cv.spfac: Cross validation for selecting penalty parameters

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

Cross validation for selecting penalty parameters

Description

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

Usage

  cv.spfac(X, lambda1_set, lambda2_set, nfolds=5)

Arguments

X	a n-by-p matrix, the observed data
lambda1_set	a positve vector, the grid for lambda_1.
lambda2_set	a positve vector, the grid for lambda_2.
nfolds	a positve integer, the folds of cross validation.

Value

return a list including following components:

lamcv.min	a 3-dimensional vector, the penalty value for lambda_1 and lambda_2 corresponding to the minimum CV on grids.
lamcvMat	a numeric matrix with three columns named lambda_1, lambda_2 and cv, where each row is corresponding to each grid.
lambda1_set	the used grid for lambda_1.
lambda2_set	the used grid for lambda_2.

Note

nothing

Author(s)

Liu Wei

References

Wei Liu, Huazhen Lin, (2019). Estimation and inference on high-dimensional sparse factor models.

See Also

gsspFactorm.

Examples

  datlist1 <- gendata_Fac(n= 100, p = 300, rho=1)
  X <- datlist1$X
  spfac <- gsspFactorm(X, q=NULL)  # use default values for lambda's.
  assessBsFun(spfac$sphB, datlist1$B0)
  lambda1_set <- seq(0.2, 2, by=0.3)
  lambda2_set <- 1:8
  # select lambda's values using CV method.
  lamList <- cv.spfac(X, lambda1_set, lambda2_set, nfolds=5)
  spfac <- gsspFactorm(X, q=NULL,lamList$lamcv.min[1], lamList$lamcv.min[2])
  assessBsFun(spfac$sphB, datlist1$B0)

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