riskEst: Computes the relative risk estimation

In localEM: Nonparametric Modelling of Spatial Risk for Areal Disease Data

Description

The riskEst function computes the estimations of the relative risk with high raster resolution.

Usage

riskEst(cases, lemObjects, bw, fact = 1, ncores = 1,
  iterations = list(tol = 1e-05, maxIter = 1000, gpu = FALSE),
  path = getwd(), filename = "lemRisk.grd", verbose = FALSE)

Arguments

cases	Spatial polygons, data frame or vector of case data
lemObjects	List of arrays for the smoothing matrix, and raster stacks for the partition and smoothed offsets
bw	Vector of bandwidths specifying which smoothing matrix in lemObjects to use
fact	Aggregation factor prior to 'final step' smoothing (set to zero to skip final step)
ncores	Number of cores/threads for parallel processing
iterations	List of convergence tolerance, number of iterations, and use of gpuR package for running local-EM recursions
path	Folder for storing rasters
filename	Filename (must have .grd extension) of the risk estimation
verbose	Verbose output

Details

After using the riskEst function, the risk estimations are computed on a fine resolution based on the rasterization of the spatial polygons of population data.

Value

The riskEst function returns a raster brick of risk estimations for the input bandwidths.

Examples

## Not run: 
# case and population data
data('kentuckyCounty')
data('kentuckyTract')

# parameters
ncores = 2
cellsCoarse = 8
cellsFine = 100
bw = c(10, 15, 17.5, 20) * 1000
path = 'example'

# rasters of case and population data
lemRaster = rasterPartition(polyCoarse = kentuckyCounty,
							polyFine = kentuckyTract,
							cellsCoarse = cellsCoarse,
							cellsFine = cellsFine,
							bw = bw,
							ncores = ncores,
							path = path,
							idFile = 'lemId.grd',
							offsetFile = 'lemOffsets.grd',
							verbose = TRUE)

# smoothing matrix
lemSmoothMat = smoothingMatrix(rasterObjects = lemRaster,
								ncores = ncores,
								path = path,
								filename = 'lemSmoothMat.grd',
								verbose = TRUE)

# risk estimation
lemRisk = riskEst(cases = kentuckyCounty[,c('id','count')],
					lemObjects = lemSmoothMat,
					bw = bw,
					ncores = ncores,
					path = path,
					filename = 'lemRisk.grd',
					verbose = TRUE)

# plot risk
rCol = mapmisc::colourScale(lemRisk$riskEst,
						breaks = 5, style = 'quantile', dec = 2)

par(mfrow = c(2,2), mar = c(0.5,0.5,3,0.5))
for(inBw in 1:length(bw)) {
	plot(lemRisk$riskEst[[inBw]],
		main = paste('Risk, bw=', bw[inBw], 'km', sep = ''),
		col = rCol$col, breaks = rCol$breaks,
		axes = FALSE, box = FALSE, legend = FALSE,
		add = FALSE)
}
mapmisc::legendBreaks('right', rCol)

## End(Not run)

localEM documentation built on May 3, 2019, 1:24 p.m.