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Standard normal variate transformation

Source: R/standardNormalVariate.R
standardNormalVariate.Rd

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). Optionally, a vector can be provided, in which case it will be treated as a single spectrum.

Value

a matrix of normalized spectral data.

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.

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

Author

Antoine Stevens and Leonardo Ramirez-Lopez

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"
)

if (FALSE) { # \dontrun{
apply(NIRsoil$spc_snv, 1, sd) # check
} # }