enpy_initial_estimates {pense} | R Documentation |
ENPY Initial Estimates for EN S-Estimators
Description
Compute initial estimates for the EN S-estimator using the EN-PY procedure.
Usage
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 = 1e-06,
sparse = FALSE,
ncores = 1L
)
Arguments
x |
|
y |
vector of response values of length |
alpha |
elastic net penalty mixing parameter with |
lambda |
a vector of positive values of penalization levels. |
bdp |
desired breakdown point of the estimator, between 0.05 and 0.5. The actual breakdown point may be slightly larger/smaller to avoid instabilities of the S-loss. |
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 EN-PY algorithm, created with the |
mscale_opts |
options for the M-scale 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. |
Details
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.
References
Cohen Freue, G.V.; Kepplinger, D.; Salibián-Barrera, M.; Smucler, E. Robust elastic net estimators for variable selection and identification of proteomic biomarkers. Ann. Appl. Stat. 13 (2019), no. 4, 2065–2090 doi:10.1214/19-AOAS1269
See Also
Other functions for initial estimates:
prinsens()
,
starting_point()