MeanMin {TOSI} | R Documentation |
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 |
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
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)
[Package TOSI version 0.3.0 Index]