MLearn,formula,MSnSet,learnerSchema,numeric-method

MLearn,formula,MSnSet,learnerSchema,xvalSpec-method

MLearn,formula,MSnSet,clusteringSchema,missing-method

MSnSetMLean

MLearnMSnSet


  This method implements <code>MLInterfaces</code>'
  <code>MLean</code> method for instances of the class
  <code>"MSnSet"</code>. 


methods

MLearn-methods

This package implements pattern recognition techniques on
quantitiative mass spectrometry data to infer protein
sub-cellular localisation.

Laurent Gatto

pRoloc

A unifying bioinformatics framework for spatial proteomics

MLearn-methods function

<dl>
    <dt><code>signature(formula = "formula", data = "MSnSet", .method
	= "learnerSchema", trainInd = "numeric")</code></dt><dd> 
      The learning problem is stated with the <code>formula</code> and applies
      the <code>.method</code> schema on the <code>MSnSet</code> <code>data</code> input
      using the <code>trainInd</code> numeric indices as train data.
    </dd> <dt><code>signature(formula = "formula", data = "MSnSet", .method
	= "learnerSchema", trainInd = "xvalSpec")</code></dt><dd> 
      In this case, an instance of <code><a rd-options="" href="/link/xvalSpec?package=pRoloc&version=1.12.4" data-mini-rdoc="pRoloc::xvalSpec">xvalSpec</a></code> is used for
      cross-validation. 
    </dd> <dt><code>signature(formula = "formula", data = "MSnSet", .method
	= "clusteringSchema", trainInd = "missing")</code></dt><dd> 
      Hierarchical (<code>hclustI</code>), k-means (<code>kmeansI</code>) and
      partitioning around medoids (<code>pamI</code>) clustering algorithms
      using <code>MLInterface</code>'s <code>MLearn</code> interface.
    </dd> </dl> 

Methods

 The <code>MLearn</code> interface for machine learning

MLearn-methods: The `MLearn` interface for machine learning

Description

Arguments

Methods

See Also