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Estimate protected mesh

Source: R/fonctions_estimation.R
imputer_cle_repartition.Rd

The information on some mesh cannot be disseminated because it doesn't respect the threshold rule. For those mesh, an estimation is done by distributing the total of the variable on a bigger zone (the groupe) into the meshes, proportionnaly to a given variable (non sensitive variable).

Usage

imputer_cle_repartition(tab_car, list_var_imput, var_cle)

Arguments

tab_car

A data.table of the variables agregates in a grid.

list_var_imput

A character vector giving the names of the variables to estimate.

var_cle

A character, the name of the non-sensitive variable according to which the distribution is done.

Value

The data.table tab_car with the estimated variables.

Examples

library(data.table)
n <- 1e4
tab <- as.data.table(
  data.frame(id_obs = 1:n, x = rnorm(n, 3e6, 2e4),
  y = rnorm(n, 2e6, 3e4), crs = 3035))
tab_GS <- create_GS_CPP(tab, 5, c(32e3,16e3,8e3,4e3,2e3,1e3))
#> [1] "Etape 1 : creation des differentes grilles *"
#> [1] "Etape 2 : Initialiser la table de diffusion 'tdiff' **"
#> [1] "Etape 3 : On complete la table de diffusion, en parcourant chaque carreau, du plus grand au plus petit ***"
#> [1] "      Traitement des carreaux de niveau 1"
#> 
#> [1] "      Traitement des carreaux de niveau 2"
#> 
#> [1] "      Traitement des carreaux de niveau 3"
#> 
#> [1] "      Traitement des carreaux de niveau 4"
#> 
#> [1] "      Traitement des carreaux de niveau 5"
#> 
tab_car <- tab_GS$tab_car
tab_car[, `:=`(poph = nb_obs*0.48, popf = nb_obs*0.52)]
#>                                 id_carreau                                  p
#>                                     <char>                             <char>
#>     1: FR_CRS3035RES32000mN1920000E2976000                               <NA>
#>     2: FR_CRS3035RES32000mN2016000E2976000                               <NA>
#>     3: FR_CRS3035RES32000mN1984000E2976000                               <NA>
#>     4: FR_CRS3035RES32000mN1952000E2976000                               <NA>
#>     5: FR_CRS3035RES32000mN2016000E3008000                               <NA>
#>    ---                                                                       
#> 10149:  FR_CRS3035RES1000mN2094000E2993000 FR_CRS3035RES2000mN2094000E2992000
#> 10150:  FR_CRS3035RES1000mN2097000E2993000 FR_CRS3035RES2000mN2096000E2992000
#> 10151:  FR_CRS3035RES1000mN2096000E3026000 FR_CRS3035RES2000mN2096000E3026000
#> 10152:  FR_CRS3035RES1000mN2100000E2967000 FR_CRS3035RES2000mN2100000E2966000
#> 10153:  FR_CRS3035RES1000mN2101000E2979000 FR_CRS3035RES2000mN2100000E2978000
#>        niveau nb_obs   etat force groupe    poph    popf
#>         <num>  <int> <lgcl> <num>  <int>   <num>   <num>
#>     1:      1    264   TRUE     0      1  126.72  137.28
#>     2:      1   1330   TRUE     0      2  638.40  691.60
#>     3:      1   2219   TRUE     0      3 1065.12 1153.88
#>     4:      1   1289   TRUE     0      4  618.72  670.28
#>     5:      1    773   TRUE     0      5  371.04  401.96
#>    ---                                                  
#> 10149:      6      1  FALSE     0    400    0.48    0.52
#> 10150:      6      1  FALSE     0    108    0.48    0.52
#> 10151:      6      1  FALSE     0    127    0.48    0.52
#> 10152:      6      1  FALSE     0    130    0.48    0.52
#> 10153:      6      1  FALSE     0    108    0.48    0.52
tab_diff <- imputer_cle_repartition(
  tab_car,
  list_var_imput = c("poph","popf"),
  var_cle = "nb_obs")