MeanMin: Data splitting-based two-stage minimum mean testing method...

In TOSI: Two-Directional Simultaneous Inference for High-Dimensional Models

Data splitting-based two-stage minimum mean testing method for the mean vector.

Description

Conduct the simultaneous inference for a set of mean components in the the Null hypothesises H02 that assumes the set of mean components exist zero.

Usage

  MeanMin(X, test.set, Nsplit = 5, frac.size=0.5, standardized=FALSE, alpha=0.05, seed=1)

Arguments

X	a n-by-p matrix, the observed data
test.set	a positive vector with values between 1 and p, the set of H02.
Nsplit	a positive integer, the random split times used, default as 5.
frac.size	a positive number between 0 and 1, the proportion of the sample used in stage I.
standardized	a logical value, whether standerdize in stage I.
alpha	a positive number, the significant level.
seed	a non-negative integer, the random seed.

Value

return a vector with names 'CriticalValue', 'TestStatistic', 'reject_status', 'p-value' if Nsplit=1, and 'reject_status' and 'adjusted_p-value' if Nsplit>1.

Note

nothing

Author(s)

Liu Wei

See Also

gendata_Mean, MeanMin

Examples

  ### Example
  n <- 100; p <- 100; i <- 1
  s0 <- 5 # First five components are nonzeros
  rho <- 4; tau <- 1;
  dat1 <- gendata_Mean(n, p, s0, seed=i, rho, tau)
  # ex1: H01 is false
  MeanMin(dat1$X, 1:s0)
  MeanMin(dat1$X, 1:s0, Nsplit=1)
  # ex1: H01 is true
  MeanMin(dat1$X, 1:p)
  MeanMin(dat1$X, 1:p, Nsplit=1)

TOSI documentation built on Feb. 16, 2023, 7:46 p.m.