eusilcA_prox

<p>A data set comprising the row-standardized proximities between the
domains of the <code>eusilcA_smpAgg</code> data set.</p>

datasets

Functions that support estimating, assessing and mapping regional
disaggregated indicators. So far, estimation methods comprise direct estimation,
the model-based unit-level approach Empirical Best Prediction (see "Small area
estimation of poverty indicators" by Molina and Rao (2010) <doi:10.1002/cjs.10051>),
the area-level model (see "Estimates of income for small places: An
application of James-Stein procedures to Census Data" by Fay and Herriot (1979)
<doi:10.1080/01621459.1979.10482505>) and various extensions of it (adjusted variance
estimation methods, log and arcsin transformation, spatial, robust and measurement
error models), as well as their precision estimates. The assessment of the used model
is supported by a summary and diagnostic plots. For a suitable presentation of
estimates, map plots can be easily created. Furthermore, results can easily be
exported to excel. For a detailed description of the package and the methods used
see "The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators"
by Kreutzmann et al. (2019) <doi:10.18637/jss.v091.i07> and the second package vignette
"A Framework for Producing Small Area Estimates Based on Area-Level Models in R".

Soeren Pannier

emdi

Estimating and Mapping Disaggregated Indicators

Sylvia Harmening

Ann-Kristin Kreutzmann

Felix Skarke

Natalia Rojas-Perilla

Nicola Salvati

Timo Schmid

Matthias Templ

Nikos Tzavidis

Nora Würz

eusilcA_prox function

<p>A data set with dimensions number of areas (94) times
number of areas (94). Values lie between <code>0</code> and <code>1</code>. The
respective row sums amount to 1.</p>

Format

Proximity matrix for spatial area-level models — eusilcA_prox

Proximity matrix for spatial area-level models

eusilcA_prox: Proximity matrix for spatial area-level models

Description

Usage

Arguments

Format

Details