stopes {STOPES}

Selection of Threshold OPtimized Empirically via Splitting (STOPES)

Description

stopes computes the STOPES estimator.

Usage

stopes(x, y, m = 20, prop_split = 0.50, prop_trim = 0.20, q_tail = 0.90)

Arguments

x

n x p covariate matrix

y

n x 1 response vector

m

number of split samples, with default value = 20

prop_split

proportion of data used for training samples, default value = 0.50

prop_trim

proportion of trimming, default prop_trim = 0.20

q_tail

proportion of truncation samples across the split samples, default values = 0.90

Value

stopes returns a list with the STOPE estimates via data splitting using 0.25 method and the PELT method:

beta_stopes

the STOPE estimate via data splitting

J_stopes

the set of active predictors corresponding to STOPES via data splitting

final_cutpoints

the final cutpoint for STOPES

beta_pelt

the STOPE estimate via PELT

J_pelt

the set of active predictors corresponding to STOPES via PELT

final_cutpoints_PELT

the final cutpoint for PELT

quan_NA

test if the vector of trimmed cutpoints has length 0, with 1 if TRUE and 0 otherwise

Author(s)

Marinela Capanu, Mihai Giurcanu, Colin Begg, and Mithat Gonen

Examples

p <- 5
n <- 100
beta <- c(2, 1, 0.5, rep(0, p - 3))
x <- matrix(nrow = n, ncol = p, rnorm(n * p))
y <- rnorm(n) + crossprod(t(x), beta)
stopes(x, y)