GeoAdjust (version 2.0.1)

predRes: Predicts model outcomes at new locations.

Description

Predicts model outcomes at new locations.

Usage

predRes(
  obj = NULL,
  predCoords = NULL,
  draws = NULL,
  covariateData = NULL,
  mesh.s = NULL,
  flag = NULL
)

Value

A matrix containing the mean, median,standard deviation and the lower and the upper bounds of 95

Arguments

obj

The optimized core model object returned by estimateModel() function.

predCoords

An sf class POINT object containing the coordinates of the prediction locations. It should contain crs information.

draws

A matrix containing 10.000 sampled values for each covariate effect size and 10.000 sampled values of random effect coefficients for each mesh node. It is one of the elements of the returning output list of estimateModel() function.

covariateData

A list containing the covariate rasters. Each covariate raster should be SpatRast object and contain crs information.

mesh.s

A mesh created based on the country borders.

flag

A value indicating the type of the likelihood that will be used. Pass 0 for Gaussian, 1 for binomial and 2 for Poisson likelihoods.

Examples

# \donttest{
path1 <- system.file("extdata", "exampleInputData.rda", package = "GeoAdjust")
path2 <- system.file("extdata", "exampleMesh.rda", package = "GeoAdjust")
path3 <- system.file("extdata", "geoData.rda", package = "GeoAdjust")
load(path1)
load(path2)
load(path3)
results <- estimateModel(data = exampleInputData,
options = list(random = 1, covariates = 1), priors = list(beta = c(0,1),
range = 114), USpatial = 1, alphaSpatial = 0.05, UNugget = 1, alphaNug = 0.05, n.sims = 1000)
crs_KM = "+units=km +proj=utm +zone=37 +ellps=clrk80 +towgs84=-160,-6,-302,0,0,0,0 +no_defs"
exampleGrid <- gridCountry(admin0 = adm0, res = 5, target_crs = crs_KM)
pred = predRes(obj = results[["obj"]],
predCoords = exampleGrid[["loc.pred"]],
draws = results[["draws"]],
mesh.s = exampleMesh, covariateData = NULL, flag = 1)
# }