fastermds: 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
)
Value
n by p matrix with final coordinates.
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).
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 )