High-Dimensional Regression with Measurement Error.
hdme
The goal of hdme is to provide penalized regression methods for High-Dimensional Measurement Error problems (errors-in-variables).
Installation
Install hdme from CRAN using.
install.packages("hdme")
You can install the latest development version from github with:
# install.packages("devtools")
devtools::install_github("osorensen/hdme", build_vignettes = TRUE)
Dependency on Rglpk
hdme uses the Rglpk package, which requires the GLPK library package to be installed. On some platforms this requires a manual installation.
On Debian/Ubuntu, you might use:
sudo apt-get install libglpk-dev
On macOS, you might use:
brew install glpk
Methods
hdme provides implementations of the following algorithms:
The methods implemented in the package include
- Corrected Lasso for Linear Models (Loh and Wainwright (2012))
- Corrected Lasso for Generalized Linear Models (Sorensen, Frigessi, and Thoresen (2015))
- Matrix Uncertainty Selector for Linear Models (Rosenbaum and Tsybakov (2010))
- Matrix Uncertainty Selector for Generalized Linear Models (Sorensen et al. (2018))
- Matrix Uncertainty Lasso for Generalized Linear Models (Sorensen et al. (2018))
- Generalized Dantzig Selector (James and Radchenko (2009))
Contributions
Contributions to hdme are very welcome. If you have a question or suspect you have found a bug, please open an Issue. Code contribution by pull requests are also appreciated.
Citation
If using hdme in a scientific publication, please cite the following paper:
citation("hdme")
#>
#> To cite package 'hdme' in publications use:
#>
#> Sorensen, (2019). hdme: High-Dimensional Regression with Measurement
#> Error. Journal of Open Source Software, 4(37), 1404,
#> https://doi.org/10.21105/joss.01404
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Article{,
#> title = {hdme: High-Dimensional Regression with Measurement Error},
#> journal = {Journal of Open Source Software},
#> volume = {4},
#> number = {37},
#> pages = {1404},
#> year = {2019},
#> doi = {10.21105/joss.01404},
#> author = {Oystein Sorensen},
#> }
References
James, Gareth M., and Peter Radchenko. 2009. “A Generalized Dantzig Selector with Shrinkage Tuning.” Biometrika 96 (2): 323–37.
Loh, Po-Ling, and Martin J. Wainwright. 2012. “High-Dimensional Regression with Noisy and Missing Data: Provable Guarantees with Nonconvexity.” Ann. Statist. 40 (3): 1637–64.
Rosenbaum, Mathieu, and Alexandre B. Tsybakov. 2010. “Sparse Recovery Under Matrix Uncertainty.” Ann. Statist. 38 (5): 2620–51.
Sorensen, Oystein, Arnoldo Frigessi, and Magne Thoresen. 2015. “Measurement Error in Lasso: Impact and Likelihood Bias Correction.” Statistica Sinica 25 (2): 809–29.
Sorensen, Oystein, Kristoffer Herland Hellton, Arnoldo Frigessi, and Magne Thoresen. 2018. “Covariate Selection in High-Dimensional Generalized Linear Models with Measurement Error.” Journal of Computational and Graphical Statistics 27 (4): 739–49. https://doi.org/10.1080/10618600.2018.1425626.