exact_mle: Maximum Likelihood Evaluation using exact method

In ecrc/exageostatR: R Package Demonstrates the R / C Language Interface for Exageostat

Maximum Likelihood Evaluation using exact method

Description

Maximum Likelihood Evaluation using exact method

Usage

exact_mle(
  data = list(x, y, z),
  kernel = c("ugsm-s", "ugsmn-s", "bgsfm-s", "bgspm-s", "tgspm-s", "ugsm-st", "bgsm-st"),
  dmetric = c("euclidean", "great_circle"),
  optimization = list(clb = c(0.001, 0.001, 0.001), cub = c(5, 5, 5), tol = 1e-04,
    max_iters = 100)
)

Arguments

data	A list of x vector (x-dim), y vector (y-dim), and z observation vector
dmetric	A string - distance metric - "euclidean" or "great_circle"
optimization	A list of opt lb values (clb), opt ub values (cub), tol, max_iters
kernel:	string - kernel ("ugsm-s", "ugsmn-s", "bgsfm-s", "bgspm-s", "tgspm-s", "ugsm-st", "bgsm-st")

Value

vector of three values (theta1, theta2, theta3)

Examples

seed <- 0 ## Initial seed to generate XY locs.
sigma_sq <- 1 ## Initial variance.
beta <- 0.1 ## Initial range.
nu <- 0.5 ## Initial smoothness.
dmetric <- "euclidean" ## "euclidean" or "great_circle" distance.
n <- 144 ## The number of locations (n must be a square number, n=m^2).
kernel <- "ugsm-s"
theta <- c(1, 0.1, 0.5)                                     #Params vector.
exageostat_init(hardware = list(ncores = 2, ngpus = 0, ts = 320, lts = 0, pgrid = 1, qgrid = 1)) ## Initiate exageostat instance
data <- simulate_data_exact(kernel, theta, dmetric, n, seed) ## Generate Z observation vector
## Estimate MLE parameters (Exact)
result <- exact_mle(data, kernel, dmetric, optimization = list(clb = c(0.001, 0.001, 0.001), cub = c(5, 5, 5), tol = 1e-4, max_iters = 1))
print(result)
exageostat_finalize() ## Finalize exageostat instance

ecrc/exageostatR documentation built on June 9, 2025, 9:06 p.m.