qkMDS-class {qkerntool}

qKernel Metric Multi-Dimensional Scaling

Description

The qkernel Metric Multi-Dimensional Scaling class

Objects of class "qkMDS"

Objects can be created by calls of the form new("qkMDS", ...). or by calling the qkMDS function.

Slots

prj:

Object of class "matrix" containing the Nxdim matrix (N samples, dim features) with the reduced input data (list of several matrices if more than one dimension specified)

dims:

Object of class "numeric" containing the dimension of the target space (default 2)

connum:

Object of class "numeric" containing the number of connected components in graph

Residuals:

Object of class "vector" containing the residual variances for all dimensions

eVal:

Object of class "vector" containing the corresponding eigenvalues

eVec:

Object of class "vector" containing the corresponding eigenvectors

Methods

prj

signature(object = "qkMDS"): returns the Nxdim matrix (N samples, dim features)

dims

signature(object = "qkMDS"): returns the dimension

Residuals

signature(object = "qkMDS"): returns the residual variances

eVal

signature(object = "qkMDS"): returns the eigenvalues

eVec

signature(object = "qkMDS"): returns the eigenvectors

xmatrix

signature(object = "qkMDS"): returns the used data matrix

kcall

signature(object = "qkMDS"): returns the performed call

cndkernf

signature(object = "qkMDS"): returns the used kernel function

Author(s)

Yusen Zhang
[e-mail]

See Also

qkernel-class, cndkernel-class, qkMDS

Examples

   # another example using the iris
  data(iris)
  testset <- sample(1:150,20)
  train <- as.matrix(iris[-testset,-5])
  labeltrain<- as.integer(iris[-testset,5])
  test <- as.matrix(iris[testset,-5])
  # ratibase(c=1,q=0.8)
  d_low = qkMDS(train, kernel = "ratibase", qpar = list(c=1,q=0.8),
                    dims=2, plotResiduals = TRUE)
  #plot the data projection on the components
  plot(prj(d_low),col=labeltrain, xlab="1st Principal Component",ylab="2nd  Principal Component")

  prj(d_low)
	dims(d_low)
	Residuals(d_low)
	eVal(d_low)
	eVec(d_low)
	kcall(d_low)
	cndkernf(d_low)