spcr (version 2.1.1)

spcrglm: Fit a sparse principal component regression for generalized linear models (SPCR-glm)

Description

This function computes a principal component regression for generalized linear models via sparse regularization.

Usage

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)

Value

loadings.B

the loading matrix B

gamma

the coefficient

gamma0

intercept

loadings.A

the loading matrix A

Arguments

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 \(\gamma\).

w

Weight parameter with \(w \ge 0\). The default is 0.1.

xi

The elastic net mixing parameter with \(0\le \alpha \le 1\). The default is 0.01.

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.

Author

Shuichi Kawano

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

cv.spcrglm

Examples

# 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