consensus,NMFfitX1-method

an object with a suitable
 <code><a rd-options="" href="/link/predict?package=NMF&version=0.24.0" data-mini-rdoc="NMF::predict">predict</a></code> method.

object

a logical that indicates if attributes
 containing information about the NMF model should be
 attached to the result (<code>TRUE</code>) or not
 (<code>FALSE</code>).

no.attrib

The result is the matrix stored in slot
 &#8216;consensus&#8217;. This method returns <code>NULL</code> if
 the consensus matrix is empty.

methods

Provides a framework to perform Non-negative Matrix
Factorization (NMF). The package implements a set of already published algorithms
and seeding methods, and provides a framework to test, develop and plug
new/custom algorithms. Most of the built-in algorithms have been optimized
in C++, and the main interface function provides an easy way of performing
parallel computations on multicore machines.

Nicolas Sauwen

Algorithms and Framework for Nonnegative Matrix Factorization
(NMF)

consensus,NMFfitX1-method function

<dl> <dt>object</dt>
<dd>an object with a suitable
 <code><a href="/link/predict?package=NMF&version=0.24.0" data-mini-rdoc="NMF::predict">predict</a></code> method.</dd>
 <dt>no.attrib</dt>
<dd>a logical that indicates if attributes
 containing information about the NMF model should be
 attached to the result (<code>TRUE</code>) or not
 (<code>FALSE</code>).</dd></dl>

Arguments

Returns the consensus matrix computed while performing all NMF runs,
amongst which object was selected as the best fit. — consensus,NMFfitX1-method

<dl>

 <dt>object</dt>
<dd>an object with a suitable
 <code><a href='https://rdrr.io/r/stats/predict.html'>predict</a></code> method.</dd>


 <dt>no.attrib</dt>
<dd>a logical that indicates if attributes
 containing information about the NMF model should be
 attached to the result (<code>TRUE</code>) or not
 (<code>FALSE</code>).</dd>

</dl>

consensus,NMFfitX1-method: Returns the consensus matrix computed while performing all NMF runs, amongst which `object` was selected as the best fit.

Description

Usage

Arguments