emdi {emdi}

A package for estimating and mapping disaggregated indicators

Description

The package emdi supports estimating and mapping regional disaggregated indicators. For estimating these indicators, direct estimation, the unit-level Empirical Best Prediction approach by Molina and Rao (2010), the extension for data under informative selection by Guadarrama et al. (2018), the area-level model by Fay and Herriot (1979) and various extensions of it (adjusted variance estimation methods, log and arcsin transformation, spatial, robust and measurement error models) are provided. Depending on the particular method, analytical, bootstrap and jackknife MSE estimation approaches are implemented. 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. Additionally, for the area-level models a stepwise variable selection function, benchmarking options and spatial correlation tests are provided.

Details

The three estimation functions are called direct, ebp and fh. For all functions, several methods are available as estimators.emdi and emdi_summaries. For a full list, please see emdiObject. Furthermore, functions map_plot and write.excel help to visualize and export results. An overview of all currently provided functions can be requested by library(help=emdi).

References

Battese, G.E., Harter, R.M. and Fuller, W.A. (1988). An Error-Components Model for Predictions of County Crop Areas Using Survey and Satellite Data. Journal of the American Statistical Association, Vol.83, No. 401, 28-36.

Fay, R. E. and Herriot, R. A. (1979), Estimates of income for small places: An application of James-Stein procedures to census data, Journal of the American Statistical Association 74(366), 269-277.

Kreutzmann, A., Pannier, S., Rojas-Perilla, N., Schmid, T., Templ, M. and Tzavidis, N. (2019). The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators, Journal of Statistical Software, Vol. 91, No. 7, 1–33, <doi:10.18637/jss.v091.i07>

Molina, I. and Rao, J.N.K. (2010). Small area estimation of poverty indicators. The Canadian Journal of Statistics, Vol. 38, No.3, 369-385. Guadarrama, M., Molina, I. and Rao, J.N.K. (2018). Small area estimation of general parameters under complex sampling designs. Computational Statistics & Data Analysis, Vol. 121, 20-40.