prinsens | R Documentation |
Compute Principal Sensitivity Components for Elastic Net Regression
prinsens( x, y, alpha, lambda, intercept = TRUE, penalty_loadings, en_algorithm_opts, eps = 1e-06, sparse = FALSE, ncores = 1L, method = deprecated() )
x |
|
y |
vector of response values of length |
alpha |
elastic net penalty mixing parameter with 0 ≤ α ≤ 1.
|
lambda |
optional user-supplied sequence of penalization levels. If given and not |
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 |
en_algorithm_opts |
options for the LS-EN algorithm. See en_algorithm_options for details. |
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. |
method |
defunct. PSCs are always computed for EN estimates. For the PY procedure for unpenalized estimation use package pyinit. |
a list of principal sensitivity components, one per element in lambda
. Each PSC is itself a list
with items lambda
, alpha
, and pscs
.
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
Pena, D., and Yohai, V.J. A Fast Procedure for Outlier Diagnostics in Large Regression Problems. J. Amer. Statist. Assoc. 94 (1999). no. 446, 434–445. doi: 10.2307/2670164
Other functions for initial estimates:
enpy_initial_estimates()
,
starting_point()
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.