wcc {wccsom} | R Documentation |
Weighted cross correlation and autocorrelation, as described in De Gelder et al. (2001), for assessing similarities in spectra-like data containing peak shifts. Euclidean distances are useless in this situation.
wcc(pattern1, pattern2, trwdth, wghts, acors) wac(pattern1, trwdth, wghts) wacmat(patterns, trwdth, wghts, do.transpose = TRUE)
pattern1 |
Pattern. |
pattern2 |
Another pattern. |
patterns |
Pattern matrix: rows correspond with patterns. |
trwdth |
Triangle width, given in the number of data points. |
wghts |
Optional weights vector, will be calculated from triangle width if necessary. Sometimes it is more efficient to pre-calculate it and give it as an argument. |
acors |
Autocorrelation, also optional to speed up calculations. |
do.transpose |
Internally, columns should correspond with patterns, so normally one should leave this value to its default: TRUE. If a matrix is already in the correct format, one can avoid unnecessary double transpose operations. |
Function wcc
returns the WCC value, a similarity value between
0 and 1. Functions wac
and wacmat
return weighted
autocorrelations for one pattern and a matrix of patterns,
respectively.
Ron Wehrens
R. de Gelder, R. Wehrens, and J.A. Hageman. A generalized expression for the similarity spectra: application to powder diffraction pattern classification. J. Comput. Chem., 22(3), 273-289, 2001.
data(cepha) wac(cepha$patterns[1,], 20) wacmat(t(cepha$patterns), 20) wcc(cepha$patterns[1,], cepha$patterns[2,], 20)