TOSI (version 0.3.0)

bic.spfac: 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)

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.

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.

Author

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.