cv.spcr: Cross-validation for spcr

In spcr: Sparse Principal Component Regression

Cross-validation for spcr

Description

This function performs cross-validation for spcr. cv.spcr enables us to determine two regularization parameters λ_β and λ_γ objectively.

Usage

cv.spcr(x, y, k, w=0.1, xi=0.01, nfolds=5, adaptive=FALSE, 
	center=TRUE, scale=FALSE, lambda.B.length=10, lambda.gamma.length=10,
	lambda.B=NULL, lambda.gamma=NULL)

Arguments

x	A data matrix.
y	A response vector.
k	The number of principal components.
w	Weight parameter with 0≤ w ≤ 1. The default is 0.1.
xi	The elastic net mixing parameter with 0≤ α ≤ 1. The default is 0.01.
nfolds	The number of folds. The default is 5.
adaptive	If "TRUE", the adaptive SPCR is used.
center	If "TRUE", the data matrix is centered.
scale	If "TRUE", the data matrix is scaled.
lambda.B.length	The number of candidates for the parameter λ_β. The default is 10.
lambda.gamma.length	The number of candidates for the parameter λ_γ. The default is 10.
lambda.B	Optional user-supplied candidates for the parameter λ_β. The default is NULL.
lambda.gamma	Optional user-supplied candidates for the parameter λ_γ. The default is NULL.

Value

lambda.gamma.seq	The values of lambda.gamma in the fit.
lambda.B.seq	The values of lambda.B in the fit.
CV.mat	Matrix of the mean values of cross-validation. The row shows a sequence of lambda.gamma. The column shows a sequence of lambda.B.
lambda.gamma.cv	The value of lambda.gamma selected by cross-validation.
lambda.B.cv	The value of lambda.B selected by cross-validation.
cvm	The minimum of the mean cross-validated error.

Author(s)

Shuichi Kawano
[e-mail]

References

Kawano, S., Fujisawa, H., Takada, T. and Shiroishi, T. (2015). Sparse principal component regression with adaptive loading. Compuational Statistics & Data Analysis, 89, 192–203.

See Also

spcr

Examples

#data
n <- 50
np <- 5
set.seed(1)
nu0 <- c(-1, 1)
x <- matrix( rnorm(np*n), n, np )
e <- rnorm(n)
y <- nu0[1]*x[ ,1] + nu0[2]*x[ ,2] + e

#fit
cv.spcr.fit <- cv.spcr(x=x, y=y, k=2)
cv.spcr.fit

#fit (adaptive SPCR)
cv.adaspcr.fit <- cv.spcr(x=x, y=y, k=2, adaptive=TRUE)
cv.adaspcr.fit

spcr documentation built on Oct. 16, 2022, 9:05 a.m.