Package: convoSPAT 1.2.5

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.

Authors:Mark D. Risser [aut, cre]

convoSPAT_1.2.5.tar.gz
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convoSPAT_1.2.5.tgz(r-4.4-any)convoSPAT_1.2.5.tgz(r-4.3-any)
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convoSPAT.pdf |convoSPAT.html✨
convoSPAT/json (API)

# Install 'convoSPAT' in R:
install.packages('convoSPAT', repos = c('https://markdrisser.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/markdrisser/convospat/issues

Datasets:
  • US.mc.grids - Mixture component grids for the western United States
  • US.prediction.locs - Prediction locations for the western United States
  • USprecip97 - Annual precipitation measurements from the western United States, 1997
  • simdata - Simulated nonstationary dataset

On CRAN:

2.68 score 2 stars 24 scripts 259 downloads 15 exports 51 dependencies

Last updated 7 years agofrom:e09df2afba. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 14 2024
R-4.5-winOKNov 14 2024
R-4.5-linuxOKNov 14 2024
R-4.4-winOKNov 14 2024
R-4.4-macOKNov 14 2024
R-4.3-winOKNov 14 2024
R-4.3-macOKNov 14 2024

Exports:Aniso_fitevaluate_CVf_mc_kernelskernel_covmake_global_loglik1make_global_loglik1_kappamake_global_loglik2make_global_loglik2_kappamake_global_loglik3make_global_loglik3_kappamake_global_loglik4_kappamake_local_likmc_NNSconvo_fitNSconvo_sim

Dependencies:clicolorspaceDBIdotCall64dplyrellipsefansifarverfieldsgenericsgeoRggplot2gluegtableisobandlabelinglatticelifecyclelpSolvemagrittrmapsMASSMatrixmgcvminqamitoolsmunsellnlmenumDerivpillarpkgconfigplotrixproxyR6RColorBrewerRcppRcppArmadillorlangscalesspspamsplancsStatMatchsurveysurvivaltibbletidyselectutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Fit the stationary spatial modelAniso_fit
Evaluation criteriaevaluate_CV
Calculate mixture component kernel matrices.f_mc_kernels
Calculate a kernel covariance matrix.kernel_cov
Constructor functions for global 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 local parameter estimation.make_local_lik
Calculate local sample sizes.mc_N
Fit the nonstationary spatial modelNSconvo_fit
Simulate data from the nonstationary model.NSconvo_sim
Plot of the estimated correlations from the stationary model.plot.Aniso
Plot from the nonstationary model.plot.NSconvo
Obtain predictions at unobserved locations for the stationary spatial model.predict.Aniso
Obtain predictions at unobserved locations for the nonstationary spatial model.predict.NSconvo
Simulated nonstationary datasetsimdata
Summarize the stationary model fit.summary.Aniso
Summarize the nonstationary model fit.summary.NSconvo
Mixture component grids for the western United StatesUS.mc.grids
Prediction locations for the western United StatesUS.prediction.locs
Annual precipitation measurements from the western United States, 1997USprecip97