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MIRL: Multiple Imputation Random Lasso for Variable Selection with Missing Entries
Implements a variable selection and prediction method for high-dimensional data with missing entries following the paper Liu et al. (2016) <doi:10.1214/15-AOAS899>. It deals with missingness by multiple imputation and produces a selection probability for each variable following stability selection. The user can further choose a threshold for the selection probability to select a final set of variables. The threshold can be picked by cross validation or the user can define a practical threshold for selection probability. If you find this work useful for your application, please cite the method paper.
Getting started
Browse package contents
Package details |
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| Author | Ying Liu, Yuanjia Wang, Yang Feng, Melanie M. Wall |
| Maintainer | Ying Liu <summeryingl@gmail.com> |
| License | GPL-2 |
| Version | 1.0 |
| Package repository | View on CRAN |
| Installation |
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