kernel.fun: Generates covariances matrices thanks to 'Kernel.class'

In CaliCo: Code Calibration in a Bayesian Framework

Description

Kernel.fun is a function that allows us to generate covariances matrices from data

Usage

kernel.fun(X, var, psi, kernel.type = "gauss")

Arguments

X	data
var	the variance for the covariance function
psi	the parameter vector
kernel.type	the choice of the form of the kernel (with d chosen as an euclidian distance) gauss σ^2 exp{-1/2(d/ψ)^2} exp σ^2 exp{-1/2 d/ψ} matern3_2 σ^2(1+√{3}d^2/ψ) exp{-√{3}d^2/ψ} matern5_2 σ^2(1+√{5}d^2/ψ+5d^2/(3ψ^2))exp{-√{5}d^2/ψ}

Value

Kernel.fun returns a covariance matrix

Author(s)

M. Carmassi

See Also

model.class, prior.class

Examples

## Not run: 
X <- cbind(seq(0,10,length.out=10),seq(8,20,length.out=10))
var <- 2
psi <- 0.1
Cov <- kernel.fun(X,var,psi,kernel.type="matern5_2")

## End(Not run)

CaliCo documentation built on May 2, 2019, 4:05 p.m.