doubleLassoSelect function is an alternative implementation of Double Lasso Selection on OLS. This implementation is based primarily on package
glmet and a paper of O. Urminsky, C. Hansen, and V. Chernozhukov (working draft), as listed in the reference.
For original implementation, please see
hdm package by Chernozhukov, Hansen, and Spindler.
About the mathematical details of the method, please refer to the original papers listed in the reference section.
rlassoEffects function in the original
doubleLassoSelect function in
dlsr provides an alternative implementation of this specific method with the following benefits:
doubleLassoSelect function accepts character vectors as variable input, instead of matrix indices or logical vectors. This improves code readability and facilitates batch implementation with external data source such as a csv.
It Supports interaction terms as variable input. The
doubleLassoSelect handles the matrix expansion for you.
Instead of the result of a linear model, the
doubleLassoSelect outputs a data frame (data.table) with the selected variables. This provides users more flexibility in subsequent operations, for example, applying the selected result further in a latent class model.
Maintainer: Chih-Yu Chiang firstname.lastname@example.org
A. Belloni, D. Chen, V. Chernozhukov, and C. Hansen (2012).Sparse models and methods for optimal instruments with an application to eminent domain. Econometrica 80 (6), 2369-2429.
A. Belloni, V. Chernozhukov, and C. Hansen (2013). Inference for high-dimensional sparse econometricmodels. InAdvancesinEconomicsandEconometrics: 10thWorldCongress,Vol. 3: Econometrics, Cambirdge University Press: Cambridge, 245-295.
A. Belloni, V. Chernozhukov, and C. Hansen (2014). Inference on treatment effects after selection among high-dimensional controls. The Review of Economic Studies 81(2), 608-650.
O. Urminsky, C. Hansen, and V. Chernozhukov (working draft). Using Double-Lasso Regression for Principled Variable Selection.
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