multiscale {smacofx}

Multiscale SMACOF

Description

An implementation for maximum likelihood MDS aka multiscale that minimizes the multiscale stress by majorization with ratio and interval optimal scaling. Uses a repeat loop.

Usage

multiscale(
  delta,
  type = c("ratio", "interval"),
  weightmat,
  init = NULL,
  ndim = 2,
  acc = 1e-06,
  itmax = 10000,
  verbose = FALSE,
  kappa = 0.1,
  principal = FALSE
)

Arguments

delta

dist object or a symmetric, numeric data.frame or matrix of distances. Warning: these will get transformed to the log scale, so make sure that log(delta)>=0.

type

what optimal scaling type of MDS to fit. Currently one of "ratio" or "interval". Default is "ratio".

weightmat

a matrix of finite weights

init

starting configuration

ndim

dimension of the configuration; defaults to 2

acc

numeric accuracy of the iteration. Default is 1e-6.

itmax

maximum number of iterations. Default is 10000.

verbose

should iteration output be printed; if > 1 then yes

kappa

As this is not exactly multiscale but an r-stress approximation, we have multiscale only for kappa->0. This argument can therefore be used to make the approximation more accurate by making it smaller. Default is 0.1.

principal

If ‘TRUE’, principal axis transformation is applied to the final configuration

Value

a 'smacofP' object (inheriting from 'smacofB', see smacofSym). It is a list with the components

• delta: Observed dissimilarities

• tdelta: Observed explicitly transformed (log) dissimilarities, normalized

• dhat: Explicitly transformed dissimilarities (dhats), optimally scaled and normalized

• confdist: Configuration dissimilarities, NOT normalized

• conf: Matrix of fitted configuration, NOT normalized

• stress: Default stress (stress 1; sqrt of explicitly normalized stress)

• spp: Stress per point

• ndim: Number of dimensions

• model: Name of smacof model

• niter: Number of iterations

• nobj: Number of objects

• type: Type of MDS model

• weightmat: weighting matrix

• stress.m: Default stress (stress-1^2)

Warning

The input delta will internally get transformed to the log scale, so make sure that log(delta)>=0 otherwise it throws an error. It is often a good idea to use 1+delta in this case.

See Also

rStressMin

Examples

dis<-smacof::kinshipdelta
res<-multiscale(as.matrix(dis),type="interval",itmax=1000)
res
summary(res)
plot(res)