prep_car_data2 function

Prepare data for the CAR model: raster analysis

Prepare data for the CAR model: raster analysis

Prepare a list of data required for the CAR model; this is for working with (large) raster data files only. For non-raster analysis, see prep_car_data .

Source

Griffith, Daniel A. (2000). Eigenfunction properties and approximations of selected incidence matrices employed in spatial analyses. Linear Algebra and its Applications 321 (1-3): 95-112. tools:::Rd_expr_doi("10.1016/S0024-3795(00)00031-8") .

prep_car_data2(row = 100, col = 100, quiet = FALSE)

Arguments

  • row: Number of rows in the raster
  • col: Number of columns in the raster
  • quiet: Controls printing behavior. By default, quiet = FALSE and the range of permissible values for the spatial dependence parameter is printed to the console.

Returns

A list containing all of the data elements required by the CAR model in stan_car.

Details

Prepare input data for the CAR model when your dataset consists of observations on a regular (rectangular) tessellation, such as a raster layer or remotely sensed imagery. The rook criteria is used to determine adjacency. This function uses Equation 5 from Griffith (2000) to generate approximate eigenvalues for a row-standardized spatial weights matrix from a P-by-Q dimension regular tessellation.

This function can accommodate very large numbers of observations for use with stan_car; for large N data, it is also recommended to use slim = TRUE or the drop argument. For more details, see: vignette("raster-regression", package = "geostan").

Examples

row = 100 col = 120 car_dl <- prep_car_data2(row = row, col = col)

See Also

prep_sar_data2, prep_car_data, stan_car.

  • Maintainer: Connor Donegan
  • License: GPL (>= 3)
  • Last published: 2024-12-04