A complete set of functions to calculate several eblups estimators and its mean square errors. All estimators are based in area-level linear mixed model introduced by Rao and Yu in 1994 (see documentation). Saery package are developed to fit the model with REML method.
The main functions of the saery package are
fit.saery is used to fit the correct model for three options.
eblup.saery calculates the eblup and mse for the model.
Maria Dolores Esteban Lefler, Domingo Morales Gonzalez, Agustin Perez Martin
Maintainer: Agustin Perez Martin <email@example.com>
Rao, J.N.K., Yu, M., 1994. Small area estimation by combining time series and cross sectional data. Canadian Journal of Statistics 22, 511-528.
Esteban, M.D., Morales, D., Perez, A., Santamaria, L., 2012. Small area estimation of poverty proportions under area-level time models. Computational Statistics and Data Analysis, 56 (10), pp. 2840-2855.
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sigma2edi <- datos[,6] X <- as.matrix(datos[,5]) ydi <- datos[,3] D <- length(unique(datos[,1])) md <- rep(length(unique(datos[,2])), D) output.fit.ar1 <- fit.saery(X, ydi, D, md, sigma2edi, "AR", 0.9) output.fit.ar1 #For computational reasons B is too low. We recomend to increase up to 100 eblup.output.ar1 <- eblup.saery(X, ydi, D, md, sigma2edi, "a", plot = TRUE, B = 2) eblup.output.ar1
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