LOO_preds {hetGP} | R Documentation |
Leave one out predictions
Description
Provide leave one out predictions, e.g., for model testing and diagnostics. This is used in the method plot available on GP and TP models.
Usage
LOO_preds(model, ids = NULL)
Arguments
model |
|
ids |
vector of indices of the unique design point considered (default to all) |
Value
list with mean and variance predictions at x_i assuming this point has not been evaluated
Note
For TP models, psi
is considered fixed.
References
O. Dubrule (1983), Cross validation of Kriging in a unique neighborhood, Mathematical Geology 15, 687–699.
F. Bachoc (2013), Cross Validation and Maximum Likelihood estimations of hyper-parameters of Gaussian processes with model misspecification, Computational Statistics & Data Analysis, 55–69.
Examples
set.seed(32)
## motorcycle data
library(MASS)
X <- matrix(mcycle$times, ncol = 1)
Z <- mcycle$accel
nvar <- 1
## Model fitting
model <- mleHomGP(X = X, Z = Z, lower = rep(0.1, nvar), upper = rep(10, nvar),
covtype = "Matern5_2", known = list(beta0 = 0))
LOO_p <- LOO_preds(model)
# model minus observation(s) at x_i
d_mot <- find_reps(X, Z)
LOO_ref <- matrix(NA, nrow(d_mot$X0), 2)
for(i in 1:nrow(d_mot$X0)){
model_i <- mleHomGP(X = list(X0 = d_mot$X0[-i,, drop = FALSE], Z0 = d_mot$Z0[-i],
mult = d_mot$mult[-i]), Z = unlist(d_mot$Zlist[-i]),
lower = rep(0.1, nvar), upper = rep(50, nvar), covtype = "Matern5_2",
known = list(theta = model$theta, k_theta_g = model$k_theta_g, g = model$g,
beta0 = 0))
model_i$nu_hat <- model$nu_hat
p_i <- predict(model_i, d_mot$X0[i,,drop = FALSE])
LOO_ref[i,] <- c(p_i$mean, p_i$sd2)
}
# Compare results
range(LOO_ref[,1] - LOO_p$mean)
range(LOO_ref[,2] - LOO_p$sd2)
# Use of LOO for diagnostics
plot(model)
[Package hetGP version 1.1.6 Index]