aenet | R Documentation |
Adaptive Elastic-Net
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
)
x |
Data matrix. |
y |
Response vector if |
family |
Model family, can be |
init |
Type of the penalty used in the initial
estimation step. Can be |
alphas |
Vector of candidate |
tune |
Parameter tuning method for each estimation step.
Possible options are |
nfolds |
Fold numbers of cross-validation when |
rule |
Lambda selection criterion when |
ebic.gamma |
Parameter for Extended BIC penalizing
size of the model space when |
scale |
Scaling factor for adaptive weights:
|
lower.limits |
Lower limits for coefficients.
Default is |
upper.limits |
Upper limits for coefficients.
Default is |
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 |
seed |
Random seed for cross-validation fold division. |
parallel |
Logical. Enable parallel parameter tuning or not,
default is |
verbose |
Should we print out the estimation progress? |
List of model coefficients, glmnet
model object,
and the optimal parameter set.
Nan Xiao <https://nanx.me>
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.
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)
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