snv: Standard and Robust Normal Variate transformations
In inspectr: Storing, manipulating and analysis spectroscopy and associated data in R.

Description Usage Arguments Details Value Author(s) References See Also Examples

Standard and Robust Normal Variate transformations are often used in chemometrics to normalise a spectra collection and remove the baseline effect.

1 2	snv(x) rnv(x, r)

`x`	a vector of numeric values
`r`	the percentile to use in the RNV computation

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.

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.

apply_spectra, baseline

# Loading example data
data(australia)
spectra(australia) <- sr_no ~ ... ~ 350:2500

# Standard Normal Variate transform
s <- apply_spectra(australia, snv)
plot(s)

# The scale function in the base package is actually doing
# the same thing!
s <- apply_spectra(australia, scale, center = TRUE, scale = TRUE)
plot(s)

# Robust Normal Variate transform
s <- apply_spectra(australia, rnv, r = 0.25)
plot(s)