print.step

<p>further arguments passed to or from other methods.</p>

<p>The elements described in step are printed.</p>

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>.

Soeren Pannier

emdi

Estimating and Mapping Disaggregated Indicators

Sylvia Harmening

Ann-Kristin Kreutzmann

Natalia Rojas-Perilla

Nicola Salvati

Timo Schmid

Matthias Templ

Nikos Tzavidis

Nora W<c3><bc>rz

print.step function

Prints step function results — print.step

Prints step function results

print.step: Prints step function results

Description

Usage

Arguments