| factory-predict | R Documentation | 
A function factory which generates a function for sampling from the predictive distribution
predictionFactory(y, locs0, locs1, covariance, X0 = list2DF(nrow = nrow(locs0)),
  X1 = list2DF(nrow = nrow(locs1)), ..., cov.args = list(), chol.args = list(),
  use_spam = TRUE)
y | 
 Observed measurements.  | 
locs0 | 
 Locations at which measurements were obtained.  | 
locs1 | 
 Locations at which to predict.  | 
covariance | 
 Covariance function.  | 
X0 | 
 Covariates. By default corresponds to an empty data.frame() with   | 
X1 | 
 Covariates for new locations. By default corresponds to an empty data.frame() with   | 
... | 
 Additional arguments. Unused.  | 
cov.args | 
 Additional arguments for covariance function.  | 
chol.args | 
 Additional arguments for function used to compute cholesky decomposition.  | 
use_spam | 
 Logical value. If TRUE, use spam to compute predictions.  | 
Returns a function of the form function(n, param), where n corresponds to the number of simulations to run, and param is a parameter vector as returned by optim.
Thomas Caspar Fischer
set.seed(23)
n <- 100
res <- c(20, 20)
locs <- data.frame(x = runif(n), y = runif(n))
locs_new <- expand.grid(x = seq(0, 1, length.out = res[1]),
                        y = seq(0, 1, length.out = res[2]))
range <- 0.3
dmat <- as.matrix(dist(locs))
theta <- c(range, 1, 1, 0, 0)
cov.args <- list()
chol.args <- list()
Sigma <- cov.wendland(h = dmat, theta = theta, cov.args = cov.args)
y <- c(spam::rmvnorm(n = 1, Sigma = Sigma))
predictionFun <- predictionFactory(y = y, locs0 = locs, locs1 = locs_new,
  covariance = cov.wendland, cov.args = cov.args, chol.args = chol.args,
  use_spam = FALSE)
predictions <- predictionFun(n=10, param=theta)
image(x = seq(0, 1, length.out=res[1]),
      y = seq(0, 1, length.out=res[2]),
      z = matrix(apply(predictions,2,mean), res[1], res[2]),
      col = hcl.colors(9, "Blue-Red"),
      breaks = qnorm(seq(0.05, 0.95, 0.1)),
      xlab = "", ylab = "", xaxt = "n", yaxt = "n",
      useRaster = TRUE)
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