gaussian_process {resemble} | R Documentation |
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 forX
.Xscale
: if matrix of predictors was scaled, the scaling vector used forX
.Ycenter
: if matrix of predictors was scaled, the centering vector used forY
.Yscale
: if matrix of predictors was scaled, the scaling vector used forY
.
Author(s)
Leonardo Ramirez-Lopez