Statistically approximating or “emulating” time series model output in parameter space is a common problem in climate science and other fields. There are many packages for spatio-temporal modeling. However, they often lack focus on time series, and exhibit statistical complexity. Here, we present the R package stilt designed for simplified AR(1) time series Gaussian process emulation, and provide examples relevant to climate modelling. Notably absent is Markov chain Monte Carlo estimation – a challenging concept to many scientists. We keep the number of user choices to a minimum. Hence, the package can be useful pedagogically, while still applicable to real life emulation problems. We provide functions for emulator cross-validation, empirical coverage, prediction, as well as response surface plotting. While the examples focus on climate model emulation, the emulator is general and can be also used for kriging spatio-temporal data.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2018-049.zip
gstat, mlegp, spBayes, ramps, spTimer, RandomFields, stilt, fields, maps, spam, dotCall64
Spatial, SpatioTemporal, Bayesian, Multivariate, TimeSeries
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For attribution, please cite this work as
Olson, et al., "The R Journal: Stilt: Easy Emulation of Time Series AR(1) Computer Model Output in Multidimensional Parameter Space", The R Journal, 2018
BibTeX citation
@article{RJ-2018-049,
author = {Olson, Roman and Ruckert, Kelsey L. and Chang, Won and Keller, Klaus and Haran, Murali and An, Soon-Il},
title = {The R Journal: Stilt: Easy Emulation of Time Series AR(1) Computer Model Output in Multidimensional Parameter Space},
journal = {The R Journal},
year = {2018},
note = {https://doi.org/10.32614/RJ-2018-049},
doi = {10.32614/RJ-2018-049},
volume = {10},
issue = {2},
issn = {2073-4859},
pages = {209-225}
}