# aggr: Compute aggregates of small area estimates and MSEs. In hbsae: Hierarchical Bayesian Small Area Estimation

## Description

Compute aggregates of small area estimates and MSEs.

## Usage

 `1` ``` aggr(x, R) ```

## Arguments

 `x` sae object. `R` aggregation matrix, r x M matrix where M is the number of areas and r the number of aggregate areas; default is aggregation over all areas.

## Value

Object of class `sae` with aggregated small area estimates and MSEs.

`sae-class`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16``` ```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); SE(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); SE(est.area2) COV(est.area2) # covariance matrix ```

### Example output

```REML estimate of variance ratio: 0.2879
numerical integration of f(x) (normalization constant): 11.06 with absolute error < 7e-08
numerical integration of x*f(x): 3.542 with absolute error < 2e-08
posterior mean for variance ratio: 0.3203
[1] 221.3017
[1] 1.31611
area21   area22   area23   area24
188.3457 189.6165 254.4981 282.7922
area21   area22   area23   area24
2.639206 2.293749 2.652971 3.129654
area21        area22        area23        area24
area21  6.9654089073 -0.0009095994  0.0021213211 -9.081502e-05
area22 -0.0004572583  5.2612861803 -0.0006379373  2.731048e-05
area23  0.0021870159 -0.0013083143  7.0382527792 -1.306230e-04
area24 -0.0001699394  0.0001016610 -0.0002370885  9.794734e+00
```

hbsae documentation built on May 29, 2017, 9:56 p.m.