lgp {dgpsi}

R Documentation

Linked (D)GP emulator construction

Description

This function constructs a linked (D)GP emulator.

Usage

lgp(struc, B = 50)

Arguments

struc	a list contains L (the number of layers in a systems of computer models) sub-lists, each of which represents a layer and contains (D)GP emulators (represented by instances of S3 class gp or dgp) of computer models. The sub-lists are placed in the list in the same order of the specified computer model system's hierarchy.
B	the number of imputations to produce the predictions. Increase the value to account for more imputation uncertainties. Decrease the value for lower imputation uncertainties but faster predictions. If the system consists only GP emulators, B is set to 1 automatically. Defaults to 50.

Details

See further examples and tutorials at https://mingdeyu.github.io/dgpsi-R/.

Value

An S3 class named lgp that contains a slot called emulator_obj, which is a 'python' object that stores the information for predictions from the linked emulator. The returned lgp object can be used by

• predict() for linked (D)GP predictions.

• validate() for the OOS validation.

• plot() for the validation plots.

Examples

## Not run: 

# load the package and the Python env
library(dgpsi)
init_py()

# model 1
f1 <- function(x) {
  (sin(7.5*x)+1)/2
}
# model 2
f2 <- function(x) {
  2/3*sin(2*(2*x - 1))+4/3*exp(-30*(2*(2*x-1))^2)-1/3
}
# linked model
f12 <- function(x) {
  f2(f1(x))
}

# training data for Model 1
X1 <- seq(0, 1, length = 9)
Y1 <- sapply(X1, f1)
# training data for Model 2
X2 <- seq(0, 1, length = 13)
Y2 <- sapply(X2, f2)

# emulation of model 1
m1 <- gp(X1, Y1, name = "matern2.5", linked_idx = c(1))
# emulation of model 2
m2 <- dgp(X2, Y2, depth = 2, name = "matern2.5")
# assign linking information after the emulation construction
m2 <- set_linked_idx(m2, c(1))

# emulation of the linked model
struc <- combine(list(m1), list(m2))
m_link <- lgp(struc)

# summarizing
summary(m_link)

# prediction
test_x <- seq(0, 1, length = 300)
m_link <- predict(m_link, x = test_x)

# OOS validation
validate_x <- sample(test_x, 20)
validate_y <- sapply(validate_x, f12)
plot(m_link, validate_x, validate_y, style = 2)

# write and read the constructed linked emulator
write(m_link, 'linked_emulator')
m_link <- read('linked_emulator')

## End(Not run)

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