covMat: Calculate a covariance matrix

Description Usage Arguments Details Value References

Description

Evaluates one of the following covariance functions at input vectors t and t':

Usage

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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:

Value

A covariance matrix

References

Shi, J. Q., and Choi, T. (2011), “Gaussian Process Regression Analysis for Functional input”, CRC Press.


GPFDA documentation built on Jan. 29, 2021, 5:14 p.m.