cv.spcrglm | R Documentation |
This function performs cross-validation for SPCR-glm. cv.spcrglm
enables us to determine two regularization parameters λ_β and λ_γ objectively.
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)
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 |
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 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. |
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. |
Shuichi Kawano
skawano@ai.lab.uec.ac.jp
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.
spcrglm
# 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
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