Nothing
## ----include = FALSE----------------------------------------------------------
# Store user's options()
old_options <- options()
library(knitr)
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.align = "center",
fig.retina = 2,
out.width = "85%",
dpi = 96
# pngquant = "--speed=1"
)
options(width = 80)
## ----eval=FALSE---------------------------------------------------------------
# # Option 1: Install stable version from CRAN
# install.packages("tlars")
#
# # Option 2: install developer version from GitHub
# install.packages("devtools")
# devtools::install_github("jasinmachkour/tlars")
## ----eval=FALSE---------------------------------------------------------------
# # Option 1: Install stable version from CRAN
# install.packages("TRexSelector")
#
# # Option 2: install developer version from GitHub
# install.packages("devtools")
# devtools::install_github("jasinmachkour/TRexSelector")
## ----eval=FALSE---------------------------------------------------------------
# library(TRexSelector)
# help(package = "TRexSelector")
# ?trex
# ?random_experiments
# ?lm_dummy
# ?add_dummies
# ?add_dummies_GVS
# ?FDP
# ?TPP
# # etc.
## ----eval=FALSE---------------------------------------------------------------
# citation("TRexSelector")
## -----------------------------------------------------------------------------
library(TRexSelector)
# Setup
n <- 75 # number of observations
p <- 150 # number of variables
num_act <- 3 # number of true active variables
beta <- c(rep(1, times = num_act), rep(0, times = p - num_act)) # coefficient vector
true_actives <- which(beta > 0) # indices of true active variables
num_dummies <- p # number of dummy predictors (also referred to as dummies)
# Generate Gaussian data
set.seed(123)
X <- matrix(stats::rnorm(n * p), nrow = n, ncol = p)
y <- X %*% beta + stats::rnorm(n)
## -----------------------------------------------------------------------------
# Seed
set.seed(1234)
# Numerical zero
eps <- .Machine$double.eps
# Variable selection via T-Rex
res <- trex(X = X, y = y, tFDR = 0.05, verbose = FALSE)
selected_var <- which(res$selected_var > eps)
paste0("True active variables: ", paste(as.character(true_actives), collapse = ", "))
paste0("Selected variables: ", paste(as.character(selected_var), collapse = ", "))
## -----------------------------------------------------------------------------
# Computations might take up to 10 minutes... Please wait...
# Numerical zero
eps <- .Machine$double.eps
# Seed
set.seed(1234)
# Setup
n <- 100 # number of observations
p <- 150 # number of variables
# Parameters
num_act <- 10 # number of true active variables
beta <- rep(0, times = p) # coefficient vector (all zeros first)
beta[sample(seq(p), size = num_act, replace = FALSE)] <- 1 # coefficient vector (active variables with non-zero coefficients)
true_actives <- which(beta > 0) # indices of true active variables
tFDR_vec <- c(0.1, 0.15, 0.2, 0.25) # target FDR levels
MC <- 100 # number of Monte Carlo runs per stopping point
# Initialize results vectors
FDP <- matrix(NA, nrow = MC, ncol = length(tFDR_vec))
TPP <- matrix(NA, nrow = MC, ncol = length(tFDR_vec))
# Run simulations
for (t in seq_along(tFDR_vec)) {
for (mc in seq(MC)) {
# Generate Gaussian data
X <- matrix(stats::rnorm(n * p), nrow = n, ncol = p)
y <- X %*% beta + stats::rnorm(n)
# Run T-Rex selector
res <- trex(X = X, y = y, tFDR = tFDR_vec[t], verbose = FALSE)
selected_var <- which(res$selected_var > eps)
# Results
FDP[mc, t] <- length(setdiff(selected_var, true_actives)) / max(1, length(selected_var))
TPP[mc, t] <- length(intersect(selected_var, true_actives)) / max(1, length(true_actives))
}
}
# Compute estimates of FDR and TPR by averaging FDP and TPP over MC Monte Carlo runs
FDR <- colMeans(FDP)
TPR <- colMeans(TPP)
## ----FDR_and_TPR, echo=FALSE, fig.align='center', message=FALSE, fig.width=12, fig.height=5, out.width = "95%"----
# Plot results
library(ggplot2)
library(patchwork)
tFDR_vec_percent <- 100 * tFDR_vec
plot_data <- data.frame(tFDR_vec = tFDR_vec_percent,
FDR = 100 * FDR,
TPR = 100 * TPR) # data frame containing data to be plotted (FDR and TPR in %)
# FDR vs. tFDR
FDR_vs_tFDR <-
ggplot(plot_data, aes(x = tFDR_vec_percent, y = FDR)) +
labs(x = "Target FDR",
y = "FDR") +
scale_x_continuous(breaks = tFDR_vec_percent, minor_breaks = c(), limits = c(tFDR_vec_percent[1], tFDR_vec_percent[length(tFDR_vec_percent)])) +
scale_y_continuous(breaks = seq(0, 100, by = 10), minor_breaks = c(), limits = c(0, 100)) +
geom_line(size = 1.5, colour = "#336C68") +
geom_abline(slope = 1, colour = "red", linetype = 2, size = 1) +
geom_point(size = 2.5, colour = "#336C68") +
theme_bw(base_size = 16) +
theme(panel.background = element_rect(fill = "white", color = "black", size = 1)) +
coord_fixed(ratio = 0.75 * (tFDR_vec_percent[length(tFDR_vec_percent)] - tFDR_vec_percent[1]) / (100 - 0))
# TPR vs. tFDR
TPR_vs_tFDR <-
ggplot(plot_data, aes(x = tFDR_vec_percent, y = TPR)) +
labs(x = "Target FDR",
y = "TPR") +
scale_x_continuous(breaks = tFDR_vec_percent, minor_breaks = c(), limits = c(tFDR_vec_percent[1], tFDR_vec_percent[length(tFDR_vec_percent)])) +
scale_y_continuous(breaks = seq(0, 100, by = 10), minor_breaks = c(), limits = c(0, 100)) +
geom_line(size = 1.5, colour = "#336C68") +
geom_point(size = 2.5, colour = "#336C68") +
theme_bw(base_size = 16) +
theme(panel.background = element_rect(fill = "white", color = "black", size = 1)) +
coord_fixed(ratio = 0.75 * (tFDR_vec_percent[length(tFDR_vec_percent)] - tFDR_vec_percent[1]) / (100 - 0))
FDR_vs_tFDR + TPR_vs_tFDR
## ----T-Rex_framework, echo=FALSE, fig.cap="Figure 1: Simplified overview of the T-Rex framework.", out.width = '85%'----
knitr::include_graphics("./figures/T-Rex_framework.png")
## ----EnlargedPredictorMatrix, echo=FALSE, fig.cap="Figure 2: The enlarged predictor matrix (predictor matrix with dummies).", out.width = '65%'----
knitr::include_graphics("./figures/predictor_matrix_with_dummies.png")
## ----include = FALSE----------------------------------------------------------
# Reset user's options()
options(old_options)
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