predict.CBrSPDEobj2d {rSPDE} | R Documentation |
Prediction of an anisotropic Whittle-Matern field
Description
The function is used for computing kriging predictions based
on data Y_i = u(s_i) + \epsilon_i
, where \epsilon
is mean-zero Gaussian measurement noise and u(s)
is defined by
a SPDE as described in matern2d.operators()
.
Usage
## S3 method for class 'CBrSPDEobj2d'
predict(
object,
A,
Aprd,
Y,
sigma.e,
mu = 0,
compute.variances = FALSE,
posterior_samples = FALSE,
n_samples = 100,
only_latent = FALSE,
...
)
Arguments
object |
The covariance-based rational SPDE approximation,
computed using |
A |
A matrix linking the measurement locations to the basis of the FEM approximation of the latent model. |
Aprd |
A matrix linking the prediction locations to the basis of the FEM approximation of the latent model. |
Y |
A vector with the observed data, can also be a matrix where the
columns are observations
of independent replicates of |
sigma.e |
The standard deviation of the Gaussian measurement noise. Put to zero if the model does not have measurement noise. |
mu |
Expectation vector of the latent field (default = 0). |
compute.variances |
Set to also TRUE to compute the kriging variances. |
posterior_samples |
If |
n_samples |
Number of samples to be returned. Will only be used if |
only_latent |
Should the posterior samples be only given to the laten model? |
... |
further arguments passed to or from other methods. |
Value
A list with elements
mean |
The kriging predictor (the posterior mean of u|Y). |
variance |
The posterior variances (if computed). |
Examples
library(fmesher)
n_loc <- 2000
loc_2d_mesh <- matrix(runif(n_loc * 2), n_loc, 2)
mesh_2d <- fm_mesh_2d(loc = loc_2d_mesh, cutoff = 0.01, max.edge = c(0.1, 0.5))
op <- matern2d.operators(hx = 0.08, hy = 0.08, hxy = 0.5, nu = 0.5,
sigma = 1, mesh = mesh_2d)
u <- simulate(op)
n.obs <- 2000
obs.loc <- cbind(runif(n.obs),runif(n.obs))
A <- fm_basis(mesh_2d,obs.loc)
sigma.e <- 0.1
Y <- as.vector(A%*%u + sigma.e*rnorm(n.obs))
A <- op$make_A(obs.loc)
proj <- fm_evaluator(mesh_2d, dims = c(100, 100),
xlim = c(0,1), ylim = c(0,1))
Aprd <- op$make_A(proj$lattice$loc)
u.krig <- predict(op, A = A, Aprd = Aprd, Y = Y, sigma.e = sigma.e)