hdme
High-Dimensional Regression with Measurement Error
Penalized regression for generalized linear models for
measurement error problems (aka. errors-in-variables). The package
contains a version of the lasso (L1-penalization) which corrects
for measurement error (Sorensen et al. (2015)
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")
Note when installing on macOS
The package Rglpk is suggested when installing hdme. In order to
install Rglpk on macOS, you may need to first install GLPK by
issuing the following statement on the command line:
brew install glpk
Then install Rglpk:
install.packages("Rglpk")
If you are not able to install Rglpk, then please install the
suggested package lpSolveAPI instead, using the command
install.packages("lpSolveAPI")
The functions in hdme that use Rglpk, will switch to lpSolveAPI
automatically if the former is not available.
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))
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). The Institute of Mathematical Statistics: 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). Institute of Statistical Science, Academia Sinica: 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. Taylor & Francis.
News
hdme 0.2.2
Fixed random number seed issue which caused test to fail in R-devel.
hdme 0.2.1
Internal fix to fit_corrected_lasso and cv_corrected_lasso. Got rid of duplicated code by calling the function set_radius.
hdme 0.2.0
Since Rglpk does not install automatically on macOS, this package was moved from Imports to Suggests. In addition, lpSolveAPI was added to Suggests. This means that the package should build also on systems that do not have Rglpk, in particular the versions of macOS on CRAN.
The changes involved adding an optional linear solver from lpSolveAPI in the function musalgorithm().
hdme 0.1.1.9002
Added a plot.gds function, which plots the coefficients estimated by fit_gds.
hdme 0.1.1.9001
Internal adjustment, which removed importing of external packages into the namespace, and instead specified the functions explicitly using ::.
hdme 0.1.1
tidyverse has been removed from Suggests field DESCRIPTION. dplyr and tidyr have been added instead. Similarly, library(tidyverse) in the vignette has been replaced by library(dplyr), library(tidyr), and library(ggplot2).
hdme 0.1.0
hdme is now on CRAN.