Fixed Rank Kriging
Automatic BAU generation
Basis-function data frame object
Basis functions
Generic basis-function constructor
Creates pixels around points
Retrieve fit information for SRE model
manifold
Construct a set of local basis functions
(Deprecated) Retrieve log-likelihood
manifold
Retrieve manifold
measure
Number of basis functions
Return the number of resolutions
Observed (or unobserved) BAUs
FRK options
plane
Uncertainty quantification of the fixed effects
Automatic basis-function placement
Combine basis functions
Convert data frame to SpatialPolygons
Distance Matrix Computation from Two Matrices
Compute distance
Pre-configured distances
Draw a map of the world with country boundaries.
Evaluate basis functions
Plot a Spatial*DataFrame or STFDF object
Plot predictions from FRK analysis
Plotting themes
real line
Removes basis functions
Show basis functions
SpatialPolygonsDataFrame to df
sphere
Spatial Random Effects class
Deprecated: Please use predict
Construct SRE object, fit and predict
plane in space-time
Space-time sphere
Tensor product of basis functions
Type of manifold
A tool for spatial/spatio-temporal modelling and prediction with large datasets. The approach models the field, and hence the covariance function, using a set of basis functions. This fixed-rank basis-function representation facilitates the modelling of big data, and the method naturally allows for non-stationary, anisotropic covariance functions. Discretisation of the spatial domain into so-called basic areal units (BAUs) facilitates the use of observations with varying support (i.e., both point-referenced and areal supports, potentially simultaneously), and prediction over arbitrary user-specified regions. `FRK` also supports inference over various manifolds, including the 2D plane and 3D sphere, and it provides helper functions to model, fit, predict, and plot with relative ease. Version 2.0.0 and above also supports the modelling of non-Gaussian data (e.g., Poisson, binomial, negative-binomial, gamma, and inverse-Gaussian) by employing a generalised linear mixed model (GLMM) framework. Zammit-Mangion and Cressie <doi:10.18637/jss.v098.i04> describe `FRK` in a Gaussian setting, and detail its use of basis functions and BAUs, while Sainsbury-Dale, Zammit-Mangion, and Cressie <doi:10.18637/jss.v108.i10> describe `FRK` in a non-Gaussian setting; two vignettes are available that summarise these papers and provide additional examples.
Useful links