predict-bcgpfit-method: 'predict' method for a 'bcgpfit' object
In cbdavis33/bcgp: Bayesian Composite Gaussian Processes

Description Usage Arguments Value See Also Examples

This function computes Bayesian posterior predictions and prediction intervals.

1 2	## S4 method for signature 'bcgpfit' predict(object, newdata = NULL, prob = 0.95, ...)

`object`	a bcgpfit object
`newdata`	Optionally, an nPred x d numeric matrix of new data locations at which to predict. If omitted, the training data matrix is used. If `newdata` is provided, it should be provided on the same scale as the user-provided training data, i.e. do not transform to [0, 1]^d.
`prob`	a single number greater than 0 and less than 1 that specifes the width of the (equal-tailed) posterior prediction interval. For example, `prob = 0.90` specifies that 90% prediction intervals are desired.

An instance of S4 class bcgpfitpred

bcgpmodel bcgpfit posterior_predict

simData <- bcgpsims(composite = TRUE, stationary = FALSE, noise = FALSE,
                    d = 2, decomposition = TRUE)

model <- bcgpmodel(x = simData@training$x, y = simData@training$y,
                   composite = TRUE, stationary = FALSE, noise = FALSE)
fit <- bcgp_sampling(model, scaled = TRUE, cores = 4, nmcmc = 500,
                     burnin = 200)

fit
print(fit, pars = c("beta0", "w", "rhoG", "rhoL"), digits_summary = 3)
predict(fit)