spTimer: Spatio-Temporal Bayesian Modeling UsingR

Abstract

Hierarchical Bayesian modeling of large point-referenced space-time data is increasingly becoming feasible in many environmental applications due to the recent advances in both statistical methodology and computation power. Implementation of these methods using the Markov chain Monte Carlo (MCMC) computational techniques, however, requires development of problem-specific and user-written computer code, possibly in a low-level language. This programming requirement is hindering the widespread use of the Bayesian model-based methods among practitioners and, hence there is an urgent need to develop high-level software that can analyze large data sets rich in both space and time.This paper develops the package spTimer for hierarchical Bayesian modeling of stylized environmental space-time monitoring data as a contributed software package in the R language that is fast becoming a very popular statistical computing platform. The package is able to fit, spatially and temporally predict large amounts of space-time data using three recently developed Bayesian models. The user is given control over many options regarding covariance function selection, distance calculation, prior selection and tuning of the implemented MCMC algorithms, although suitable defaults are provided. The package has many other attractive features such as on the fly transformations and an ability to spatially predict temporally aggregated summaries on the original scale, which saves the problem of storage when using MCMC methods for large datasets. A simulation example, with more than a million observations, and a real life data example are used to validate the underlying code and to illustrate the software capabilities.

Spatially continuous dynamic factor modeling with basis expansion usingL2penalized likelihood

“…4), and contain 15 missing values. The dataset is available in the "spTimer"-package of R software [20]. The data were also log-transformed to normalize them.…”

Spatio-temporal models for generating a map of high resolution NO2 level

“…The full conditional distributions of the parameters are provided by Bakar and Sahu (2015). That are, the full conditional distribution of β can be obtained by: π(β|..., z) ∼ N ( χ, ),where…”

“…We use the median of the MCMC samples and the lengths of the 95% intervals to summarize the predictions. Further details regarding these predictions methods can be found in the articles by Bakar and Sahu (2015) for the GP model and Sahu and Mardia (2005) for the AR model respectively.…”

Software for Spatial Statistics

“…Sigrist, Künsch, and Stahel (2015) present a modeling approach where the data are assumed to come from a stochastic advection-diffusion process. Bakar and Sahu (2015) deal with a Bayesian approach to modeling spatio-temporal data. Brown (2015) presents interfaces that simplify the use of Bayesian methods, using MCMC or Laplace approximations, based on other packages.…”