smacof_forward_mds: Do forward MDS by using the SMACOF algorithm defined on page...

In NathanWycoff/mds.methods:

Description

Do forward MDS by using the SMACOF algorithm defined on page 191 of Borg and Groenen.

Usage

smacof_forward_mds(high_d, weights, dist.func = euclidean.dist,
  thresh = 1e-05, max.iters = 1000, n.inits = 10, seed = NULL,
  std = TRUE, symm = FALSE)

Arguments

high_d	The high dimensional data of which a low dimensional representation is desired, an n by p matrix where rows represent observations
dist.func	The distance function to be used for both low and high D distance computation.
thresh	The threshold below which stress must fall before computation ends.
max.iters	The maximum number of SMACOF iterations (i.e., the maximum number of times we solve the quadratic system described in chapter 8 of Borg and Groenen) per initialization.
n.inits	The number of times the SMACOF algorithm is run from a random configuration. This can be important, as the cost surface is highly nonconvex.
seed	Random seed used for initialization
symm	Boolean, is the distance function symmetric? We can save on computation if so.
low_d	The low_d solution, an n by 2 matrix, the cost of which is to be evaluated.

Value

List with $par, the optimal configuration as an n by 2 matrix, and $value as the stress of this configuration

NathanWycoff/mds.methods documentation built on May 23, 2019, 7:32 a.m.