shape_Aut

The data contains the borders of 94 Austrian districts and simplifies the loading of the shape file for Austrian districts.
It is originally used for examples in package emdi.

datasets

Mixed Effects Random Forests (MERFs) are a data-driven,
nonparametric alternative to current methods of Small Area Estimation
(SAE). 'SAEforest' provides functions for the estimation of regionally
disaggregated linear and nonlinear indicators using survey sample
data. Included procedures facilitate the estimation of domain-level
economic and inequality metrics and assess associated uncertainty.
Emphasis lies on straightforward interpretation and visualization of results.
From a methodological perspective, the package builds on approaches discussed in
Krennmair and Schmid (2022) <arXiv:2201.10933v2> and Krennmair
et al. (2022) <arXiv:2204.10736>.

Patrick Krennmair

SAEforest

Mixed Effect Random Forests for Small Area Estimation

shape_Aut function

A shape file of class <code><a href="/link/SpatialPolygonsDataFrame?package=SAEforest&version=1.0.0" data-mini-rdoc="SAEforest::SpatialPolygonsDataFrame">SpatialPolygonsDataFrame</a></code> for 94 Austrian districts.<dl>
The main purpose of this function is the visualization of estimation results with the
plotting function map_indicators. Further information on Copyrights is found in the attached
copyright statement.</dl>

Format

Data on shape for Austrian districts — shape_Aut

A shape file of class <code><a href='https://rdrr.io/pkg/sp/man/SpatialPolygons.html'>SpatialPolygonsDataFrame</a></code> for 94 Austrian districts.<dl>
The main purpose of this function is the visualization of estimation results with the
plotting function map_indicators. Further information on Copyrights is found in the attached
copyright statement.

</dl>

shape_Aut: Data on shape for Austrian districts

Description

Usage

Arguments

Format

See Also