gaussian_process: Gaussian process regression with linear kernel...
In resemble: Memory-Based Learning in Spectral Chemometrics

gaussian_process

R Documentation

Gaussian process regression with linear kernel (gaussian_process)

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

gaussian_process(X, Y, noisev, scale)

`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.

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.