This is power stress with free kappa and lambda but rho is fixed to -1 and the weights are delta.
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"),
registry = struc_reg
)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
numeric matrix or dist object of a matrix of proximities
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.
MDS type. Defaults to "ratio".
(optional) a matrix of nonnegative weights
(optional) initial configuration
number of dimensions of the target space
number of iterations
additional arguments to be passed to the fitting procedure
weight to be used for the fit measure; defaults to 1
which structures to look for
weight to be used for the structures; defaults to 0.5
a list of parameters for the structuredness indices; each list element corresponds to one index in the order of the appeacrance in structures
numeric value hat prints information on the fitting process; >2 is extremely verbose
which weighting to be used in the multi-objective optimization? Either 'additive' (default) or 'multiplicative'.
registry object with c-structuredness indices.