stop_powerstress {stops}

STOPS version of powerstress

Description

Power stress with free kappa and lambda and rho.

Usage

stop_powerstress(
  dis,
  theta = c(1, 1, 1),
  weightmat = NULL,
  init = NULL,
  ndim = 2,
  itmax = 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,
  type = c("additive", "multiplicative")
)

Arguments

dis

numeric matrix or dist object of a matrix of proximities

theta

the theta vector of powers; the first is kappa (for the fitted distances), the second lambda (for the observed proximities), the third nu (for the weights). If a scalar is given it is recycled. Defaults to 1 1 1.

weightmat

(optional) a matrix of nonnegative weights

init

(optional) initial configuration

ndim

number of dimensions of the target space

itmax

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

a character vector listing the structure indices to use. They always are called "cfoo" with foo being the structure.

strucweight

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

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

type

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, nu)

• fit: the returned object of the fitting procedure

• stopobj: the stopobj object