Mlp-class {ClusterSignificance} | R Documentation

## Projection of points into one dimension.

### Description

Project points onto the mean based line.

### Usage

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

    ## S4 method for signature 'Mlp'
    initialize(.Object, ..., classes, points.orig, line,
      points.onedim, class.color)

    ## S4 method for signature 'Mlp,missing'
    plot(x, y, steps = "all", ...)

    mlp(mat, ...)

    ## S4 method for signature 'matrix'
    mlp(mat, classes, class.color = NULL, ...)

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

### Arguments

`x` | matrix object for the function mlp otherwise it is a Mlp object
`n` | data to extract from Mlp (NULL gives all)
`.Object` | internal object
`...` | additional arguments to pass on
`classes` | vector in same order as rows in matrix
`points.orig` | multidimensional points describing the original data
`line` | multidimensional points describing a line
`points.onedim` | a vector of points
`class.color` | user assigned group coloring scheme
`y` | default plot param, which should be set to NULL(default: NULL)
`steps` | 1,2,3,4,5,6 or "all"
`mat` | matrix with samples on rows, PCs in columns. Ordered PCs, with PC1 to the left.
`object` | Mlp object

### Details

Projection of the points onto a line between the mean of two groups. Mlp is the abbreviation for 'mean line projection'. The function accepts, at the moment, only two groups and two PCs at a time.

An object containing results from a mean line projection reduction to one dimension.

The group and the one dimensional points are the most important information to carry out a classification using the classify() function. As a help to illustrate the details of the dimension reduction, the information from some critical steps are stored in the object. To visually explore these there is a dedicated plot method for Mlp objects, use plot().

### Value

The mlp function returns an object of class Mlp

### Author(s)

Jesper R. Gadin and Jason T. Serviss

### Examples

    #use demo data
    data(mlpMatrix)
    groups <- rownames(mlpMatrix)

    #run function
    prj <- mlp(mlpMatrix, groups)

    #getData accessor
    getData(prj)

    #getData accessor specific
    getData(prj, "line")

    #plot result
    plot(prj)

[Package ClusterSignificance version 1.20.0 Index]