opts: Optimization via Subsampling (OPTS)

In STOPES: Selection Threshold Optimized Empirically via Splitting

Optimization via Subsampling (OPTS)

Description

opts computes the OPTS MLE in low dimensional case.

Usage

opts(X, Y, m, crit = "aic", prop_split = 0.5, cutoff = 0.75, ...)

Arguments

X	n x p covariate matrix (without intercept)
Y	n x 1 binary response vector
m	number of subsamples
crit	information criterion to select the variables: (a) aic = minimum AIC and (b) bic = minimum BIC
prop_split	proportion of subsample size and sample size, default value = 0.5
cutoff	cutoff used to select the variables using the stability selection criterion, default value = 0.75
...	other arguments passed to the glm function, e.g., family = "binomial"

Value

opts returns a list:

betahat	OPTS MLE of regression parameter vector
Jhat	estimated set of active predictors (TRUE/FALSE) corresponding to the OPTS MLE
SE	standard error of OPTS MLE
freqs	relative frequency of selection for all variables

Examples

require(MASS)
P = 15
N = 100
M = 20
BETA_vector = c(0.5, rep(0.5, 2), rep(0.5, 2), rep(0, P - 5))
MU_vector = numeric(P)
SIGMA_mat = diag(P)

X <- mvrnorm(N, MU_vector, Sigma = SIGMA_mat)
linearPred <- cbind(rep(1, N), X) 
Y <- rbinom(N, 1, plogis(linearPred))

# OPTS-AIC MLE
opts(X, Y, 10, family = "binomial")

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