bic.spfac {TOSI}

Modified BIC criteria for selecting penalty parameters

Description

Evalute the BIC values on a set of grids of penalty parameters.

Usage

  bic.spfac(X, c1.max= 10, nlamb1=10, C10=4, c2.max=10, nlamb2=10, C20=4)
  

Arguments

X

a n-by-p matrix, the observed data

c1.max

a positve scalar, the maximum of the grids of c1.

nlamb1

a positive integer, the length of grids of penalty parameter lambda1.

C10

a positve scalar, the penalty factor C1 of modified BIC.

c2.max

a positve scalar, the maximum of the grids of c2.

nlamb2

a positive integer, the length of grids of penalty parameter lambda2.

C20

a positve scalar, the penalty factor C2 of modified BIC.

Value

return a list with class named pena_info and BIC, including following components:

lambda1.min

a positive number, the penalty value for lambda1 corresponding to the minimum BIC on grids.

lambda2.min

a positive number, the penalty value for lambda2 corresponding to the minimum BIC on grids.

bic1

a numeric matrix with three columns named c1, lambda1 and bic1, where each row is corresponding to each grid.

bic2

a numeric matrix with three columns named c2, lambda2 and bic2, where each row is corresponding to each grid.

Note

nothing

Author(s)

Liu Wei

References

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

See Also

gsspFactorm.

Examples

  datlist1 <- gendata_Fac(n= 100, p = 500)
  X <- datlist1$X
  spfac <- gsspFactorm(X, q=NULL) # use default values for lambda's.
  assessBsFun(spfac$sphB, datlist1$B0)

  biclist <- bic.spfac(datlist1$X, c2.max=20,nlamb1 = 10) # # select lambda's values using BIC.