Nothing
d <- generateFakeData()
# compute small area estimates
sae <- fSAE(y0 ~ x + area2, data=d$sam, area="area", popdata=d$Xpop)
# by default aggregate over all areas
global <- aggr(sae)
EST(global); RMSE(global)
# aggregation to broad area
# first build aggregation matrix
M <- d$Xpop[, c("area22", "area23", "area24")] / d$Xpop[, "(Intercept)"]
M <- cbind(1 - rowSums(M), M); colnames(M)[1] <- "area21"
est.area2 <- aggr(sae, M)
EST(est.area2); RMSE(est.area2)
COV(est.area2) # covariance matrix
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