spcrglm | R Documentation |
This function computes a principal component regression for generalized linear models via sparse regularization.
spcrglm(x, y, k, family=c("binomial","poisson","multinomial"), lambda.B, lambda.gamma, w=0.1, xi=0.01, adaptive=FALSE, q=1, center=TRUE, scale=FALSE)
x |
A data matrix. |
y |
A response data. |
k |
The number of principal components. |
family |
Response type. |
lambda.B |
The regularization parameter for the parameter B. |
lambda.gamma |
The regularization parameter for the coefficient vector γ. |
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. |
adaptive |
If |
q |
The tuning parameter that controls weights in aSPCR-glm. The default is 1. |
center |
If |
scale |
If |
loadings.B |
the loading matrix B |
gamma |
the coefficient |
gamma0 |
intercept |
loadings.A |
the loading matrix A |
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.
cv.spcrglm
# binomial n <- 100 np <- 5 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] )))) spcrglm.fit <- spcrglm(x=x, y=y, k=2, family="binomial", lambda.B=2, lambda.gamma=1) spcrglm.fit # Poisson set.seed(4) y <- rpois(n, exp( (nu0[1]*x[ ,1] + nu0[2]*x[ ,2] ) )) spcrglm.fit <- spcrglm(x=x, y=y, k=2, family="poisson", lambda.B=2, lambda.gamma=1) spcrglm.fit # multinomial set.seed(4) y <- sample(1:4, n, replace=TRUE) spcrglm.fit <- spcrglm(x=x, y=y, k=2, family="multinomial", lambda.B=2, lambda.gamma=2) spcrglm.fit
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