msaenet (version 2.1)

aenet: Adaptive Elastic-Net

Description

Adaptive Elastic-Net

Usage

aenet(x, y, family = c("gaussian", "binomial", "poisson", "multinomial", "cox", "mgaussian"), init = c("enet", "ridge"), nfolds = 5L, alphas = seq(0.05, 0.95, 0.05), gamma = 1, rule = c("lambda.min", "lambda.1se"), seed = 1001, parallel = FALSE, verbose = FALSE)

Arguments

x

Data matrix.

y

Response vector.

family

Response type. See glmnet for details.

init

Type of the penalty used in the initial estimation step. Can be "enet" or "ridge".

nfolds

Fold numbers of cross-validation.

alphas

Vector of candidate alphas to use in cv.glmnet.

gamma

Scaling factor for adaptive weights: weights = coefs^(-gamma).

rule

Model selection criterion, "lambda.min" or "lambda.1se". See cv.glmnet for details.

seed

Random seed for cross-validation fold division.

parallel

Logical. Enable parallel parameter tuning or not, default is FALSE. To enable parallel tuning, load the doParallel package and run registerDoParallel() with the number of CPU cores before calling this function.

verbose

Should we print out the estimation progress?

Value

List of coefficients beta and glmnet model object model.

References

Zou, Hui, and Hao Helen Zhang. (2009). On the Adaptive Elastic-Net with a Diverging Number of Parameters. The Annals of Statistics 37(4), 1733--51.

Examples

dat = msaenet.sim.gaussian(n = 150, p = 500, rho = 0.6,
                           coef = rep(1, 5), snr = 2, p.train = 0.7,
                           seed = 1001)

aenet.fit = aenet(dat$x.tr, dat$y.tr,
                  alphas = seq(0.2, 0.8, 0.2), seed = 1002)

print(aenet.fit)
msaenet.nzv(aenet.fit)
msaenet.fp(aenet.fit, 1:5)
msaenet.tp(aenet.fit, 1:5)
aenet.pred = predict(aenet.fit, dat$x.te)
msaenet.rmse(dat$y.te, aenet.pred)
plot(aenet.fit)