dshape3dProp: 3d Kernel density estimation for data classified in polygons...

In Kernelheaping: Kernel Density Estimation for Heaped and Rounded Data

Description

3d Kernel density estimation for data classified in polygons or shapes

Usage

dshape3dProp(
  data,
  burnin = 2,
  samples = 5,
  shapefile,
  gridsize = 200,
  boundary = FALSE,
  deleteShapes = NULL,
  fastWeights = TRUE,
  numChains = 1,
  numThreads = 1
)

Arguments

data	data.frame with 5 columns: x-coordinate, y-coordinate (i.e. center of polygon) and number of observations in area for partial population and number of observations for complete observations and third variable (numeric).
burnin	burn-in sample size
samples	sampling iteration size
shapefile	shapefile with number of polygons equal to nrow(data) / length(unique(data[,5]))
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)

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