stopes: Selection of Threshold OPtimized Empirically via Splitting...

In STOPES: Selection Threshold Optimized Empirically via Splitting

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)

STOPES documentation built on May 28, 2022, 1:08 a.m.