Variable Selection for Multiply Imputed Data
Overview
Penalized regression methods, such as lasso and elastic net, are used in many biomedical applications when simultaneous regression coefficient estimation and variable selection is desired. However, missing data complicates the implementation of these methods, particularly when missingness is handled using multiple imputation. Applying a variable selection algorithm on each imputed dataset will likely lead to different sets of selected predictors, making it difficult to ascertain a final active set without resorting to ad hoc combination rules. 'miselect' presents Stacked Adaptive Elastic Net (saenet) and Grouped Adaptive LASSO (galasso) for continuous and binary outcomes, developed by Du et al (2022) doi:10.1080/10618600.2022.2035739. They, by construction, force selection of the same variables across multiply imputed data. 'miselect' also provides cross validated variants of these methods.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- OK14 OK2026-03-10
Documentation
- Help pages
- 11
- Examples that run
- 0%4 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 100%
- Documented parameters
- 75%
- Return-value docs
- 100%
- Help pages citing references
- 36%
- miselect HTML
Downloads
Dependencies
Installing miselect adds no packages beyond those that come with R.
Code & Tests
Datasets
People & History
Previously maintained by Alexander Rix (0.9.0).
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 0.9.2Latest
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- 0.9.02020-03-31
- RR 3.6.0 released · 2019-04-26
Package metadata
- First published
- 2020-03-31
- Total releases
- 2 / 6 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 37 KB / 1 file
- Download size
- 64 KB
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