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An R Package for Generating Covariance Matrices for Maximum-Entropy Sampling from Precipitation Chemistry Data

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Abstract

We present an open-source R package (MESgenCov v 0.1.0) for temporally fitting multivariate precipitation chemistry data and extracting a covariance matrix for use in the MESP (maximum-entropy sampling problem). We provide multiple functionalities for modeling and model assessment. The package is tightly coupled with NADP/NTN (National Atmospheric Deposition Program/National Trends Network) data from their set of 379 monitoring sites, 1978–present. The user specifies the sites, chemicals, and time period desired, fits an appropriate user-specified univariate model for each site and chemical selected, and the package produces a covariance matrix for use by MESP algorithms.

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Notes

  1. National Atmospheric Deposition Program (NRSP-3). 2019. NADP Program Office, Wisconsin State Laboratory of Hygiene, 465 Henry Mall, Madison, WI 53706, USA

  2. Reprinted with the kind permission of the National Atmospheric Deposition Program (NRSP-3). 2019. NADP Program Office, Wisconsin State Laboratory of Hygiene, 465 Henry Mall, Madison, WI 53706, USA.

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Acknowledgments

The authors are very grateful to Dr. Martin Shafer and Robert Larson for helping us gain access to the NADP/NTN data in a convenient form.

Funding

J. Lee was funded by the Air Force Office of Scientific Research (Complex Networks program), FA9550-19-1-0175. H. Al-Thani was funded by the Qatar National Research Fund (Graduate Sponsorship Research Award), GSRA4-2-0526-17114.

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Authors and Affiliations

  1. Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, 48109, USA

    Hessa Al-Thani & Jon Lee

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  1. Hessa Al-Thani
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  2. Jon Lee
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Correspondence to Jon Lee.

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Appendices

Appendix

A NADP/NTN Data Descriptions

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figure ag

B Geographic Split

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C Internal Covariance Matrices Site Lists

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Al-Thani, H., Lee, J. An R Package for Generating Covariance Matrices for Maximum-Entropy Sampling from Precipitation Chemistry Data. SN Oper. Res. Forum 1, 17 (2020). https://doi.org/10.1007/s43069-020-0011-z

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