dshape3dProp

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).

data

burnin

samples

shapefile with number of polygons equal to nrow(data) / length(unique(data[,5]))

shapefile

gridsize

boundary corrected kernel density estimate?

boundary

shapefile containing areas without observations

deleteShapes

if TRUE weigths for boundary estimation are only computed for first 10 percent of samples to speed up computation

fastWeights

numChains

number of threads to be used (only applicable if more than one chains)

numThreads

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

In self-reported or anonymised data the user often encounters
heaped data, i.e. data which are rounded (to a possibly different degree
of coarseness). While this is mostly a minor problem in parametric density
estimation the bias can be very large for non-parametric methods such as kernel
density estimation. This package implements a partly Bayesian algorithm treating
the true unknown values as additional parameters and estimates the rounding
parameters to give a corrected kernel density estimate. It supports various
standard bandwidth selection methods. Varying rounding probabilities (depending
on the true value) and asymmetric rounding is estimable as well: Gross, M. and Rendtel, U. (2016) (<doi:10.1093/jssam/smw011>).
Additionally, bivariate non-parametric density estimation for rounded data, Gross, M. et al. (2016) (<doi:10.1111/rssa.12179>),
as well as data aggregated on areas is supported.

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

Description

Usage

Arguments