SuppressDominantCells: Suppress magnitude tables using dominance (n,k) or p% rule...

In GaussSuppression: Tabular Data Suppression using Gaussian Elimination

Suppress magnitude tables using dominance ⁠(n,k)⁠ or p% rule for primary suppression.

Description

Suppress magnitude tables using dominance ⁠(n,k)⁠ or p% rule for primary suppression.

Usage

SuppressDominantCells(
  data,
  n = 1:length(k),
  k = NULL,
  pPercent = NULL,
  allDominance = FALSE,
  dominanceVar = NULL,
  numVar = NULL,
  dimVar = NULL,
  hierarchies = NULL,
  formula = NULL,
  contributorVar = NULL,
  sWeightVar = NULL,
  ...,
  candidatesVar = NULL,
  singletonZeros = FALSE,
  spec = PackageSpecs("dominanceSpec")
)

Arguments

data	Input data as a data frame
n	Parameter n in dominance rule. Default is 1:length(k).
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.
allDominance	Logical parameter. If TRUE, adds primary columns for each pair of parameters n,k in the dominance rules
dominanceVar	Numerical variable to be used in dominance rule. The first numVar variable will be used if it is not specified.
numVar	Numerical variable to be aggregated. Any dominanceVar and candidatesVar that are specified and not included in numVar will be aggregated accordingly.
dimVar	The main dimensional variables and additional aggregating variables. This parameter can be useful when hierarchies and formula are unspecified.
hierarchies	List of hierarchies, which can be converted by AutoHierarchies. Thus, the variables can also be coded by "rowFactor" or "", which correspond to using the categories in the data.
formula	A model formula
contributorVar	Extra variables to be used as grouping elements in the dominance rule. Typically, the variable contains the contributor IDs.
sWeightVar	Name of variable which represents sampling weights to be used in dominance rule
...	Further arguments to be passed to the supplied functions and to ModelMatrix (such as inputInOutput and removeEmpty).
candidatesVar	Variable to be used in the candidate function to prioritize cells for publication and thus not suppression. If not specified, the same variable that is used for the dominance rule will be applied (see dominanceVar and numVar).
singletonZeros	When negative values cannot occur, one can determine from a non-suppressed marginal cell with the value 0 that all underlying cells also have the value 0. The use of singletonZeros = TRUE is intended to prevent this phenomenon from causing suppressed cells to be revealable. It is the zeros in the dominanceVar variable that are examined. Specifically, the ordinary singleton method is combined with a method that is actually designed for frequency tables. This approach also works for volume tables when SingletonUniqueContributor0 is utilized.
spec	NULL or a named list of arguments that will act as default values.

Value

data frame containing aggregated data and suppression information.

Examples

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))
sweight <- 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, sweight = sweight)

# basic use
SuppressDominantCells(d, n = c(1,2), k = c(80,70), numVar = "num", formula = ~v1 -1)
SuppressDominantCells(d, k = c(80,70), numVar = "num", formula = ~v1 -1) # same as above
SuppressDominantCells(d, pPercent = 7, numVar = "num", formula = ~v1 -1) 

# with weights
SuppressDominantCells(d, n = c(1,2), k = c(80,70), numVar = "num",
dimVar = "v1", sWeightVar = "sweight")

# overwriting some parameters in default spec
SuppressDominantCells(d, n = c(1,2), k = c(80,70), numVar = "num",
dimVar = "v1", sWeightVar = "sweight", domWeightMethod = "tauargus")

# using dominance and few contributors rule together, see second example compared to first
SuppressDominantCells(d, n = c(1,2), k = c(80,70), numVar = "num", formula = ~v1 -1,
primary = c(DominanceRule, NContributorsRule), maxN = 3, allDominance = TRUE)

SuppressDominantCells(d, n = c(1,2), k = c(80,70), numVar = "num", formula = ~v1 -1,
primary = c(DominanceRule, NContributorsRule), maxN = 4, allDominance = TRUE)


d2 <- SSBtoolsData("d2")
set.seed(123)
d2$v <- rnorm(nrow(d2))^2

# Hierarchical region variables are detected automatically -> same output column
SuppressDominantCells(data = d2, n = c(1, 2), k = c(70, 95), numVar = "v", 
                      dimVar = c("region", "county", "k_group"), allDominance = TRUE)

# Formula. Hierarchical variables still detected automatically.
SuppressDominantCells(data = d2, n = c(1, 2), k = c(70, 95), numVar = "v", 
                      formula = ~main_income * k_group + region + county - k_group)

# With hierarchies created manually
ml <- data.frame(levels = c("@", "@@", "@@@", "@@@", "@@@", "@@"), 
        codes = c("Total", "not_assistance", "other", "pensions", "wages", "assistance"))
SuppressDominantCells(data = d2, n = c(1, 2), k = c(70, 95), numVar = "v", 
                      hierarchies = list(main_income = ml, k_group = "Total_Norway"))

GaussSuppression documentation built on Sept. 24, 2024, 5:07 p.m.