cmdscale_lanczos: Faster multi-dimensional scaling

In refund: Regression with Functional Data

Faster multi-dimensional scaling

Description

This is a modified version of {cmdscale} that uses the Lanczos procedure ([mgcv]{slanczos}) instead of eigen. Called by {smooth.construct.pco.smooth.spec}.

Usage

cmdscale_lanczos(d, k = 2, eig = FALSE, add = FALSE, x.ret = FALSE)

Arguments

d	a distance structure as returned by {dist}, or a full symmetric matrix of distances or dissimilarities.
k	the maximum dimension of the space which the data are to be represented in; must be in {1, 2, ..., n-1}.
eig	logical indicating whether eigenvalues should be returned.
add	logical indicating if the additive constant of Cailliez (1983) should be computed, and added to the non-diagonal dissimilarities such that the modified dissimilarities are Euclidean.
x.ret	indicates whether the doubly centred symmetric distance matrix should be returned.

Value

as {cmdscale}

Author(s)

David L Miller, based on code by R Core.

References

Cailliez, F. (1983). The analytical solution of the additive constant problem. Psychometrika, 48, 343-349.

See Also

{smooth.construct.pco.smooth.spec}

refund documentation built on Sept. 21, 2024, 1:07 a.m.