convoSPAT: Convolution-Based Nonstationary Spatial Modeling

Fits convolution-based nonstationary Gaussian process models to point-referenced spatial data. The nonstationary covariance function allows the user to specify the underlying correlation structure and which spatial dependence parameters should be allowed to vary over space: the anisotropy, nugget variance, and process variance. The parameters are estimated via maximum likelihood, using a local likelihood approach. Also provided are functions to fit stationary spatial models for comparison, calculate the Kriging predictor and standard errors, and create various plots to visualize nonstationarity.

AuthorMark D. Risser [aut, cre]
Date of publication2016-10-21 10:32:36
MaintainerMark D. Risser <markdrisser@gmail.com>
LicenseMIT + file LICENSE
Version1.1.1
http://github.com/markdrisser/convoSPAT

View on CRAN

Man pages

Aniso_fit: Fit the stationary spatial model

evaluate_CV: Evaluation criteria

f_mc_kernels: Calculate mixture component kernel matrices.

kernel_cov: Calculate a kernel covariance matrix.

make_aniso_loglik: Constructor functions for local parameter estimation.

make_aniso_loglik_kappa: Constructor functions for local parameter estimation.

make_global_loglik1: Constructor functions for global parameter estimation.

make_global_loglik1_kappa: Constructor functions for global parameter estimation.

make_global_loglik2: Constructor functions for global parameter estimation.

make_global_loglik2_kappa: Constructor functions for global parameter estimation.

make_global_loglik3: Constructor functions for global parameter estimation.

make_global_loglik3_kappa: Constructor functions for global parameter estimation.

make_global_loglik4_kappa: Constructor functions for global parameter estimation.

mc_N: Calculate local sample sizes.

NSconvo_fit: Fit the nonstationary spatial model

NSconvo_sim: Simulate data from the nonstationary model.

plot.Aniso: Plot of the estimated correlations from the stationary model.

plot.NSconvo: Plot from the nonstationary model.

predict.Aniso: Obtain predictions at unobserved locations for the stationary...

predict.NSconvo: Obtain predictions at unobserved locations for the...

simdata: Simulated nonstationary dataset

summary.Aniso: Summarize the stationary model fit.

summary.NSconvo: Summarize the nonstationary model fit.

US.mc.grids: Mixture component grids for the western United States

USprecip97: Annual precipitation measurements from the western United...

US.prediction.locs: Prediction locations for the western United States

Functions

Aniso_fit Man page
evaluate_CV Man page
f_mc_kernels Man page
kernel_cov Man page
make_aniso_loglik Man page
make_aniso_loglik_kappa Man page
make_global_loglik1 Man page
make_global_loglik1_kappa Man page
make_global_loglik2 Man page
make_global_loglik2_kappa Man page
make_global_loglik3 Man page
make_global_loglik3_kappa Man page
make_global_loglik4_kappa Man page
mc_N Man page
NSconvo_fit Man page
NSconvo_sim Man page
plot.Aniso Man page
plot.NSconvo Man page
predict.Aniso Man page
predict.NSconvo Man page
simdata Man page
summary.Aniso Man page
summary.NSconvo Man page
US.mc.grids Man page
USprecip97 Man page
US.prediction.locs Man page

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.