MdsDiss: Mds of a dissimilarity matrix

In FreeSortR: Free Sorting Data Analysis

Description

Computes the multidimensional scaling of a matrix of dissimilarities between stimuli. Mds is based on smacof algorithm. The Mds configuration is rotated in order to get orthogonal dimensions sorted by decreasing variance.

Usage

MdsDiss(MatDissimil, ndim = 2, metric = TRUE, ties = "primary",
              itmax = 5000, eps = 1e-06)

Arguments

MatDissimil	A matrix of dissimilarities
ndim	Dimension of the Mds
metric	Metric or not metric Mds
ties	Treatment of ties in case of non metric Mds
itmax	Maximum number of iterations
eps	Epsilon for Mds computation

Value

List of the following components :

Config	Mds configuration of the stimuli
Percent	Percentage of inertia of the dimensions of Mds
Stress	Stress of the Mds solution

Examples

  data(AromaSort)
  Aroma<-SortingPartition(AromaSort)
  ListDissimil<-Dissimil(Aroma)
  MatDissim<-apply(simplify2array(ListDissimil),c(1,2),'sum')
  Mdsres<-MdsDiss(MatDissim)

Example output

Loading required package: smacof
Loading required package: plotrix

Attaching package: 'smacof'

The following object is masked from 'package:base':

    transform

Loading required package: vegan
Loading required package: permute
Loading required package: lattice
This is vegan 2.5-3
Loading required package: ellipse

Attaching package: 'ellipse'

The following object is masked from 'package:graphics':

    pairs

FreeSortR documentation built on May 2, 2019, 2:47 p.m.