gaussian_process: Gaussian process regression with linear kernel...

In resemble: Memory-Based Learning in Spectral Chemometrics

Gaussian process regression with linear kernel (gaussian_process)

Description

Carries out a gaussian process regression with a linear kernel (dot product). For internal use only!

Usage

gaussian_process(X, Y, noisev, scale)

Arguments

X	a matrix of predictor variables
Y	a matrix with a single response variable
noisev	a value indicating the variance of the noise for Gaussian process regression. Default is 0.001. a matrix with a single response variable
scale	a logical indicating whether both the predictors and the response variable must be scaled to zero mean and unit variance.

Value

a list containing the following elements:

• b: the regression coefficients.

• Xz: the (final transformed) matrix of predictor variables.

• alpha: the alpha matrix.

• is.scaled: logical indicating whether both the predictors and response variable were scaled to zero mean and unit variance.

• Xcenter: if matrix of predictors was scaled, the centering vector used for X.

• Xscale: if matrix of predictors was scaled, the scaling vector used for X.

• Ycenter: if matrix of predictors was scaled, the centering vector used for Y.

• Yscale: if matrix of predictors was scaled, the scaling vector used for Y.

Author(s)

Leonardo Ramirez-Lopez

resemble documentation built on June 8, 2025, 10:19 a.m.