NMFOffset-class {NMF} | R Documentation

## NMF Model - Nonnegative Matrix Factorization with Offset

### Description

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

### Usage

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

### Arguments

`offset` | optional numeric vector used to initialise slot ‘offset’.
`.Object` | An object: see the Details section.
`...` | data to include in the new object. Named arguments correspond to slots in the class definition. Unnamed arguments must be objects from classes that this class extends.

### Details

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.

### Methods

fitted

  `signature(object = "NMFOffset")`: Computes the target matrix estimate for an NMFOffset object.

  The estimate is computed as:

  W H + offset

offset

  `signature(object = "NMFOffset")`: The function `offset` returns the offset vector from an NMF model that has an offset, e.g. an `NMFOffset` model.

rnmf

  `signature(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

  `signature(object = "NMFOffset")`: Show method for objects of class `NMFOffset`

### Creating objects from the Class

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.

### Initialize 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.

### References

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, <URL: http://www.ncbi.nlm.nih.gov/pubmed/18229692>.

### See Also

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

### Examples

    # 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')

[Package NMF version 0.20.6 Index]