marginal.test {sdat} | R Documentation |
Conduct the sum-test, max-test and adaptive-test for testing beta = 0 in a linear model y = x^T beta + epsilon.
marginal.test(x, y, num_sim = 5000L)
x |
the predictors, an n by p matrix |
y |
the responses, a vector of length n |
num_sim |
the number of resampling simulations to obtain the null distribution of the test statistic |
See the reference for a detailed description of the method.
marginal.test
returns a self-explanatory named vector.
Zhang, Y., Laber E. B. (2015). Comment on "An adaptive resampling test for detecting the presence of signifficant predictors". Journal of the American Statistical Association, 110(512), 1451-1454.
n <- 100 p <- 10 x <- matrix(rnorm(n * p), n, p) y <- 0.3 * x[, 1] + rnorm(n) result <- marginal.test(x, y) result[1 : 3] # gives p-values of max-test, sum-test and adaptive-test result[4] # gives running time in seconds