Identifying and characterizing extrapolation in multivariate response data
Abstract
Extrapolation is defined as making predictions beyond the range of the data used to estimate a statistical model. In ecological studies, it is not always obvious when and where extrapolation occurs because of the multivariate nature of the data. Previous work on identifying extrapolation has focused on univariate response data, but these methods are not directly applicable to multivariate response data, which are more and more common in ecological investigations. In this paper, we extend previous work that identified extrapolation by applying the predictive variance from the univariate setting to the multivariate case. We illustrate our approach through an analysis of jointly modeled lake nutrients and indicators of algal biomass and water clarity in over 7000 inland lakes from across the Northeast and Mid-west US. In addition, we illustrate novel exploratory approaches for identifying regions of covariate space where extrapolation is more likely to occur using classification and regression trees.
Cite
@article{arxiv.1906.07036,
title = {Identifying and characterizing extrapolation in multivariate response data},
author = {Meridith L Bartley and Ephraim M Hanks and Erin M Schliep and Patricia A Soranno and Tyler Wagner},
journal= {arXiv preprint arXiv:1906.07036},
year = {2019}
}
Comments
28 pages, 2 supplementary files, 6 main figures, 2 supplementary figures, 2 supplementary tables