mlp: Projection of points into one dimension.

In ClusterSignificance: The ClusterSignificance package provides tools to assess if class clusters in dimensionality reduced data representations have a separation different from permuted data

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)

ClusterSignificance documentation built on Nov. 8, 2020, 5:28 p.m.