inst/doc/dgpsi.R

In dgpsi: Interface to 'dgpsi' for Deep and Linked Gaussian Process Emulations

## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  echo = TRUE,
  eval = FALSE
)

get_article_url <- function(article) {
  pkg_version <- as.character(utils::packageVersion("dgpsi"))
  
  is_dev <- grepl("\\.9000$", pkg_version)
  
  base_url <- if (is_dev) {
    "https://mingdeyu.github.io/dgpsi-R/dev"
  } else {
    "https://mingdeyu.github.io/dgpsi-R"
  }
  
  paste0(base_url, article)
}

## ----eval = FALSE-------------------------------------------------------------
# library(dgpsi)

## ----eval = FALSE-------------------------------------------------------------
# set_seed(9999)

## ----eval = FALSE-------------------------------------------------------------
# f <- function(x) {
#   if (x < 0.5) return(-1)
#   if (x >= 0.5) return(1)
# }

## ----eval = FALSE-------------------------------------------------------------
# X <- seq(0, 1, length = 10)
# Y <- sapply(X, f)

## ----eval = FALSE-------------------------------------------------------------
# m <- dgp(X, Y, depth = 3)

## ----eval = FALSE-------------------------------------------------------------
# summary(m)

## ----eval = FALSE-------------------------------------------------------------
# plot(m)

## ----eval = FALSE-------------------------------------------------------------
# oos_x <- sample(seq(0, 1, length = 200), 10)
# oos_y <- sapply(oos_x, f)

## ----eval = FALSE-------------------------------------------------------------
# plot(m,oos_x,oos_y)

## ----eval = FALSE-------------------------------------------------------------
# test_x <- seq(0, 1, length = 200)
# test_y <- sapply(test_x, f)

## ----eval = FALSE-------------------------------------------------------------
# m <- predict(m, x = test_x)

## ----eval = FALSE-------------------------------------------------------------
# mu <- m$results$mean # extract the predictive means
# sd <- sqrt(m$results$var) # extract the predictive variance and compute the predictive standard deviations
# # compute predictive bounds which are two predictive standard deviations above and below the predictive means
# up <- mu + 2*sd
# lo <- mu - 2*sd
# 
# plot(test_x, mu, type = 'l', lty = 2, lwd = 1.5, col = 'black', xlab = 'x', cex.axis = 1.3, cex.lab = 1.3, ylab = 'y', ylim = c(-1.5,1.5))  # predictive means
# polygon(c(test_x, rev(test_x)), c(up,rev(lo)), col = 'grey80', border = F) # credible interval
# lines(test_x, test_y, type = 'l', col = "#D55E00", lwd = 2) # Underlying truth
# lines(test_x, mu, type = 'l', lty = 2, lwd = 1.5, col = 'black')
# lines(X, Y, type = 'p', pch = 16, cex = 1, col = "#0072B2") # Training data points