convoSPAT: Convolution-Based Nonstationary Spatial Modeling

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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.

Author
Mark D. Risser [aut, cre]
Date of publication
2016-10-21 10:32:36
Maintainer
Mark D. Risser <markdrisser@gmail.com>
License
MIT + file LICENSE
Version
1.1.1
URLs

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

Files in this package

convoSPAT
convoSPAT/NAMESPACE
convoSPAT/data
convoSPAT/data/USpredictionlocs.RData
convoSPAT/data/simdata.RData
convoSPAT/data/USprecip97.RData
convoSPAT/data/USmcgrids.RData
convoSPAT/R
convoSPAT/R/convoSPAT_simulate.R
convoSPAT/R/convoSPAT_summplot.R
convoSPAT/R/convoSPAT_fitpred.R
convoSPAT/R/convoSPAT_paramEst.R
convoSPAT/R/DataDocumentation.R
convoSPAT/MD5
convoSPAT/DESCRIPTION
convoSPAT/man
convoSPAT/man/make_global_loglik2_kappa.Rd
convoSPAT/man/NSconvo_sim.Rd
convoSPAT/man/predict.NSconvo.Rd
convoSPAT/man/Aniso_fit.Rd
convoSPAT/man/predict.Aniso.Rd
convoSPAT/man/make_global_loglik1_kappa.Rd
convoSPAT/man/mc_N.Rd
convoSPAT/man/kernel_cov.Rd
convoSPAT/man/plot.NSconvo.Rd
convoSPAT/man/NSconvo_fit.Rd
convoSPAT/man/f_mc_kernels.Rd
convoSPAT/man/US.prediction.locs.Rd
convoSPAT/man/simdata.Rd
convoSPAT/man/summary.NSconvo.Rd
convoSPAT/man/summary.Aniso.Rd
convoSPAT/man/evaluate_CV.Rd
convoSPAT/man/plot.Aniso.Rd
convoSPAT/man/make_global_loglik2.Rd
convoSPAT/man/US.mc.grids.Rd
convoSPAT/man/make_global_loglik1.Rd
convoSPAT/man/make_global_loglik3.Rd
convoSPAT/man/make_global_loglik4_kappa.Rd
convoSPAT/man/make_aniso_loglik_kappa.Rd
convoSPAT/man/make_aniso_loglik.Rd
convoSPAT/man/USprecip97.Rd
convoSPAT/man/make_global_loglik3_kappa.Rd
convoSPAT/LICENSE