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. Additionally, bivariate non-parametric density estimation for rounded data as well as data aggregated on areas is supported.
|Date of publication||2016-04-16 00:09:59|
|Maintainer||Marcus Gross <email@example.com>|
|License||GPL-2 | GPL-3|
createSim.Kernelheaping: Create heaped data for Simulation
dbivr: Bivariate kernel density estimation for rounded data
dclass: Kernel density estimation for classified data
dheaping: Kernel density estimation for heaped data
dshapebivr: Bivariate Kernel density estimation for data classified in...
Kernelheaping: Kernel Density Estimation for Heaped Data
plot.bivrounding: Plot Kernel density estimate of heaped data naively and...
plot.Kernelheaping: Plot Kernel density estimate of heaped data naively and...
sim.Kernelheaping: Simulation of heaping correction method
simSummary.Kernelheaping: Simulation Summary
summary.Kernelheaping: Prints some descriptive statistics (means and quantiles) for...
tracePlots: Plots some trace plots for the rounding, bias and...