Appendix C: Specification of the main R functions Here we describe the main R packages we used for the small area estimates. All the other analyses were programmed manually. C.1 Estimation of small area means and MSE under EBLUP approach with the package “sae” (Molina and Marhuenda 2015) Required packages: nlme, MASS Functions: eblupBHF( ) and pbmseBHF( ). C.2 Robust estimation and prediction of area means and MSE under REBLUP approach with (Schoch 2014). the package “rsae” Functions: fitsaemodel() and robpredict(). (Muthén and Muthén, C.3 Running Mplus models in the R environment via MplusAutomation (2012), Hallquist and Wiley (2014)) Functions: mplusObject( ), mplusModeler( ). C.4 Mapping using spdep, maptools, sp, Hmisc Functions: readShapePoly( ), spplot( )
Appendix C: Specification of the main R functions Here we describe the main R packages we used for the small area estimates. All the other analyses were programmed manually. C.1 Estimation of small area means and MSE under EBLUP approach with the “sae” package (Molina and Marhuenda 2015) Required packages: nlme, MASS Functions: eblupBHF( ) and pbmseBHF( ). C.2 Robust estimation and prediction of area means and MSE under REBLUP approach with the “rsae” package (Schoch 2014). Functions: fitsaemodel() and robpredict(). C.3 Running Mplus models in the R environment via MplusAutomation (Muthén and Muthén, (2012), Hallquist and Wiley (2014)) Functions: mplusObject( ), mplusModeler( ). C.4 Mapping using spdep, maptools, sp, Hmisc Functions: readShapePoly( ), spplot( )