TSrowMaxST: Two-Stage Maximum Row Test method for the loading vectors.
In feiyoung/SpagFainfer: Estimation and inference on high-dimensional group-sparse factor models

Description Usage Arguments Value Note Author(s) References See Also Examples

Conduct the simultaneous inference for a set of loading vectors as for the NUll hypothesises H01, where H01 assume the set of loading vectors are all zeroes.

1	TSrowMaxST(X, G1, alpha=0.05, seed=1, sub.frac=0.5)

`X`	a `n`-by-`p` matrix, the observed data
`G1`	a positive vector with values between 1 and p, the set of H01.
`alpha`	a positive number, the significant level.
`seed`	a non-negative integer, the random seed.
`sub.frac`	a positive number between 0 and 1, the proportion of the sample used in stage I.

return a matrix with class "Max-test", row names 'chiq_test' , and column names 'CriticalValue', 'TestStatistic', 'reject_status', and 'p-value', including all the information about testing.

nothing

Liu Wei

Wei Liu, Huazhen Lin, Jin Liu (2020). Estimation and inference on high-dimensional sparse factor models.

factor, Factorm,simultestentryX

  ### Example
  dat <- gendata(n = 300, p = 500)
  res <- Factorm(dat$X)
  X <- dat$X
  # ex1: H01 is false
  G1 <- 1:10; # all are nonzero loading vectors
  TSrowMaxST(X, G1=G1, alpha=0.05, sub.frac=0.5)
  # ex2: H01 is true
  G1 <- 481:500 # all are zero loading vectors
  TSrowMaxST(X, G1=G1, alpha=0.05, sub.frac=0.5)