Standard and Robust Normal Variate transformations
Standard and Robust Normal Variate transformations are often used in chemometrics to normalise a spectra collection and remove the baseline effect.
The Standard Normal Variate transformation (SNV, Barnes et al., 1989) is a common method to reduce within-class variance.
The Robust Normal Variate transformation (RNV, Guo et al., 1999) is a modification of the SNV to make it more robust to closure problems.
These function are to be used in conjonction with
apply_spectra.
snv(x) rnv(x, r)
x |
a vector of numeric values |
r |
the percentile to use in the RNV computation |
A vector of numeric values
Pierre Roudier pierre.roudier@gmail.com
Barnes, R.J., Dhanoa, M.S., Lister, S.J. 1989. Standard normal variate transformation and detrending of near-infra-red diffuse reflectance spectra. Applied Spectroscopy 43, 772–777.
Guo, Q., Wu, W., Massar, D.L. 1999. The robust normal variate transform for pattern recognition with near-infrared data. Analytica Chimica Acta 382:1–2, 87–103.
# Loading example data data(australia) spectra(australia) <- sr_no ~ ... ~ 350:2500 # Standard Normal Variate transform s <- apply_spectra(australia[1:10,], snv) plot(s) # The scale function in the base package is actually doing # the same thing! s <- apply_spectra(australia[1:10,], scale, center = TRUE, scale = TRUE) plot(s) # Robust Normal Variate transform s <- apply_spectra(australia[1:10,], rnv, r = 0.25) plot(s)
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