Uses Smacof, so it can deal with a weight matrix too. The free parameter is lambda for power transformations of the observed proximities. The fitted distances power is internally fixed to 1 and the power for the weights=delta is -1.
stop_sammon2(
dis,
theta = 1,
type = "ratio",
ndim = 2,
weightmat = NULL,
init = NULL,
itmaxi = 1000,
...,
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-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 stopobj object
numeric matrix or dist object of a matrix of proximities
the theta vector of powers; this must be a scalar of the lambda transformation for the observed proximities. Defaults to 1.
MDS type
number of dimensions of the target space
(optional) a matrix of nonnegative weights
(optional) initial configuration
number of iterations
additional arguments to be passed to the fitting procedure
weight to be used for the fit measure; defaults to 1
which structuredness indices to be included in the loss
weight to be used for the structuredness indices; ; defaults to 1/#number of structures
the parameters for the structuredness indices
numeric value hat prints information on the fitting process; >2 is extremely verbose
How to construct the target function for the multi objective optimization? Either 'additive' (default) or 'multiplicative'.
registry object with c-structuredness indices.