MultiStep Adaptive ElasticNet
msaenet(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, nsteps = 2L, tune.nsteps = c("max", "ebic", "bic", "aic"), ebic.gamma.nsteps = 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 crossvalidation when 
rule  Lambda selection criterion when 
ebic.gamma  Parameter for Extended BIC penalizing
size of the model space when 
nsteps  Maximum number of adaptive estimation steps.
At least 
tune.nsteps  Optimal step number selection method
(aggregate the optimal model from the each step and compare).
Options include 
ebic.gamma.nsteps  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 crossvalidation fold division. 
parallel  Logical. Enable parallel parameter tuning or not,
default is FALSE. To enable parallel tuning, load the

verbose  Should we print out the estimation progress? 
List of model coefficients, glmnet
model object,
and the optimal parameter set.
Nan Xiao and QingSong Xu. (2015). Multistep adaptive elasticnet: reducing false positives in highdimensional variable selection. Journal of Statistical Computation and Simulation 85(18), 37553765.
dat < msaenet.sim.gaussian( n = 150, p = 500, rho = 0.6, coef = rep(1, 5), snr = 2, p.train = 0.7, seed = 1001 ) msaenet.fit < msaenet( dat$x.tr, dat$y.tr, alphas = seq(0.2, 0.8, 0.2), nsteps = 3L, seed = 1003 ) print(msaenet.fit)#> Call: msaenet(x = dat$x.tr, y = dat$y.tr, alphas = seq(0.2, 0.8, 0.2), #> nsteps = 3L, seed = 1003) #> Df %Dev Lambda #> 1 12 0.8149474 9.517421e+14msaenet.nzv(msaenet.fit)#> [1] 2 3 4 5 35 114 171 312 379 441 464 500#> [1] 8#> [1] 4#> [1] 2.656733plot(msaenet.fit)