analytics (version 3.0)

Minstress: Better Starting Configuration For Non-Metric MDS

Description

Minstress is a heuristic to find better non-metric MDS solutions, by finding better starting configurations, instead of just using a random one.

Usage

Minstress(x, p, s, k, iter = 5, pb = F, m = "euclidean")

Arguments

x

a data frame containing numeric values only

p

the size of the population of seeds (any positive integer)

s

the number of seeds we sample (any positive integer)

k

the number of dimensions wanted (any positive integer)

iter

a positive integer specifying the number of iterations.

pb

a Boolean variable declaring if one wants to display a pogress bar (default: False)

m

a string specifying the distance method (default: 'euclidean')

Value

A list informing about dimensionality, minimum STRESS level found, and best seed found. One can then use the best seed found to perform non-metric MDS with a better initial configuration (generally).

Details

This function performs several iterations, each using a different starting seed, and in turn each one of those iterations performs non-metric MDS many times (typically, thousands or more) in an attempt to find the best seed (which induces a particular initial configuration) of them all.

Examples

# NOT RUN {
require(MASS)

swiss.x <- as.data.frame(swiss[, -1])
Minstress(swiss.x, 1e5, 50, 2, iter = 3)

# Comparing without using Minstress (for such a low value of s, difference is minimal)
swiss.x <- as.matrix(swiss[, -1])
swiss.dist <- dist(swiss.x)
swiss.mds <- isoMDS(swiss.dist)

# }