Uses smacofx::sammon. The free parameter is lambda for power transformations of the observed proximities.
stop_sammon(
dis,
theta = 1,
type = "ratio",
ndim = 2,
init = NULL,
weightmat = NULL,
itmaxi = 1000,
...,
stressweight = 1,
structures = c("cclusteredness", "clinearity", "cdependence", "cmanifoldness",
"cassociation", "cnonmonotonicity", "cfunctionality", "ccomplexity", "cfaithfulness",
"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(
stress.m: default normalized stress
stoploss: the weighted loss value
indices: the values of the structuredness indices
parameters: the parameters used for fitting
fit: the returned object of the fitting procedure smacofx::sammon
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. Ignored here.
number of dimensions of the target space
(optional) initial configuration
a matrix of nonnegative weights. Has no effect here.
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.