# Function factory for prediction generator A function factory which generates a function for sampling from the predictive distribution ```r predictionFactory(y, locs0, locs1, covariance, X0 = list2DF(nrow = nrow(locs0)), X1 = list2DF(nrow = nrow(locs1)), ..., cov.args = list(), chol.args = list(), use_spam = TRUE) ``` ## Arguments - `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 `nrow(locs0)` rows. - `X1`: Covariates for new locations. By default corresponds to an empty data.frame() with `nrow(locs1)` rows. - `...`: 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 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`. ## Author(s) Thomas Caspar Fischer ## Examples ```r 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) ```

factory_predict function