| covMat {GPFDA} | R Documentation |
Calculate a covariance matrix
Description
Evaluates one of the following covariance functions at input vectors t and t':
Powered exponential
Rational quadratic
Matern
Linear
Usage
cov.pow.ex(hyper, input, inputNew = NULL, gamma = 2)
cov.rat.qu(hyper, input, inputNew = NULL)
cov.matern(hyper, input, inputNew = NULL, nu)
cov.linear(hyper, input, inputNew = NULL)
Arguments
hyper |
The hyperparameters. It must be a list with certain names. See details. |
input |
The covariate t. It must be either a matrix, where each column represents a covariate, or a vector if there is only one covariate. |
inputNew |
The covariate t'. It also must be a vector or a matrix. If NULL (default), 'inputNew' will be set to be equal to ‘input’ and the function will return a squared, symmetric covariance matrix. |
gamma |
Power parameter used in powered exponential kernel function. It must be 0<gamma<=2. Default to 2, which gives the squared exponential covariance function. |
nu |
Smoothness parameter of the Matern class. It must be a positive value. |
Details
The names for the hyperparameters should be:
"pow.ex.v" and "pow.ex.w" (powered exponential);
"rat.qu.v", "rat.qu.w" and "rat.qu.a" (rational quadratic);
"matern.v" and "matern.w" (Matern);
"linear.i" and "linear.a" (linear);
"vv" (Gaussian white noise).
Value
A covariance matrix
References
Shi, J. Q., and Choi, T. (2011), “Gaussian Process Regression Analysis for Functional input”, CRC Press.