cv.spcrglm: Cross-validation for spcr-glm

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/cv.spcrglm.R

Description

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

Usage

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cv.spcrglm(x, y, k, family=c("binomial","poisson","multinomial"), 
	w=0.1, xi=0.01, nfolds=5, adaptive=FALSE, q=1, 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.

family

Response type.

w

Weight parameter with w ≥ 0. 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-glm (aSPCR-glm) is used.

q

The tuning parameter that controls weights in aSPCR-glm. The default is 1.

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
skawano@ai.lab.uec.ac.jp

References

Kawano, S., Fujisawa, H., Takada, T. and Shiroishi, T. (2018). Sparse principal component regression for generalized linear models. Compuational Statistics & Data Analysis, 124, 180–196.

See Also

spcrglm

Examples

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# binomial
n <- 100
np <- 3
nu0 <- c(-1, 1)
set.seed(4)
x <- matrix( rnorm(np*n), n, np )
y <- rbinom(n,1,1-1/(1+exp(  (nu0[1]*x[ ,1] + nu0[2]*x[ ,2]  ))))
cv.spcrglm.fit <- cv.spcrglm(x=x, y=y, k=1, family="binomial")
cv.spcrglm.fit

# Poisson
set.seed(5)
y <- rpois(n, 1)
cv.spcrglm.fit <- cv.spcrglm(x=x, y=y, k=1, family="poisson")
cv.spcrglm.fit

# multinomial
set.seed(4)
y <- sample(1:4, n, replace=TRUE)
cv.spcrglm.fit <- cv.spcrglm(x=x, y=y, k=1, family="multinomial")
cv.spcrglm.fit

spcr documentation built on Sept. 28, 2021, 5:08 p.m.

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