snv {spectacles}

Standard and Robust Normal Variate transformations

Description

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.

Usage

snv(x)

rnv(x, r)

Arguments

x

a vector of numeric values

r

the percentile to use in the RNV computation

Value

A vector of numeric values

Author(s)

Pierre Roudier [e-mail]

References

• 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.

Examples

# 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)