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. 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.
|Author||Fabio Sigrist, Hans R. Kuensch, Werner A. Stahel|
|Date of publication||2016-08-29 19:29:37|
|Maintainer||Fabio Sigrist <firstname.lastname@example.org>|
cols: Function that returns the color scale for 'image()'.
ffbs: Forward Filtering Backward Sampling algorithm.
ffbs.spectral: Forward Filtering Backward Sampling algorithm in the spectral...
get.propagator: Propagator matrix G.
get.propagator.vec: Propagator matrix G in vector form.
get.real.dft.mat: Matrix applying the two-dimensional real Fourier transform.
hist.post.dist: Histogram of posterior distributions.
index.complex.to.real.dft: Auxilary function for the real Fourier transform.
innov.spec: Spectrum of the innovation term epsilon.
lin.pred: Linear predictor.
loglike: Log-likelihood of the hyperparameters.
map.obs.to.grid: Maps non-gridded data to a grid.
matern.spec: Spectrum of the Matern covariance function.
mcmc.summary: Summary function for MCMC output.
Palpha: Prior for direction of anisotropy in diffusion parameter...
Pgamma: Prior for amount of anisotropy in diffusion parameter gamma.
Plambda: Prior for transformation parameter of the Tobit model.
plot.spateMCMC: Plot fitted spateMCMC objects.
plot.spateSim: Plotting function for 'spateSim' objects.
Pmux: Prior for y-component of drift.
Pmuy: Prior for y-component of drift.
Prho0: Prior for range parameter rho0 of innovation epsilon.
Prho1: Prior for range parameter rho1 of diffusion.
print.spateMCMC: Print function for spateMCMC objects.
print.spateSim: Print function for 'spateSim' objects.
propagate.spectral: Function that propagates a state (spectral coefficients).
Psigma2: Prior for for variance parameter sigma2 of innovation...
Ptau2: Prior for nugget effect parameter tau2.
Pzeta: Prior for damping parameter zeta.
real.fft: Fast calculation of the two-dimensional real Fourier...
real.fft.TS: Fast calculation of the two-dimensional real Fourier...
sample.four.coef: Sample from the full conditional of the Fourier coefficients.
spate.init: Constructor for 'spateFT' object which are used for the...
spate.mcmc: MCMC algorithm for fitting the model.
spateMCMC: 'spateMCMC' object output obtained from 'spate.mcmc'.
spateMLE: Maximum likelihood estimate for SPDE model with Gaussian...
spate-package: Spatio-temporal modeling of large data with the spectral SPDE...
spate.plot: Plot a spatio-temporal field.
spate.predict: Obtain samples from predictive distribution in space and...
spate.sim: Simulate from the SPDE.
summary.spateSim: Summary function for 'spateSim' objects.
tobit.lambda.log.full.cond: Full conditional for transformation parameter lambda.
trace.plot: Trace plots for MCMC output analysis.
TSmat.to.vect: Converts a matrix stacked vector.
vect.to.TSmat: Converts a stacked vector into matrix.
vnorm: Eucledian norm of a vector
wave.numbers: Wave numbers.