In the context of regression with a large number of explanatory variables, Cox and Battey (2017) emphasize that if there are alternative reasonable explanations of the data that are statistically indistinguishable, one should aim to specify as many of these explanations as is feasible. The standard practice, by contrast, is to report a single effective model for prediction. This paper illustrates the R implementation of the new ideas in the package HCmodelSets, using simple reproducible examples and real data. Results of some simulation experiments are also reported.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2019-057.zip
Survival, ClinicalTrials, Econometrics, MachineLearning, SocialSciences
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For attribution, please cite this work as
Hoeltgebaum & Battey, "HCmodelSets: An R Package for Specifying Sets of Well-fitting Models in High Dimensions", The R Journal, 2020
BibTeX citation
@article{RJ-2019-057,
author = {Hoeltgebaum, Henrique and Battey, Heather},
title = {HCmodelSets: An R Package for Specifying Sets of Well-fitting Models in High Dimensions},
journal = {The R Journal},
year = {2020},
note = {https://doi.org/10.32614/RJ-2019-057},
doi = {10.32614/RJ-2019-057},
volume = {11},
issue = {2},
issn = {2073-4859},
pages = {370-379}
}