# corr_matrix: Power Exponential or Matern Correlation Matrix In GPfit: Gaussian Processes Modeling

## Description

Computes the power exponential or Matern correlation matrix for a set of n design points in d-dimensional input region and a vector of d correlation hyper-parameters (beta).

## Usage

 1 corr_matrix(X, beta, corr = list(type = "exponential", power = 1.95)) 

## Arguments

 X the (n x d) design matrix beta a (d x 1) vector of correlation hyper-parameters in (-∞, ∞) corr a list that specifies the type of correlation function along with the smoothness parameter. The default corresponds to power exponential correlation with smoothness parameter "power=1.95". One can specify a different power (between 1.0 and 2.0) for the power exponential, or use the Matern correlation function, specified as corr=list(type = "matern", nu=(2*k+1)/2), where k \in \{0,1,2,...\}

## Details

The power exponential correlation function is given by

R_{ij} = ∏ exp(-10^β*|x_{i}-x_{j}|^{power}).

The Matern correlation function given by Santner, Williams, and Notz (2003) is

R_{ij} = ∏ \frac{1}{Γ(ν)2^{ν-1}}(2√{ν}|x_{ik} - x_{jk}|10^{β_k})^ν κ_{ν}(2√{ν}|x_{ik} - x_{jk}|10^{β_k})R_{ij} = ∏ \frac{1}{Γ(ν)2^{ν-1}}(2√{ν}|x_{ik} - x_{jk}|10^{β_k})^ν κ_{ν}(2√{ν}|x_{ik} - x_{jk}|10^{β_k})R_{ij} = ∏ \frac{1}{Γ(ν)2^{ν-1}}(2√{ν}|x_{ik} - x_{jk}|10^{β_k})^ν κ_{ν}(2√{ν}|x_{ik} - x_{jk}|10^{β_k}),

where κ_{ν} is the modified Bessel function of order ν.

## Value

The (n x n) correlation matrix, R, for the design matrix (X) and the hyper-parameters (beta).

## Note

Both Matern and power exponential correlation functions use the new β parametrization of hyper-parameters given by θ_k = 10^{β_k} for easier likelihood optimization. That is, beta is a log scale parameter (see MacDonald et al. (2015)).

## Author(s)

Blake MacDonald, Hugh Chipman, Pritam Ranjan

## References

MacDonald, K.B., Ranjan, P. and Chipman, H. (2015). GPfit: An R Package for Fitting a Gaussian Process Model to Deterministic Simulator Outputs. Journal of Statistical Software, 64(12), 1-23. http://www.jstatsoft.org/v64/i12/

Ranjan, P., Haynes, R., and Karsten, R. (2011). A Computationally Stable Approach to Gaussian Process Interpolation of Deterministic Computer Simulation Data, Technometrics, 53(4), 366 - 378.

Santner, T.J., Williams, B., and Notz, W. (2003), The design and analysis of computer experiments, Springer Verlag, New York.

## Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 ## 1D Example - 1 n = 5 d = 1 set.seed(3) library(lhs) x = maximinLHS(n,d) beta = rnorm(1) corr_matrix(x,beta) ## 1D Example - 2 beta = rnorm(1) corr_matrix(x,beta,corr = list(type = "matern")) ## 2D example - 1 n = 10 d = 2 set.seed(2) library(lhs) x = maximinLHS(n,d) beta = rnorm(2) corr_matrix(x, beta, corr = list(type = "exponential", power = 2)) ## 2D example - 2 beta = rnorm(2) R = corr_matrix(x,beta,corr = list(type = "matern", nu = 5/2)) print(R) 

GPfit documentation built on May 2, 2019, 5:31 a.m.