standardNormalVariate {prospectr}

Standard normal variate transformation

Description

This function normalizes each row of an input matrix by subtracting each row by its mean and dividing it by its standard deviation

Usage

standardNormalVariate(X)

Arguments

X

a numeric matrix of spectral data (optionally a data frame that can be coerced to a numerical matrix).

Details

SNV is simple way for normalizing spectral data that intends to correct for light scatter. It operates row-wise:

\[SNV_i = \frac{x_i - \bar{x}_i}{s_i}\]

where \(x_i\) is the signal of the \(i\)th observation, \(\bar{x}_i\) is its mean and \(s_i\) its standard deviation.

Value

a matrix of normalized spectral data.

Author(s)

Antoine Stevens

References

Barnes RJ, Dhanoa MS, Lister SJ. 1989. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Applied spectroscopy, 43(5): 772-777.

See Also

msc, detrend, blockScale, blockNorm

Examples

data(NIRsoil)
NIRsoil$spc_snv <- standardNormalVariate(X = NIRsoil$spc)
# 10 first snv spectra
matplot(
  x = as.numeric(colnames(NIRsoil$spc_snv)),
  y = t(NIRsoil$spc_snv[1:10, ]),
  type = "l",
  xlab = "wavelength, nm",
  ylab = "snv"
)
## Not run: 
apply(NIRsoil$spc_snv, 1, sd) # check

## End(Not run)