cv.fastmds: Repeated Cross-Validation Penalized Restricted...

In fmds: Multidimensional Scaling Development Kit

Repeated Cross-Validation Penalized Restricted Multidimensional Scaling Function

Description

cv.fastmds performs repeated cross-validation for a penalized restricted multidimensional scaling model.

Usage

cv.fastmds(
  delta,
  w = NULL,
  p = 2,
  q = NULL,
  b = NULL,
  lambda = 0,
  alpha = 1,
  grouped = FALSE,
  NFOLDS = 10,
  NREPEATS = 30,
  MAXITER = 1024,
  FCRIT = 1e-08,
  ZCRIT = 1e-06,
  error.check = FALSE,
  echo = FALSE
)

Arguments

delta	an n by n symmatric and hollow matrix containing dissimilarities.
w	an identical sized matrix containing nonnegative weights (all ones when omitted).
p	dimensionality (default = 2).
q	independent variables (n by h).
b	initial regression coefficients (h by p).
lambda	regularization penalty parameter(s) (default = 0.0: no penalty).
alpha	elastic-net parameter (default = 1.0: lasso only).
grouped	boolean for lasso penalty (default = FALSE: ordinary lasso).
NFOLDS	number of folds for the k-fold cross-validation.
NREPEATS	number of repeats for the repeated k-fold cross-validation.
MAXITER	maximum number of iterations (default = 1024).
FCRIT	relative convergence criterion function value (default = 0.00000001).
ZCRIT	absolute convergence criterion coordinates (default = 0.000001).
error.check	extensive check validity input parameters (default = FALSE).
echo	print intermediate algorithm results (default = FALSE).

Value

mserrors mean squared errors for different values of lambda.

stderrors standard errors for mean squared errors.

varnames labels of independent row variables.

coefficients list with final h by p matrices with regression coefficients (lambda order).

lambda sorted regularization penalty parameters.

alpha elastic-net parameter (default = 1.0: lasso only).

grouped boolean for lasso penalty (default = FALSE: ordinary lasso).

References

de Leeuw, J., and Heiser, W. J. (1980). Multidimensional scaling with restrictions on the configuration. In P.R. Krishnaiah (Ed.), Multivariate analysis (Vol. 5, pp. 501–522). Amsterdam, The Netherlands: North-Holland Publishing Company.

Heiser,W. J. (1987a). Joint ordination of species and sites: The unfolding technique. In P. Legendre and L. Legendre (Eds.), Developments in numerical ecology (pp. 189–221). Berlin, Heidelberg: Springer-Verlag.

Busing, F.M.T.A. (2010). Advances in multidimensional unfolding. Unpublished doctoral dissertation, Leiden University, Leiden, the Netherlands.

fmds documentation built on June 8, 2025, 1:34 p.m.