lin.pred

Functionality for spatio-temporal modeling of large data sets is provided. A Gaussian process in space and time is defined through a stochastic partial differential equation (SPDE). The SPDE is solved in the spectral space, and after discretizing in time and space, a linear Gaussian state space model is obtained. When doing inference, the main computational difficulty consists in evaluating the likelihood and in sampling from the full conditional of the spectral coefficients, or equivalently, the latent space-time process. In comparison to the traditional approach of using a spatio-temporal covariance function, the spectral SPDE approach is computationally advantageous. See Sigrist, Kuensch, and Stahel (2015) <doi:10.1111/rssb.12061> for more information on the methodology. This package aims at providing tools for two different modeling approaches. First, the SPDE based spatio-temporal model can be used as a component in a customized hierarchical Bayesian model (HBM). The functions of the package then provide parameterizations of the process part of the model as well as computationally efficient algorithms needed for doing inference with the HBM. Alternatively, the adaptive MCMC algorithm implemented in the package can be used as an algorithm for doing inference without any additional modeling. The MCMC algorithm supports data that follow a Gaussian or a censored distribution with point mass at zero. Covariates can be included in the model through a regression term.

Fabio Sigrist

spate

Spatio-Temporal Modeling of Large Data Using a Spectral SPDE
Approach

lin.pred function

<dl> <dt>x</dt>
<dd>Covariates in an array of dimensions p x T X N, where p denotes the
 number of covariates, T the number of time points, and N the number of
 spatial points.</dd> <dt>beta</dt>
<dd>Coefficients of covariates in a vector of length p.</dd></dl>

Arguments

Author

Linear predictor. — lin.pred

<dl>

 <dt>x</dt>
<dd>Covariates in an array of dimensions p x T X N, where p denotes the
 number of covariates, T the number of time points, and N the number of
 spatial points.</dd>

 <dt>beta</dt>
<dd>Coefficients of covariates in a vector of length p.</dd>

</dl>

lin.pred: Linear predictor.

Description

Usage

Value

Arguments

Author