aenet: Adaptive Elastic-Net

In road2stat/msaenet: Multi-Step Adaptive Estimation Methods for Sparse Regressions

Adaptive Elastic-Net

Description

Adaptive Elastic-Net

Usage

aenet(
  x,
  y,
  family = c("gaussian", "binomial", "poisson", "cox"),
  init = c("enet", "ridge"),
  alphas = seq(0.05, 0.95, 0.05),
  tune = c("cv", "ebic", "bic", "aic"),
  nfolds = 5L,
  rule = c("lambda.min", "lambda.1se"),
  ebic.gamma = 1,
  scale = 1,
  lower.limits = -Inf,
  upper.limits = Inf,
  penalty.factor.init = rep(1, ncol(x)),
  seed = 1001,
  parallel = FALSE,
  verbose = FALSE
)

Arguments

x	Data matrix.
y	Response vector if family is "gaussian", "binomial", or "poisson". If family is "cox", a response matrix created by Surv.
family	Model family, can be "gaussian", "binomial", "poisson", or "cox".
init	Type of the penalty used in the initial estimation step. Can be "enet" or "ridge".
alphas	Vector of candidate alphas to use in cv.glmnet.
tune	Parameter tuning method for each estimation step. Possible options are "cv", "ebic", "bic", and "aic". Default is "cv".
nfolds	Fold numbers of cross-validation when tune = "cv".
rule	Lambda selection criterion when tune = "cv", can be "lambda.min" or "lambda.1se". See cv.glmnet for details.
ebic.gamma	Parameter for Extended BIC penalizing size of the model space when tune = "ebic", default is 1. For details, see Chen and Chen (2008).
scale	Scaling factor for adaptive weights: weights = coefficients^(-scale).
lower.limits	Lower limits for coefficients. Default is -Inf. For details, see glmnet.
upper.limits	Upper limits for coefficients. Default is Inf. For details, see glmnet.
penalty.factor.init	The multiplicative factor for the penalty applied to each coefficient in the initial estimation step. This is useful for incorporating prior information about variable weights, for example, emphasizing specific clinical variables. To make certain variables more likely to be selected, assign a smaller value. Default is rep(1, ncol(x)).
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 model coefficients, glmnet model object, and the optimal parameter set.

Author(s)

Nan Xiao <https://nanx.me>

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), 1733–1751.

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)

road2stat/msaenet documentation built on March 4, 2024, 10:12 p.m.