The R Journal: accepted article

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Stilt: Easy Emulation of Time Series AR(1) Computer Model Output in Multidimensional Parameter Space PDF download
Roman Olson, Kelsey L. Ruckert, Won Chang, Klaus Keller, Murali Haran and Soon-Il An

Abstract A common problem in climate science and other fields is one of statistically approximating (“emulating”) time series model output in parameter space. 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) timeseries 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.

Received: 2017-11-16; online 2018-12-07, supplementary material, (1.4 Kb)
CRAN packages: gstat, mlegp, spBayes, ramps, spTimer, RandomFields, stilt
CRAN Task Views implied by cited CRAN packages: Spatial, SpatioTemporal, Bayesian, TimeSeries


CC BY 4.0
This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2018-049,
  author = {Roman Olson and Kelsey L. Ruckert and Won Chang and Klaus
          Keller and Murali Haran and Soon-Il An},
  title = {{Stilt: Easy Emulation of Time Series AR(1) Computer Model
          Output in Multidimensional Parameter Space}},
  year = {2018},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2018-049},
  url = {https://journal.r-project.org/archive/2018/RJ-2018-049/index.html}
}