ClassifiedPoints-class {ClusterSignificance} | R Documentation

## Classification of the one dimensional points in a Pcp or Mlp object.

### Description

Classification based on ROC params (TN TP FP FN).

### Usage

    ## S4 method for signature 'ClassifiedPoints'
    getData(x, n = NULL)

    classify(x, ...)

    ## S4 method for signature 'Pcp'
    classify(x, ...)

    ## S4 method for signature 'Mlp'
    classify(x, ...)

    ## S4 method for signature 'ClassifiedPoints'
    initialize(.Object, ..., scores,
      scores.points = scores.points, scores.index = scores.index, ROC, AUC,
      class.color)

    ## S4 method for signature 'ClassifiedPoints,missing'
    plot(x, y, comparison = "all",
      class.color = NULL, ...)

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

### Arguments

`x` | Pcp or Mlp Object for the function classify otherwise it is a ClassifiedPoints object
`n` | data to extract from ClassifiedPoints (NULL gives all)
`...` | additional arguments to pass on
`.Object` | internal object
`scores` | final scores
`scores.points` | sorted points
`scores.index` | index of sorted points
`ROC` | parameters (TN, TP, FN and FP)
`AUC` | area under the curve
`class.color` | user assigned group coloring scheme
`y` | default plot param, which should be set to NULL
`comparison` | Specify a comparison i.e. ("grp1 vs grp2") and plot only that comparison.
`object` | ClassifiedPoints Object

### Details

Tests all possible discrimination lines and picks the one with highest score based on a score which is simply calculated by the formula (TP - FP) + (TN - FN).

The plot shows the distribution of scores for different discrimination lines. Each line is a separator that has a score for the separation of the two groups, and the height of the line marks the score for this separation.

### Value

The classify function returns an object of class ClassifiedPoints

### Author(s)

Jesper R. Gadin and Jason T. Serviss

### Examples

    #use demo data
    data(pcpMatrix)
    classes <- rownames(pcpMatrix)

    #run function
    prj <- pcp(pcpMatrix, classes)
    cl <- classify(prj)

    #getData accessor
    getData(cl)

    #getData accessor specific
    getData(cl, "scores")

    #plot result
    plot(cl)

[Package ClusterSignificance version 1.15.0 Index]