MdsDiss function

Mds of a dissimilarity matrix

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.

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

Returns

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)