cv.spcrglm {spcr} | R Documentation |
Cross-validation for spcr-glm
Description
This function performs cross-validation for SPCR-glm. cv.spcrglm
enables us to determine two regularization parameters \lambda_\beta
and \lambda_\gamma
objectively.
Usage
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 |
xi |
The elastic net mixing parameter with |
nfolds |
The number of folds. The default is 5. |
adaptive |
If |
q |
The tuning parameter that controls weights in aSPCR-glm. The default is 1. |
center |
If |
scale |
If |
lambda.B.length |
The number of candidates for the parameter |
lambda.gamma.length |
The number of candidates for the parameter |
lambda.B |
Optional user-supplied candidates for the parameter |
lambda.gamma |
Optional user-supplied candidates for the parameter |
Value
lambda.gamma.seq |
The values of |
lambda.B.seq |
The values of |
CV.mat |
Matrix of the mean values of cross-validation. The row shows a sequence of |
lambda.gamma.cv |
The value of |
lambda.B.cv |
The value of |
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
# 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