stop_powersammon {stops}

STOPS version of sammon with powers

Description

This is power stress with free kappa and lambda but rho is fixed to -1 and the weights are delta.

Usage

stop_powersammon(
  dis,
  theta = c(1, 1),
  type = "ratio",
  weightmat = NULL,
  init = NULL,
  ndim = 2,
  itmaxi = 10000,
  ...,
  stressweight = 1,
  structures = c("cclusteredness", "clinearity", "cdependence", "cmanifoldness",
    "cassociation", "cnonmonotonicity", "cfunctionality", "ccomplexity", "cfaithfulness",
    "cregularity", "chierarchy", "cconvexity", "cstriatedness", "coutlying",
    "cskinniness", "csparsity", "cstringiness", "cclumpiness", "cinequality"),
  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 of powers; a vector of length two where the first element is kappa (for the fitted distances), the second lambda (for the observed proximities). If a scalar is given it is recycled for the free parameters. Defaults to 1 1.

type

MDS type. Defaults to "ratio".

weightmat

(optional) a matrix of nonnegative weights

init

(optional) initial configuration

ndim

number of dimensions of the target space

itmaxi

number of iterations

...

additional arguments to be passed to the fitting procedure

stressweight

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

structures

which structures to look for

strucweight

weight to be used for the structures; defaults to 0.5

strucpars

a list of parameters for the structuredness indices; each list element corresponds to one index in the order of the appeacrance in structures

verbose

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

stoptype

which weighting to be used in the multi-objective optimization? Either 'additive' (default) or 'multiplicative'.

Value

A list with the components

• stress: the stress

• stress.m: default normalized stress

• stoploss: the weighted loss value

• struc: the structuredness indices

• parameters: the parameters used for fitting (kappa, lambda)

• fit: the returned object of the fitting procedure

• stopobj: the stopobj object