generates a random NMF model with offset, from class NMFOffset.

The offset values are drawn from a uniform distribution between 0 and the maximum entry of the basis and coefficient matrices, which are drawn by the next suitable rnmf method, which is the workhorse method rnmf,NMF,numeric.
show(object = NMFOffset): Show method for objects of class NMFOffset
offset(object = NMFOffset): Offsets in NMF Models with Offset

The function offset returns the offset vector from an NMF model that has an offset, e.g. an NMFOffset model.
fitted(object = NMFOffset): Computes the target matrix estimate for an NMFOffset object.

The estimate is computed as:

W H + offset

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*, 267-78. ISSN 1793-5091, <URL: http://www.ncbi.nlm.nih.gov/pubmed/18229692>.

Other NMF-model: NMFns-class, NMFstd-class

 
# create a completely empty NMF object
new('NMFOffset')

# create a NMF object based on random (compatible) matrices
n <- 50; r <- 3; p <- 20
w <- rmatrix(n, r) 
h <- rmatrix(r, p)
nmfModel(model='NMFOffset', W=w, H=h, offset=rep(0.5, nrow(w)))

# apply Nonsmooth NMF algorithm to a random target matrix
V <- rmatrix(n, p)
## Not run: nmf(V, r, 'offset')

# random NMF model with offset  
rnmf(3, 10, 5, model='NMFOffset')