powerStressMin {cops}

Power Stress SMACOF

Description

An implementation to minimize power stress by minimization-majorization. Usually more accurate but slower than powerStressFast. Uses a repeat loop.

Usage

powerStressMin(
  delta,
  kappa = 1,
  lambda = 1,
  nu = 1,
  weightmat = 1 - diag(nrow(delta)),
  init = NULL,
  ndim = 2,
  acc = 1e-10,
  itmax = 50000,
  verbose = FALSE
)

Arguments

delta

dist object or a symmetric, numeric data.frame or matrix of distances

kappa

power of the transformation of the fitted distances; defaults to 1

lambda

the power of the transformation of the proximities; defaults to 1

nu

the power of the transformation for weightmat; defaults to 1

weightmat

a matrix of finite weights

init

starting configuration

ndim

dimension of the configuration; defaults to 2

acc

numeric accuracy of the iteration

itmax

maximum number of iterations. Default is 50000.

verbose

should iteration output be printed; if > 1 then yes

Value

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

• delta: Observed dissimilarities, not normalized

• obsdiss: Observed dissimilarities, 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 (based on stress.en)

• ndim: Number of dimensions

• model: Name of smacof model

• niter: Number of iterations

• nobj: Number of objects

• type: Type of MDS model

and some additional components

• stress.m: default stress for the COPS and STOP defaults to the explicitly normalized stress on the normalized, transformed dissimilarities

• stress.en: a manually calculated stress on the normalized, transformed dissimilarities and normalized transformed distances which is not correct

• deltaorig: observed, untransformed dissimilarities

• weightmat: weighting matrix

See Also

smacofSym

Examples

dis<-smacof::kinshipdelta
res<-powerStressMin(as.matrix(dis),kappa=2,lambda=1.5,itmax=1000)
res
summary(res)
plot(res)