dshapebivr: Bivariate Kernel density estimation for data classified in...

In Kernelheaping: Kernel Density Estimation for Heaped and Rounded Data

Description

Bivariate Kernel density estimation for data classified in polygons or shapes

Usage

dshapebivr(
  data,
  burnin = 2,
  samples = 5,
  adaptive = FALSE,
  shapefile,
  gridsize = 200,
  boundary = FALSE,
  deleteShapes = NULL,
  fastWeights = TRUE,
  numChains = 1,
  numThreads = 1
)

Arguments

data	data.frame with 3 columns: x-coordinate, y-coordinate (i.e. center of polygon) and number of observations in area.
burnin	burn-in sample size
samples	sampling iteration size
adaptive	TRUE for adaptive kernel density estimation
shapefile	shapefile with number of polygons equal to nrow(data)
gridsize	number of evaluation grid points
boundary	boundary corrected kernel density estimate?
deleteShapes	shapefile containing areas without observations
fastWeights	if TRUE weigths for boundary estimation are only computed for first 10 percent of samples to speed up computation
numChains	number of chains of SEM algorithm
numThreads	number of threads to be used (only applicable if more than one chains)

Value

The function returns a list object with the following objects (besides all input objects):

Mestimates	kde object containing the corrected density estimate
gridx	Vector Grid of x-coordinates on which density is evaluated
gridy	Vector Grid of y-coordinates on which density is evaluated
resultDensity	Matrix with Estimated Density for each iteration
resultX	Matrix of true latent values X estimates

Examples

## Not run: 
library(maptools)

# Read Shapefile of Berlin Urban Planning Areas (download available from:
# https://www.statistik-berlin-brandenburg.de/opendata/RBS_OD_LOR_2015_12.zip)
Berlin <- rgdal::readOGR("X:/SomeDir/RBS_OD_LOR_2015_12.shp") #(von daten.berlin.de)

# Get Dataset of Berlin Population (download available from:
# https://www.statistik-berlin-brandenburg.de/opendata/EWR201512E_Matrix.csv)
data <- read.csv2("X:/SomeDir/EWR201512E_Matrix.csv")

# Form Dataset for Estimation Process
dataIn <- cbind(t(sapply(1:length(Berlin@polygons),
 function(x) Berlin@polygons[[x]]@labpt)), data$E_E65U80)

#Estimate Bivariate Density
Est <- dshapebivr(data = dataIn, burnin = 5, samples = 10, adaptive = FALSE,
                 shapefile = Berlin, gridsize = 325, boundary = TRUE)
## End(Not run)

# Plot Density over Area:
## Not run: breaks <- seq(1E-16,max(Est$Mestimates$estimate),length.out = 20)
image.plot(x=Est$Mestimates$eval.points[[1]],y=Est$Mestimates$eval.points[[2]],
          z=Est$Mestimates$estimate, asp=1, breaks = breaks,
          col =  colorRampPalette(brewer.pal(9,"YlOrRd"))(length(breaks)-1))
plot(Berlin, add=TRUE)
## End(Not run)

Kernelheaping documentation built on Jan. 27, 2022, 1:09 a.m.