cv.fastmds {fmds} | R Documentation |
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 Unpublished doctoral dissertation, Leiden University, Leiden, the Netherlands.