MagnitudeRule {GaussSuppression}

Dominance ⁠(n,k)⁠ or p% 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

MagnitudeRule(
  data,
  x,
  numVar,
  n = NULL,
  k = NULL,
  pPercent = NULL,
  protectZeros = FALSE,
  charVar = NULL,
  removeCodes = character(0),
  sWeightVar = NULL,
  domWeightMethod = "default",
  allDominance = FALSE,
  outputWeightedNum = !is.null(sWeightVar),
  dominanceVar = NULL,
  ...
)

DominanceRule(data, n, k, protectZeros = FALSE, ...)

PPercentRule(data, pPercent, protectZeros = FALSE, ...)

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.

pPercent

Parameter in the p% rule, when non-NULL. Parameters n and k will then be ignored. Technically, calculations are performed internally as if n = 1:2. The results of these intermediate calculations can be viewed by setting allDominance = TRUE.

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.

removeCodes

A vector of charVar codes that are to be excluded when calculating dominance percentages. Essentially, the corresponding numeric values from dominanceVar or numVar are set to zero before proceeding with the dominance calculations. With empty charVar row indices are assumed and conversion to integer is performed.

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.

dominanceVar

When specified, dominanceVar is used in place of numVar. Specifying dominanceVar is beneficial for avoiding warnings when there are multiple numVar variables. Typically, dominanceVar will be one of the variables already included in numVar.

...

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.

Note

Explicit protectZeros in wrappers since default needed by GaussSuppressionFromData

Author(s)

Daniel Lupp and Øyvind Langsrud

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")