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

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

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