We have created the R package ciuupi to compute confidence intervals that utilize uncertain prior information in linear regression. Unlike post-model-selection confidence intervals, the confidence interval that utilizes uncertain prior information (CIUUPI) implemented in this package has, to an excellent approximation, coverage probability throughout the parameter space that is very close to the desired minimum coverage probability. Furthermore, when the uncertain prior information is correct, the CIUUPI is, on average, shorter than the standard confidence interval constructed using the full linear regression model. In this paper we provide motivating examples of scenarios where the CIUUPI may be used. We then give a detailed description of this interval and the numerical constrained optimization method implemented in R to obtain it. Lastly, using a real data set as an illustrative example, we show how to use the functions in ciuupi.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2019-026.zip
Distributions, NumericalMathematics, Optimization
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For attribution, please cite this work as
Mainzer & Kabaila, "The R Journal: ciuupi: An R package for Computing Confidence Intervals that Utilize Uncertain Prior Information", The R Journal, 2019
BibTeX citation
@article{RJ-2019-026,
author = {Mainzer, Rheanna and Kabaila, Paul},
title = {The R Journal: ciuupi: An R package for Computing Confidence Intervals that Utilize Uncertain Prior Information},
journal = {The R Journal},
year = {2019},
note = {https://doi.org/10.32614/RJ-2019-026},
doi = {10.32614/RJ-2019-026},
volume = {11},
issue = {1},
issn = {2073-4859},
pages = {323-336}
}