The package emdi supports estimating and mapping regional
disaggregated indicators. For estimating these indicators direct estimation
and the Empirical Best Prediction approach by Molina and Rao (2010)
are provided. Estimates of the mean squared error for these methods can be
conducted by using a parametric bootstrap approach (Gonzalez-Manteiga et al. 2008).
Furthermore, a mapping tool for plotting the estimates on their geographic
regions is provided. Point and uncertainty measures as well as diagnostic
tests can be easily extracted to excel.
The two estimation functions are called direct and
ebp. For both functions several methods are available as
estimators.emdi, plot.emdi (only for emdi objects
obtained by function ebp), print.emdi and
summary.emdi. 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).
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. Gonzalez-Manteiga, W. et al. (2008). Bootstrap mean squared error of a small-area EBLUP. Journal of Statistical Computation and Simulation, 78:5, 443-462. 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.