Description Usage Arguments Details Value References See Also Examples
The function applies an iterative kernel density algorithm for the estimation of a variety of statistical indicators (e.g. mean, median, quantiles, gini) from interval censored data. The estimation of the standard errors is facilitated by a nonparametric bootstrap.
1 2 3 4 
xclass 
interval censored values; factor with ordered factor values,
as in 
classes 
numeric vector of classes; Inf as last value is allowed,
as in 
threshold 
used for the HeadCount Ratio and Poverty Gap, default is 60%
of the median e.g. 
burnin 
burnin sample size, as in 
samples 
sampling iteration size, as in 
bootstrap.se 
if 
b 
number of bootstrap iterations for the estimation of the standard errors 
bw 
bandwidth selector method, defaults to "nrd0", as in

evalpoints 
number of evaluation grid points, as in

adjust 
the user can multiply the bandwidth by a certain factor such
that bw=adjust*bw as in 
custom_indicator 
a list of functions containing the indicators to be
additionally calculated.
Such functions must only depend on the target variable 
upper 
if the upper bound of the upper interval is 
weights 
any kind of survey or design weights that will be used for the weighted estimation of the statistical indicators 
oecd 
weights for equivalized household size 
The statistical indicators are estimated using pseudo samples as
proxy for the interval censored variable. The object resultX
returns the
pseudo samples for each iteration step of the KDEalgorithm.
An object of class "kdeAlgo" that provides estimates for statistical indicators
and optionally, corresponding standard error estimates. Generic
functions such as, print
,
plot
, and summary
have methods that can be used
to obtain further information. See kdeAlgoObject
for a description
of components of objects of class "kdeAlgo".
Walter, P., Weimer, K. (2018). Estimating Poverty and Inequality Indicators
using Interval Censored Income Data from the German Microcensus.
FUBerlin School of Business & Economics, Discussion
Paper.
Gro<c3><9f>, M., U. Rendtel, T. Schmid, S. Schmon, and N. Tzavidis (2017).
Estimating the density of ethnic minorities and aged people in Berlin:
Multivariate
Kernel Density Estimation applied to sensitive georeferenced administrative
data
protected via measurement error. Journal of the Royal Statistical Society:
Series A
(Statistics in Society), 180.
dclass
, print.kdeAlgo
,
plot.kdeAlgo
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  ## Not run:
# Generate data
x=rlnorm(500, meanlog = 8, sdlog = 1)
classes < c(0,500,1000,1500,2000,2500,3000,4000,5000, 6000,8000,10000, 15000,Inf)
xclass < cut(x,breaks=classes)
weights < abs(rnorm(500,0,1))
oecd < rep(seq(1,6.9,0.3),25)
# Estimate statistical indicators with default settings
Indicator < kdeAlgo(xclass = xclass, classes = classes)
# Include custom indicators
Indicator_custom < kdeAlgo(xclass = xclass, classes = classes,
custom_indicator = list(quant5 = function(y, threshold)
{quantile(y, probs = 0.05)}))
# Indclude survey and oecd weights
Indicator_weights < kdeAlgo(xclass = xclass, classes = classes,
weights = weights, oecd = oecd)
## End(Not run)

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