fastermds: Stochastic Iterative Majorization Multidimensional Scaling...

In fmds: Multidimensional Scaling Development Kit

Stochastic Iterative Majorization Multidimensional Scaling Function

Description

fastermds performs multidimensional scaling using a stochastic iterative majorization algorithm. The data are either dissimilarities (full or only lower triangular part) or multivariate data. The dissimilarities and the weights may not contain negative values. The configuration is either unrestricted or (partly) fixed. Local multidimensional scaling is performed when a boundary is provided. Interval multidimensional scaling is performed with a full dissimilarity matrix, using the lower triangular part for the lower bound and the upper triangular part for the upper bound.

Usage

fastermds(
  delta = NULL,
  lower = NULL,
  data = NULL,
  w = NULL,
  p = 2,
  z = NULL,
  fixed = NULL,
  linear = FALSE,
  boundary = NULL,
  interval = FALSE,
  NCYCLES = 32,
  MINRATE = 0.01,
  error.check = FALSE,
  test = 0
)

Arguments

delta	dissimilarity matrix, non-negative, square, and hollow.
lower	lower triangular part of dissimilarity matrix.
data	multivariate data matrix.
w	non-negative weights per dissimilarity for delta and lower, and per object for data
p	dimensionality (default = 2).
z	n by p matrix with initial coordinates.
fixed	n by p matrix with booleans indicating free (FALSE) or fixed (TRUE) coordinates.
linear	boolean indicating whether linear is used.
boundary	boundary value for local mds.
interval	interval measurements for interval mds, requires delta data format.
NCYCLES	number of cycles taken by the algorithm (default = 32).
MINRATE	criterion rate of convergence (default = 0.01).
error.check	extensive validity check input parameters (default = FALSE).
test	indicates which test is applied.

Details

One of the following three data formats need to be specified:

Value

n by p matrix with final coordinates.

Author(s)

Frank M.T.A. Busing

References

Agrafiotis, and others, and Busing

Examples

n <- 1000
m <- 10
delta <- as.matrix( dist( matrix( runif( n * m ), n, m ) ) )
p <- 2
zinit <- matrix( runif( n * p ), n, p )
# r <- fastermds( delta = delta, p = p, z = zinit, error.check = TRUE )

fmds documentation built on June 8, 2025, 1:34 p.m.