icExplore: Exploring Initial Configurations

In smacof: Multidimensional Scaling

Exploring Initial Configurations

Description

Allows to user to explore the effect of various random starting configurations when fitting an MDS model.

Usage

icExplore(delta, nrep = 100, returnfit = FALSE, ndim = 2, 
type = c("ratio", "interval", "ordinal","mspline"), weightmat = NULL, ties = "primary",	
verbose = FALSE, relax = FALSE, modulus = 1, itmax = 1000, eps = 1e-6, 
spline.degree = 2, spline.intKnots = 2)

Arguments

delta	Either a symmetric dissimilarity matrix or an object of class "dist"
nrep	Number of initial random configurations
returnfit	If TRUE all fitted models are returned.
ndim	Number of dimensions
weightmat	Optional matrix with dissimilarity weights
type	MDS type: "interval", "ratio", "ordinal" (nonmetric MDS), or "mspline"
ties	Tie specification (ordinal MDS only): "primary", "secondary", or "tertiary"
verbose	If TRUE, replication number is printed
relax	If TRUE, block relaxation is used for majorization
modulus	Number of smacof iterations per monotone regression call
itmax	Maximum number of iterations
eps	Convergence criterion
spline.degree	Degree of the spline for "mspline" MDS type
spline.intKnots	Number of interior knots of the spline for "mspline" MDS type

Details

This function generates a large set of MDS solutions using random initial configurations, matches them all by Procrustean fittings, computes the inter-correlations of their point coordinates, and finally runs an interval MDS of these inter-correlations. It can be used to explore local minima.

In the plot function the number reflects the index of corresponding MDS fit, the size reflects the stress value: the larger the font, the larger the stress (i.e., the worse the solution). The size is associated with a corresponding color shading (the smaller the size the darker the color).

Value

mdsfit	Fitted MDS objects (NULL if returnfit = FALSE)
conf	Configuration based on multiple random starts
stressvec	Vector with stress values

References

Borg, I. and Mair, P. (2017). The choice of initial configurations in multidimensional scaling: local minima, fit, and interpretability. Austrian Journal of Statistics, 46, 19-32. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.17713/ajs.v46i2.561")}

See Also

mds

Examples

## simple example with 20 random starts
diss <- sim2diss(wish, method = 7)
set.seed(123)
res <- icExplore(diss, type = "ordinal", nrep = 20, returnfit = TRUE)
res
plot(res) 

res$mdsfit[[14]]  ## bad fitting solution
res$mdsfit[[3]]   ## better fitting solution

smacof documentation built on Oct. 10, 2024, 5:09 p.m.