gear (version 0.3.4)

evaluate.cmodStd: Evaluate spatial dependence model

Description

evaluate evaluates the spatial dependence model based on the provided arguments.

Usage

# S3 method for cmodStd
evaluate(mod, d, e = TRUE, f = TRUE)
evaluate(mod, d, e = TRUE, f = TRUE)

Arguments

mod

A covariance or semivariogram model.

d

An \(n \times m\) matrix of distances. If mod$ratio != 1, i.e., if geometric anisotropy has been specified, then d must be produced by the ganiso_d function.

e

A single logical value indicating whether the error variance should be added to the returned covariance matrix. Default is TRUE.

f

A single logical value indicating whether the finescale/microscale variance should be added to the returned covariance matrix. Default is TRUE.

Value

Returns the evaluated model with necessary components needed for estimate and predict.

Details

If mod is of class cmodStd (from the cmod_std function), then the function returns an \(n \times m\) matrix with the evaluated standard covariance function.

Examples

# NOT RUN {
n = 10
coords = matrix(runif(2*n), nrow = n, ncol = 2)
d = as.matrix(dist(coords))
cmod = cmod_std(model = "exponential", psill = 1, r = 1)
evaluate(cmod, d)
# }