inst/doc/computing_adapense.R

In pense: Penalized Elastic Net S/MM-Estimator of Regression

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

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

## -----------------------------------------------------------------------------
library(parallel)
# If you don't know how many CPU cores are available, first run `detectCores(logical = FALSE)`
cluster <- makeCluster(3)

## -----------------------------------------------------------------------------
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

## ---- 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)
#  plot(fit_075)

## ---- 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)

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summary(fit_075)

## -----------------------------------------------------------------------------
summary(fit_075, lambda = "2-se")

## ----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)
#  
#  set.seed(1234)
#  fit_exp_3 <- adapense_cv(x, y, alpha = c(0.75, 1), exponent = 3, cv_k = 5, cv_repl = 10, cl = cluster)

## -----------------------------------------------------------------------------
prediction_performance(fit_exp_1, fit_exp_2, fit_exp_3)

## -----------------------------------------------------------------------------
prediction_performance(fit_exp_1, fit_exp_2, fit_exp_3, lambda = '1-se')

## -----------------------------------------------------------------------------
prediction_performance(fit_exp_1, fit_exp_2, fit_exp_3, lambda = '3-se')

## -----------------------------------------------------------------------------
summary(fit_exp_3, alpha = 0.75, lambda = '3-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_metric = mae)

## ---- eval=FALSE, include=FALSE-----------------------------------------------
#  save(fit_075_10repl, fit_075_3repl, fit_075_mae, fit_exp_1, fit_exp_2, fit_exp_3,
#       file = 'computing_adapense_fits.RData')