cv.spfac {TOSI} | R Documentation |
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 |
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
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)
[Package TOSI version 0.3.0 Index]