RMS {ptw} | R Documentation |
Functions to compare patterns with shifted features. These functions compare warped sample patterns to one or more reference patterns. RMS returns the usual root-mean-squared difference measure; WCC returns 1-wcc, where wcc indicates the weighted cross-correlation. Perfect alignment leads to a value of 0 for both criteria.
Internal function, not meant to be called directly by the user. In
particular, note that the identity warping may lead to slightly
different estimates than a direct comparison of the reference and
sample signals - a warping even slightly outside the original range of
1 : ncol(ref)
leads to NA values.
RMS(warp.coef, ref, samp, B, mode) WCC(warp.coef, ref, samp, B, trwdth = 20, wghts, mode, ref.acors = NULL)
warp.coef |
a vector of warping coefficients |
ref |
reference signal; a matrix with one or more rows. If the
number of rows is greater than one, it should equal the number of
rows in |
samp |
sample signal; a matrix with one or more rows |
B |
basis for warping function |
mode |
either "forward" (new implementation, also used for warping peak lists) or "backward" (classical implementation). |
trwdth |
triangle width for the WCC function, expressed 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 |
ref.acors |
autocorrelation of the reference. Since the reference is often unchanged over multiple evaluations (e.g., during an optimization), it is useful to pre-calculate this number |
All patterns in samp
are warped using the same warping
function, and then compared to ref
, either pair-wise (when
ref
and samp
are of the same size), or with the one
pattern in ref
.
One number - either the WCC or RMS value
Jan Gerretzen, Tom Bloemberg, Ron Wehrens
Eilers, P.H.C. (2004) "Parametric Time Warping", Analytical Chemistry, 76 (2), 404 – 411.
de Gelder, R., Wehrens, R. and Hageman, J.A. (2001) "A generalized expression for the similarity of spectra: Application to powder diffraction pattern classification", Journal of Computational Chemistry, 22, 273 – 289.