Convolution-Based Nonstationary Spatial Modeling
Fit the stationary spatial model
Calculate spatial covariance.
Evaluation criteria
Calculate mixture component kernel matrices.
Calculate a kernel covariance matrix.
Constructor functions for global parameter estimation.
Constructor functions for global parameter estimation.
Constructor functions for global parameter estimation.
Constructor functions for global parameter estimation.
Constructor functions for global parameter estimation.
Constructor functions for global parameter estimation.
Constructor functions for global parameter estimation.
Constructor functions for local parameter estimation.
Calculate local sample sizes.
Fit the nonstationary spatial model
Simulate data from the nonstationary model.
Plot of the estimated correlations from the stationary model.
Plot from the nonstationary model.
Obtain predictions at unobserved locations for the stationary spatial ...
Obtain predictions at unobserved locations for the nonstationary spati...
Summarize the stationary model fit.
Summarize the nonstationary model fit.
Mixture component grids for the western United States
Prediction locations for the western United States
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.