Nothing
## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## -----------------------------------------------------------------------------
library(owl)
x <- heart$x
y <- heart$y
fit <- owl(x, y, family = "binomial", lambda = "bh")
## ---- fig.cap = "Regularization path for a binomial regression model fit to the heart data set.", fig.width = 6, fig.height = 5----
plot(fit)
## -----------------------------------------------------------------------------
set.seed(924)
x <- bodyfat$x
y <- bodyfat$y
tune <- trainOwl(x,
y,
q = c(0.1, 0.2),
number = 10,
repeats = 3)
## ---- fig.cap = "Model tuning results from Gaussian SLOPE on the bodyfat dataset.", fig.width = 5.5, fig.height = 3----
plot(tune, measure = "mae") # plot mean absolute error
## -----------------------------------------------------------------------------
tune
## ---- fig.cap = "Control of false discovery rate using SLOPE.", fig.width = 4----
# proportion of real signals
q <- seq(0.05, 0.5, length.out = 20)
fdr <- double(length(q))
set.seed(1)
for (i in seq_along(q)) {
n <- 1000
p <- n/2
sigma <- 1
problem <- owl:::randomProblem(n, p, q = q[i], sigma = sigma)
x <- problem$x
y <- problem$y
signals <- problem$nonzero
fit <- owl(x,
y,
lambda = "gaussian",
q = 0.1,
sigma = sigma)
selected_owl <- which(fit$nonzeros)
V <- length(setdiff(selected_owl, signals))
R <- length(selected_owl)
fdr[i] <- V/R
}
library(lattice)
xyplot(fdr ~ q, type = "b", ylab = "FDR",
panel = function(...) {
panel.refline(h = 0.1)
panel.xyplot(...)
})
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