DominanceRule {GaussSuppression}

R Documentation

Dominance ⁠(n,k)⁠ rule for magnitude tables

Description

Supports application of multiple values for n and k. The function works on magnitude tables containing negative cell values by calculating contribution based on absolute values.

Usage

DominanceRule(
  data,
  x,
  numVar,
  n,
  k,
  protectZeros = FALSE,
  charVar = NULL,
  sWeightVar = NULL,
  domWeightMethod = "default",
  allDominance = FALSE,
  outputWeightedNum = !is.null(sWeightVar),
  ...
)

Arguments

data	the dataset
x	ModelMatrix generated by parent function
numVar	vector containing numeric values in the data set
n	parameter n in dominance rule.
k	parameter k in dominance rule.
protectZeros	parameter determining whether cells with value 0 should be suppressed.
charVar	Variable in data holding grouping information. Dominance will be calculated after aggregation within these groups.
sWeightVar	variable with sampling weights to be used in dominance rule
domWeightMethod	character representing how weights should be treated in the dominance rule. See Details.
allDominance	Logical parameter. If TRUE, adds primary columns for each pair of parameters n,k in the dominance rules
outputWeightedNum	logical value to determine whether weighted numerical value should be included in output. Default is TRUE if sWeightVar is provided.
...	unused parameters

Details

This method only supports suppressing a single numeric variable. There are multiple ways of handling sampling weights in the dominance rule. the default method implemented here compares unweighted sample values with the corresponding weighted cell totals. if domWeightMethod is set to "tauargus", the method implemented in tauArgus is used. For more information on this method, see "Statistical Disclosure Control" by Hundepool et al (2012, p. 151).

Value

logical vector that is TRUE in positions corresponding to cells breaching the dominance rules.

Author(s)

Daniel Lupp

Examples

  set.seed(123)
z <- SSBtools::MakeMicro(SSBtoolsData("z2"), "ant")
z$value <- sample(1:1000, nrow(z), replace = TRUE)

GaussSuppressionFromData(z, dimVar = c("region", "fylke", "kostragr", "hovedint"), 
numVar = "value", candidates = CandidatesNum, primary = DominanceRule, preAggregate = FALSE,
singletonMethod = "sub2Sum", n = c(1, 2), k = c(65, 85), allDominance = TRUE)


num <- c(100,
         90, 10,
         80, 20,
         70, 30,
         50, 25, 25,
         40, 20, 20, 20,
         25, 25, 25, 25)
v1 <- c("v1",
        rep(c("v2", "v3", "v4"), each = 2),
        rep("v5", 3),
        rep(c("v6", "v7"), each = 4))
sw <- c(1, 2, 1, 2, 1, 2, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1)
d <- data.frame(v1 = v1, num = num, sw = sw)

# without weights
GaussSuppressionFromData(d, formula = ~v1 - 1, 
 numVar = "num",  n = c(1,2), k = c(80,70),
  preAggregate = FALSE, allDominance = TRUE, candidates = CandidatesNum,
  primary = DominanceRule)

# with weights, standard method
GaussSuppressionFromData(d, formula = ~v1 - 1,
 numVar = "num",  n = c(1,2), k = c(80,70), sWeightVar = "sw",
 preAggregate = FALSE, allDominance = TRUE, candidates = CandidatesNum,
 primary = DominanceRule)

# with weights, tauargus method
GaussSuppressionFromData(d, formula = ~v1 - 1,
 numVar = "num",  n = c(1,2), k = c(80,70), sWeightVar = "sw",
 preAggregate = FALSE, allDominance = TRUE, candidates = CandidatesNum,
 primary = DominanceRule, domWeightMethod = "tauargus")

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