matern_categorical: Isotropic Matern covariance function with random effects for...
In GpGp: Fast Gaussian Process Computation Using Vecchia's Approximation

Description Usage Arguments Value Functions Parameterization

From a matrix of locations and covariance parameters of the form (variance, range, smoothness, category variance, nugget), return the square matrix of all pairwise covariances.

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matern_categorical(covparms, locs)

d_matern_categorical(covparms, locs)

`covparms`	A vector with covariance parameters in the form (variance, range, smoothness, category variance, nugget)
`locs`	A matrix with `n` rows and `d` columns. Each row of locs gives a point in R^d.

A matrix with n rows and n columns, with the i,j entry containing the covariance between observations at locs[i,] and locs[j,].

d_matern_categorical: Derivatives of isotropic Matern covariance

The covariance parameter vector is (variance, range, smoothness, category variance, nugget) = (σ^2,α,ν,c^2,τ^2), and the covariance function is parameterized as

M(x,y) = σ^2 2^{1-ν}/Γ(ν) (|| x - y ||/α )^ν K_ν(|| x - y ||/α )

The nugget value σ^2 τ^2 is added to the diagonal of the covariance matrix. The category variance c^2 is added if two observation from same category NOTE: the nugget is σ^2 τ^2 , not τ^2 .

GpGp documentation built on June 10, 2021, 1:07 a.m.