An Application to SAE with Averaging Pseudo Area Level Model on Sample Dataset

Load package and data

library(saePseudo)
data("dataVill")

Fitting Model

result <- avgPseudo(
  prov = dataVill$Area1,
  reg = dataVill$Area2,
  sub = dataVill$Area3,
  vill = dataVill$Area4,
  y = dataVill$ydir_area4,
  x = dataVill$X1,
  var = dataVill$vardir_area4,
  N = dataVill$N,
  method = "REML"
)

Extract estimation

Estimation for Sub-district Area

result$Est_Area3
#> # A tibble: 28 × 5
#> # Groups:   Province, Region [6]
#>    Province Region Subdistrict y_agr_villsub rse_agr_villsub
#>       <dbl>  <dbl>       <dbl>         <dbl>           <dbl>
#>  1        1      1           1          28.9           0.826
#>  2        1      1           2          25.7           0.764
#>  3        1      1           3          28.0           0.654
#>  4        1      2           1          30.1           0.918
#>  5        1      2           2          31.0           0.872
#>  6        1      2           3          28.7           1.03 
#>  7        1      2           4          28.1           1.63 
#>  8        1      2           5          28.1           1.15 
#>  9        1      2           6          28.8           0.925
#> 10        1      3           1          28.2           1.61 
#> # ℹ 18 more rows

Estimation for Region Area

result$Est_Area2
#> # A tibble: 6 × 4
#> # Groups:   Province [1]
#>   Province Region y_agr_subreg rse_agr_subreg
#>      <dbl>  <dbl>        <dbl>          <dbl>
#> 1        1      1         27.5          0.426
#> 2        1      2         29.3          0.421
#> 3        1      3         28.9          0.295
#> 4        1      4         28.1          0.375
#> 5        1      5         28.6          0.450
#> 6        1      6         28.2          0.532

Estimation for Provincial Area

result$Est_Area1
#> # A tibble: 1 × 3
#>   Province y_agr_regprov rse_agr_regprov
#>      <dbl>         <dbl>           <dbl>
#> 1        1          28.5           0.165