stop_smacofSym {stops}

STOPS version of smacofSym models

Description

The free parameter is lambda for power transformations the observed proximities. The fitted distances power is internally fixed to 1 and the power for the weights is 1.

Usage

stop_smacofSym(
  dis,
  theta = 1,
  type = "ratio",
  ndim = 2,
  weightmat = 1 - diag(nrow(dis)),
  init = NULL,
  itmaxi = 1000,
  ...,
  structures = c("cclusteredness", "clinearity", "cdependence", "cmanifoldness",
    "cassociation", "cnonmonotonicity", "cfunctionality", "ccomplexity", "cfaithfulness",
    "chierarchy", "cconvexity", "cstriatedness", "coutlying", "cskinniness", "csparsity",
    "cstringiness", "cclumpiness", "cinequality"),
  stressweight = 1,
  strucweight = rep(1/length(structures), length(structures)),
  strucpars,
  verbose = 0,
  stoptype = c("additive", "multiplicative")
)

Arguments

dis

numeric matrix or dist object of a matrix of proximities

theta

the theta vector; must be a scalar for the lambda (proximity) transformation. Defaults to 1.

type

MDS type. Defaults ot 'ratio'.

ndim

number of dimensions of the target space

weightmat

(optional) a matrix of nonnegative weights

init

(optional) initial configuration

itmaxi

number of iterations

...

additional arguments to be passed to the fitting

structures

which structuredness indices to be included in the loss

stressweight

weight to be used for the fit measure; defaults to 1

strucweight

weight to be used for the structuredness indices; ; defaults to 1/#number of structures

strucpars

the parameters for the structuredness indices

verbose

numeric value hat prints information on the fitting process; >2 is extremely verbose

stoptype

How to construct the target function for the multi objective optimization? Either 'additive' (default) or 'multiplicative'

Value

A list with the components

• stress: the stress-1 (sqrt(stress.m))

• stress.m: default normalized stress (used for STOPS)

• stoploss: the weighted loss value

• indices: the values of the structuredness indices

• parameters: the parameters used for fitting (lambda)

• fit: the returned object of the fitting procedure

• stopobj: the stops object