clca function

Curvilinear Component Analysis (CLCA)

A wrapper to run curvilinear component analysis via CCA and returning a 'smacofP' object. Note this functionality is rather rudimentary.

clca(
  delta,
  Epochs = 20,
  alpha0 = 0.5,
  lambda0,
  ndim = 2,
  weightmat = 1 - diag(nrow(delta)),
  init = NULL,
  acc = 1e-06,
  itmax = 10000,
  verbose = 0,
  method = "euclidean",
  principal = FALSE
)

Arguments

• delta: dist object or a symmetric, numeric data.frame or matrix of distances.
• Epochs: Scalar; gives the number of passes through the data.
• alpha0: (scalar) initial step size, 0.5 by default
• lambda0: the boundary/neighbourhood parameter(s) (called lambda_y in the original paper). It is supposed to be a numeric scalar. It defaults to the 90% quantile of delta.
• ndim: dimension of the configuration; defaults to 2
• weightmat: not used
• init: starting configuration, not used
• acc: numeric accuracy of the iteration; not used
• itmax: maximum number of iterations. Not used.
• verbose: should iteration output be printed; not used
• method: Distance calculation; currently not used.
• principal: If 'TRUE', principal axis transformation is applied to the final configuration

Returns

a 'smacofP' object. It is a list with the components

• delta: Observed, untransformed dissimilarities
• tdelta: Observed explicitly transformed dissimilarities, normalized
• dhat: Explicitly transformed dissimilarities (dhats), optimally scaled and normalized
• confdist: Configuration dissimilarities
• conf: Matrix of fitted configuration
• stress: Default stress (stress-1; sqrt of explicitly normalized stress)
• spp: Stress per point
• ndim: Number of dimensions
• model: Name of model
• niter: Number of iterations (training length)
• nobj: Number of objects
• type: Type of MDS model. Only ratio here.
• weightmat: weighting matrix as supplied
• stress.m: Default stress (stress-1^2)
• tweightmat: transformed weighting matrix; it is weightmat here.

Details

This implements CCA as in Demartines & Herault (1997). A different take on the ideas of curvilinear compomnent analysis is available in the experimental functions spmds and spmds.

Examples

dis<-smacof::morse
res<-clca(dis,lambda0=0.4)
res
summary(res)
plot(res)