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'SAEforest' - Estimating disaggregated indicators using Mixed Effects Random Forests

Source: R/SAEforest.R
SAEforest.Rd

The package SAEforest promotes the use of Mixed Effects Random Forests (MERFs) for applications of Small Area Estimation (SAE). The package effectively combines functions for the estimation of regionally disaggregated linear and nonlinear economic and inequality indicators using survey sample data. Estimated models increase the precision of direct estimates from survey data, combining unit-level and aggregated population level covariate information from census or register data. Apart from point estimates, MSE estimates for requested indicators can be easily obtained. The package provides procedures to facilitate the analysis of model performance of MERFs and visualizes predictive relations from covariates and variable importance. Additionally, users can summarize and map indicators and corresponding measures of uncertainty. Methodological details for the functions in this package are found in Krennmair & Schmid (2022), Krennmair et al. (2022a) and Krennmair et al. (2022b).

Details

This package includes a main function MERFranger that is wrapped in SAEforest_model for an improved SAE workflow. Each function produces an object inheriting requested results of regionally disaggregated point and uncertainty estimates. Additionally, statistical information on model fit and variable importance is accessible through generic functions such as a summary (summary.SAEforest) or a class-specific plot function (plot.SAEforest). For a full documentation of objects of class SAEforest see SAEforestObject. An overview of all currently provided functions within this package can be be seen with help(package="SAEforest").

References

Krennmair, P., & Schmid, T. (2022). Flexible Domain Prediction Using Mixed Effects Random Forests. Journal of Royal Statistical Society: Series C (Applied Statistics) (forthcoming).

Krennmair, P., & Würz, N. & Schmid, T. (2022a). Analysing Opportunity Cost of Care Work using Mixed Effects Random Forests under Aggregated Census Data.

Krennmair, P., & Schmid, T & Tzavidis, Nikos. (2022b). The Estimation of Poverty Indicators Using Mixed Effects Random Forests. Working Paper.