## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>", error = TRUE, warning = TRUE)
load("computing_adapense_fits.RData")

## -----------------------------------------------------------------------------
library(pense)

## ----eval=FALSE---------------------------------------------------------------
# library(parallel)
# # If you don't know how many CPU cores are available, first run `detectCores(logical = FALSE)`
# cluster <- makeCluster(2)

## ----include=FALSE------------------------------------------------------------
library(parallel)
USE_CPUS <- Sys.getenv("PENSE_VIGNETTES_MAX_CPUS", unset = "2") |> 
  as.integer()
cluster <- if (isTRUE(USE_CPUS > 1)) {
  makeCluster(USE_CPUS)
} else {
  NULL
}

## -----------------------------------------------------------------------------
set.seed(1234)
x <- matrix(rweibull(50 * 40, 2, 3), ncol = 40)
y <- 1 * x[, 1] - 1.1 * x[, 2] + 1.2 * x[, 3] + rt(nrow(x), df = 2)

## -----------------------------------------------------------------------------
y[1:3] <- 5 * apply(x[1:3, ], 1, max)
x[3:6, 4:6] <- 1.5 * max(x) + abs(rcauchy(4 * 3))

## ----fit_075, eval=FALSE------------------------------------------------------
# set.seed(1234)
# fit_075 <- adapense_cv(x, y, alpha = 0.75, cv_k = 5, cv_repl = 3, cl = cluster)

## ----eval=FALSE, include=FALSE------------------------------------------------
# fit_075_3repl <- fit_075

## ----fit_075_mopt, eval=FALSE-------------------------------------------------
# set.seed(1234)
# fit_075_mopt <- adapense_cv(x, y, alpha = 0.75, cv_k = 5, cv_repl = 3, cl = cluster,
#                             mscale_opts = mscale_algorithm_options(rho = "mopt"))

## ----eval=FALSE, include=FALSE------------------------------------------------
# fit_075_3repl_mopt <- fit_075_mopt

## ----include=FALSE------------------------------------------------------------
fit_075 <- fit_075_3repl

## ----fig.width=7, fig.height=5, fig.cap="Estimated prediction accuracy using 3 replications of 5-fold CV."----
plot(fit_075)

## ----eval=FALSE---------------------------------------------------------------
# set.seed(1234)
# fit_075 <- adapense_cv(x, y, alpha = 0.75, cv_k = 5, cv_repl = 10, cl = cluster)

## ----eval=FALSE, include=FALSE------------------------------------------------
# fit_075_10repl <- fit_075

## ----fig.width=7, fig.height=5, echo=FALSE, fig.cap="Estimated prediction accuracy using 10 replications of 5-fold CV."----
fit_075 <- fit_075_10repl
plot(fit_075)

## -----------------------------------------------------------------------------
summary(fit_075)

## -----------------------------------------------------------------------------
summary(fit_075, lambda = "se", se_mult = 1)

## ----fit_all, eval=FALSE------------------------------------------------------
# set.seed(1234)
# fit_exp_1 <- adapense_cv(x, y, alpha = c(0.75, 1), exponent = 1, cv_k = 5, cv_repl = 10, cl = cluster)
# 
# set.seed(1234)
# fit_exp_2 <- adapense_cv(x, y, alpha = c(0.75, 1), exponent = 2, cv_k = 5, cv_repl = 10, cl = cluster)

## -----------------------------------------------------------------------------
prediction_performance(fit_exp_1, fit_exp_2)

## -----------------------------------------------------------------------------
prediction_performance(fit_exp_1, fit_exp_2, lambda = 'se')

## -----------------------------------------------------------------------------
prediction_performance(fit_exp_1, fit_exp_2, lambda = 'se', se_mult = 2)

## -----------------------------------------------------------------------------
summary(fit_exp_2, alpha = 0.75, lambda = 'se')

## ----eval=FALSE---------------------------------------------------------------
# mae <- function (prediction_errors) {
#   mean(abs(prediction_errors))
# }
# 
# set.seed(1234)
# fit_075_mae <- adapense_cv(x, y, alpha = 0.75, cv_k = 5, cv_repl = 5,
#                            cl = cluster, cv_type = "naive", cv_metric = mae)

## ----eval=FALSE, include=FALSE------------------------------------------------
# save(fit_075_10repl, fit_075_3repl, fit_075_mae, fit_exp_1, fit_exp_2, fit_075_3repl_mopt,
#      file = 'computing_adapense_fits.RData')