standardNormalVariate: Standard normal variate transformation

In l-ramirez-lopez/prospectr: Miscellaneous Functions for Processing and Sample Selection of Spectroscopic Data

Standard normal variate transformation

Description

\loadmathjax

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:

\mjdeqn

SNV_i = \fracx_i - \barx_is_iSNV_i = \fracx_i - \barx_is_i

where \mjeqnx_ix_i is the signal of the \mjeqniith observation, \mjeqn\barx_i\barx_i is its mean and \mjeqns_is_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)

l-ramirez-lopez/prospectr documentation built on Feb. 18, 2024, 7:52 a.m.