Compute initial estimates for the EN Sestimator using the ENPY procedure.
enpy_initial_estimates( x, y, alpha, lambda, bdp = 0.25, cc, intercept = TRUE, penalty_loadings, enpy_opts = enpy_options(), mscale_opts = mscale_algorithm_options(), eps = 1e06, sparse = FALSE, ncores = 1L )
x 


y  vector of response values of length 
alpha  elastic net penalty mixing parameter with \(0 \le \alpha \le 1\).

lambda  a vector of positive values of penalization levels. 
bdp  desired breakdown point of the estimator, between 0 and 0.5. The actual breakdown point may be slightly larger/smaller to avoid instabilities of the Sloss. 
cc  cutoff value for the bisquare rho function. By default, chosen to yield a consistent estimate for the Normal distribution. 
intercept  include an intercept in the model. 
penalty_loadings  a vector of positive penalty loadings (a.k.a. weights) for different
penalization of each coefficient. Only allowed for 
enpy_opts  options for the ENPY algorithm, created with the 
mscale_opts  options for the Mscale estimation. See 
eps  numerical tolerance. 
sparse  use sparse coefficient vectors. 
ncores  number of CPU cores to use in parallel. By default, only one CPU core is used. Not supported on all platforms, in which case a warning is given. 
If these manually computed initial estimates are intended as starting points for pense()
,
they are by default shared for all penalization levels.
To restrict the use of the initial estimates to the penalty level they were computed for, use
as_starting_point(..., specific = TRUE)
. See as_starting_point()
for details.
Cohen Freue, G.V.; Kepplinger, D.; SalibiánBarrera, M.; Smucler, E. Robust elastic net estimators for variable selection and identification of proteomic biomarkers. Ann. Appl. Stat. 13 (2019), no. 4, 20652090 doi: 10.1214/19AOAS1269
Other functions for initial estimates:
prinsens()
,
starting_point()