This function applies the Cell Key Method perturbation to a contingency table generated by `tabulate_cnt_micro_data`, using specified deviation and variance parameters.
Usage
apply_ckm(
tab_data,
cnt_var = "nb_obs",
ck_var = "ckey",
D,
V,
js = 0,
I = NULL,
J = NULL,
...
)Arguments
- tab_data
Object returned by `tabulate_cnt_micro_data` (data.frame or list)
- cnt_var
Character. Name of the count variable (default: "nb_obs")
- ck_var
Character. Name of the cell key variable (must be decimal between 0-1)
- D
integer. Deviation parameter (must be strictly positive)
- V
numeric. Noise variance (must be strictly positive)
- js
integer. Threshold for sensitive values (default: 0). If js=0, only value 0 will be forbidden
- I
integer vector. Original values to consider
- J
integer vector. Perturbed values to consider
- ...
Additional parameters passed to transition matrix creation
Value
List containing: - tab: Perturbed table (tibble) - risque: Risk measures (NULL if empirical frequencies not provided) - utilite: Utility measures (MAD, RMAD, Hellinger distance) - ptab: Transition matrix object
Examples
if (FALSE) { # \dontrun{
data("dtest")
set.seed(8245)
dtest_avec_cles <- build_individual_keys(dtest)
tab_avant <- tabulate_cnt_micro_data(
df = dtest_avec_cles,
cat_vars = c("DIPLOME", "SEXE", "AGE"),
hrc_vars = list(GEO = c("REG", "DEP")),
marge_label = "Total",
freq_empiriq = TRUE
)
res_ckm <- apply_ckm(tab_avant, D = 5, V = 2)
str(res_ckm, max.level = 2)
# With a hierarchical structure
tab_avant2 <- tabulate_cnt_micro_data(
df = dtest_avec_cles |> mutate(NUM = 12),
cat_vars = c("DIPLOME", "SEXE", "AGE"),
hrc_vars = list(GEO = c("REG", "DEP")),
num_var = "NUM",
marge_label = "Total",
freq_empiriq = TRUE #pour pouvoir mesurer le risque
)
res_ckm2 <- apply_ckm(tab_avant2, cnt_var = "num_tot", D = 5, V = 2)
head(res_ckm2$tab)
} # }