yGradientglsw: y-Gradient Generalized Least Squares Weighting

In ChristianGoueguel/specProc: Preprocessing Tools For Laser-Induced Breakdown Spectroscopy (LIBS) Spectra

y-Gradient Generalized Least Squares Weighting

Description

The y-gradient generalized least squares weighting algorithm (GLSW) removes variance from the data (spectra), which is orthogonal to the response.

Usage

yGradientglsw(x, y, alpha = 0.01)

Arguments

x	A numeric matrix, data frame or tibble, representing the predictors data.
y	A numeric vector representing the response vector.
alpha	A numeric value specifying the weighting parameter. Typical values range from 1 to 0.0001. Default is 0.01.

Details

The y-Gradient GLSW is an alternative method to GLSW, where a continuous \textbf{y}-variable is used to develop pseudo-groupings of samples in \textbf{X} by comparing the differences in the corresponding \textbf{y} values. This is referred to as the "gradient method" because it utilizes a gradient of the sorted \textbf{X}- and \textbf{y}-blocks to calculate a covariance matrix.

Value

A tibble containing the filtering matrix.

References

• Zorzetti, B.M., Shaver, J.M., Harynuk, J.J., (2011). Estimation of the age of a weathered mixture of volatile organic compounds. Analytica Chimica Acta, 694(1-2):31–37.

ChristianGoueguel/specProc documentation built on Nov. 9, 2024, 3:23 p.m.