stop_cldak {stops}

STOPS version of CLDA with free k.

Description

CLDA with free lambda0 and k and 20 epochs. Should we add alpha0?

Usage

stop_cldak(
  dis,
  theta = c(3 * max(sd(dis)), nrow(dis)/4),
  type = "ratio",
  weightmat = 1 - diag(nrow(dis)),
  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 explicit parameters; first is lambda0 for the maximal neighbourhood and second is k for the number of neighbours for the geodesic distance.

type

MDS type.

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

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 appearance 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-1 value

• stress.m: default normalized stress

• stoploss: the weighted loss value

• struc: the structuredness indices

• parameters: the parameters used for fitting (tau)

• fit: the returned object of the fitting procedure

• stopobj: the stopobj object