ST {SILM} | R Documentation |
Testing for Sparse Signals
Description
This function implements the method for testing sparse signals in Zhang and Cheng (2017).
Usage
ST(X.f, Y.f, sub.size, test.set, M = 500, alpha = 0.05)
Arguments
X.f |
n times p design matrix. |
Y.f |
Response variable. |
sub.size |
The sub-sample size used for screening. |
test.set |
The set of variables of interest. |
M |
The number of bootstrap replications (default 500). |
alpha |
The nominal level alpha (default 0.05). |
Value
Values of the non-studentized and studentized statistics, and whether the tests get rejected at the level alpha.
References
Zhang, X., and Cheng, G. (2017) Simultaneous Inference for High-dimensional Linear Models, Journal of the American Statistical Association, 112, 757-768.
Examples
## The function is intended for large n and p.
## Use small p here for illustration purpose only.
n <- 100
p <- 10
s0 <- 3
set <- 1:s0
Sigma <- matrix(NA, p, p)
for (i in 1:p) Sigma[i,] <- 0.9^(abs(i-(1:p)))
X <- matrix(rnorm(n*p), n, p)
X <- t(t(chol(Sigma))%*%t(X))
beta <- rep(0,p)
beta[1:s0] <- runif(s0,0,2)
Y <- X%*%beta+rt(n,4)/sqrt(2)
test.set <- (s0+1):p
sub.size <- n*0.3
ST(X, Y, sub.size, test.set)
test.set <- s0:p
ST(X, Y, sub.size, test.set)
[Package SILM version 1.0.0 Index]