sFit {bisque} R Documentation

Fit a spatially mean-zero spatial Gaussian process model

Description

Uses a Gibbs sampler to estimate the parameters of a Matern covariance function used to model observations from a Gaussian process with mean 0.

Usage

```sFit(
x,
coords,
nSamples,
thin = 1,
rw.initsd = 0.1,
inits = list(),
C = 1,
alpha = 0.44,
priors = list(sigmasq = list(a = 2, b = 1), rho = list(L = 0, U = 1), nu = list(L = 0,
U = 1))
)
```

Arguments

 `x` Observation of a spatial Gaussian random field, passed as a vector `coords` Spatial coordinates of the observation `nSamples` (thinned) number of MCMC samples to generate `thin` thinning to be used within the returned MCMC samples `rw.initsd` initial standard devaition for random walk proposals. this parameter will be adaptively tuned during sampling `inits` list of initial parameters for the MCMC chain `C` scale factor used during tuning of the random walk proposal s.d. `alpha` target acceptance rate for which the random walk proposals should optimize `priors` parameters to specify the prior distributions for the model

Examples

```library(fields)

simulate.field = function(n = 100, range = .3, smoothness = .5, phi = 1){
# Simulates a mean-zero spatial field on the unit square
#
# Parameters:
#  n - number of spatial locations
#  range, smoothness, phi - parameters for Matern covariance function

coords = matrix(runif(2*n), ncol=2)

Sigma = Matern(d = as.matrix(dist(coords)),
range = range, smoothness = smoothness, phi = phi)

list(coords = coords,
params = list(n=n, range=range, smoothness=smoothness, phi=phi),
x = t(chol(Sigma)) %*%  rnorm(n))
}

# simulate data
x = simulate.field()

# configure gibbs sampler
it = 100

# run sampler using default posteriors
post.samples = sFit(x = x\$x, coords = x\$coords, nSamples = it)

# build kriging grid
cseq = seq(0, 1, length.out = 10)
coords.krig = expand.grid(x = cseq, y = cseq)

# sample from posterior predictive distribution
burn = 75
samples.krig = sKrig(x\$x, post.samples, coords.krig = coords.krig, burn = burn)
```

[Package bisque version 1.0.2 Index]