Suppress magnitude tables using dominance (n,k) or p% rule for primary suppression.
Source: R/SuppressDominantCells.R
SuppressDominantCells.RdThis function utilizes MagnitudeRule.
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,
preAggregate = !is.null(contributorVar) & is.null(sWeightVar),
spec = PackageSpecs("dominanceSpec")
)Arguments
- data
Input data, typically a data frame, tibble, or data.table. If
datais not a classic data frame, it will be coerced to one internally unlesspreAggregateisTRUEandaggregatePackageis"data.table".- n
Parameter
nin dominance rule. Default is1:length(k).- k
Parameter
kin dominance rule.- pPercent
Parameter in the p% rule, when non-NULL. Parameters
nandkwill then be ignored. Technically, calculations are performed internally as ifn = 1:2. The results of these intermediate calculations can be viewed by settingallDominance = TRUE.- allDominance
Logical. If
TRUE, additional information is included in the output, as described inMagnitudeRule.- dominanceVar
Numerical variable to be used in dominance rule. The first
numVarvariable will be used if it is not specified.- numVar
Numerical variable to be aggregated. Any
dominanceVarandcandidatesVarthat are specified and not included innumVarwill 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 asinputInOutputandremoveEmpty).- 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
dominanceVarandnumVar).- 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 = TRUEis intended to prevent this phenomenon from causing suppressed cells to be revealable. It is the zeros in thedominanceVarvariable 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 magnitude tables whenSingletonUniqueContributor0is utilized.- preAggregate
Parameter to
GaussSuppressionFromData. Necessary to include here since the specification inspeccannot takesWeightVarinto account.- spec
NULLor a named list of arguments that will act as default values.
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)
#> [extraAggregate 18*2->7*3] Checking ....
#> GaussSuppression_numttHTT: ..
#> v1 num primary suppressed
#> 1 v1 100 TRUE TRUE
#> 2 v2 100 TRUE TRUE
#> 3 v3 100 TRUE TRUE
#> 4 v4 100 TRUE TRUE
#> 5 v5 100 TRUE TRUE
#> 6 v6 100 FALSE FALSE
#> 7 v7 100 FALSE FALSE
SuppressDominantCells(d, k = c(80,70), numVar = "num", formula = ~v1 -1) # same as above
#> [extraAggregate 18*2->7*3] Checking ....
#> GaussSuppression_numttHTT: ..
#> v1 num primary suppressed
#> 1 v1 100 TRUE TRUE
#> 2 v2 100 TRUE TRUE
#> 3 v3 100 TRUE TRUE
#> 4 v4 100 TRUE TRUE
#> 5 v5 100 TRUE TRUE
#> 6 v6 100 FALSE FALSE
#> 7 v7 100 FALSE FALSE
SuppressDominantCells(d, pPercent = 7, numVar = "num", formula = ~v1 -1)
#> [extraAggregate 18*2->7*3] Checking ....
#> GaussSuppression_numttHTT: ...
#> v1 num primary suppressed
#> 1 v1 100 TRUE TRUE
#> 2 v2 100 TRUE TRUE
#> 3 v3 100 TRUE TRUE
#> 4 v4 100 TRUE TRUE
#> 5 v5 100 FALSE FALSE
#> 6 v6 100 FALSE FALSE
#> 7 v7 100 FALSE FALSE
# with weights
SuppressDominantCells(d, n = c(1,2), k = c(80,70), numVar = "num",
dimVar = "v1", sWeightVar = "sweight")
#> [extraAggregate 18*3->7*3] Checking ....
#> GaussSuppression_numttHTT: .......
#> v1 num sweight weighted.num primary suppressed
#> 1 Total 700 24 1055 FALSE FALSE
#> 2 v1 100 1 100 TRUE TRUE
#> 3 v2 100 3 190 FALSE FALSE
#> 4 v3 100 3 180 FALSE FALSE
#> 5 v4 100 3 170 FALSE TRUE
#> 6 v5 100 4 150 FALSE FALSE
#> 7 v6 100 5 140 FALSE FALSE
#> 8 v7 100 5 125 FALSE FALSE
# overwriting some parameters in default spec
SuppressDominantCells(d, n = c(1,2), k = c(80,70), numVar = "num",
dimVar = "v1", sWeightVar = "sweight", domWeightMethod = "tauargus")
#> [extraAggregate 18*3->7*3] Checking ....
#> GaussSuppression_numttHTT: ....
#> v1 num sweight weighted.num primary suppressed
#> 1 Total 700 24 1055 FALSE FALSE
#> 2 v1 100 1 100 TRUE TRUE
#> 3 v2 100 3 190 TRUE TRUE
#> 4 v3 100 3 180 TRUE TRUE
#> 5 v4 100 3 170 TRUE TRUE
#> 6 v5 100 4 150 FALSE FALSE
#> 7 v6 100 5 140 FALSE FALSE
#> 8 v7 100 5 125 FALSE FALSE
# 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)
#> [extraAggregate 18*2->7*3] Checking ....
#> GaussSuppression_numttHTT: ..
#> v1 num dominant1 dominant2 max1contributor max2contributor n_contr
#> 1 v1 100 1.00 1.00 1 NA 1
#> 2 v2 100 0.90 1.00 2 3 2
#> 3 v3 100 0.80 1.00 4 5 2
#> 4 v4 100 0.70 1.00 6 7 2
#> 5 v5 100 0.50 0.75 8 9 3
#> 6 v6 100 0.40 0.60 11 12 4
#> 7 v7 100 0.25 0.50 15 16 4
#> n_non0_contr nRule nAll primary suppressed
#> 1 1 1 1 TRUE TRUE
#> 2 2 2 2 TRUE TRUE
#> 3 2 2 2 TRUE TRUE
#> 4 2 2 2 TRUE TRUE
#> 5 3 3 3 TRUE TRUE
#> 6 4 4 4 FALSE FALSE
#> 7 4 4 4 FALSE FALSE
SuppressDominantCells(d, n = c(1,2), k = c(80,70), numVar = "num", formula = ~v1 -1,
primary = c(DominanceRule, NContributorsRule), maxN = 4, allDominance = TRUE)
#> [extraAggregate 18*2->7*3] Checking ....
#> GaussSuppression_numttHTT:
#> v1 num dominant1 dominant2 max1contributor max2contributor n_contr
#> 1 v1 100 1.00 1.00 1 NA 1
#> 2 v2 100 0.90 1.00 2 3 2
#> 3 v3 100 0.80 1.00 4 5 2
#> 4 v4 100 0.70 1.00 6 7 2
#> 5 v5 100 0.50 0.75 8 9 3
#> 6 v6 100 0.40 0.60 11 12 4
#> 7 v7 100 0.25 0.50 15 16 4
#> n_non0_contr nRule nAll primary suppressed
#> 1 1 1 1 TRUE TRUE
#> 2 2 2 2 TRUE TRUE
#> 3 2 2 2 TRUE TRUE
#> 4 2 2 2 TRUE TRUE
#> 5 3 3 3 TRUE TRUE
#> 6 4 4 4 TRUE TRUE
#> 7 4 4 4 TRUE TRUE
d2 <- SSBtoolsData("d2")[1:4] # Data considered as microdata
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)
#> [extraAggregate 44*4->11*5] Checking ....
#> GaussSuppression_numttHTT: ..............
#> region v dominant1 dominant2 max1contributor max2contributor
#> 1 1 3.1878024 0.3302228 0.5721194 23 34
#> 2 10 10.0786664 0.4667651 0.6256381 44 43
#> 3 300 27.3487153 0.1414174 0.2581709 18 16
#> 4 4 1.4198773 0.4753900 0.8495649 35 24
#> 5 400 10.9990663 0.4277063 0.5732848 44 43
#> 6 5 6.7724116 0.4200770 0.7788224 26 3
#> 7 6 7.2219335 0.4421335 0.8494272 16 6
#> 8 8 9.6670903 0.4000773 0.5656266 18 8
#> 9 Total 38.3477816 0.1226765 0.2235319 44 18
#> 10 A 2.2674025 0.4642692 0.8043581 23 34
#> 11 B 1.4198773 0.4753900 0.8495649 35 24
#> 12 C 3.3067218 0.7347372 0.8781498 3 36
#> 13 D 3.4656899 0.8208854 0.9100334 26 15
#> 14 E 3.9154941 0.8154931 0.9947520 16 27
#> 15 F 3.3064395 0.8896119 0.9645732 6 17
#> 16 G 5.5201407 0.7006312 0.9352912 18 29
#> 17 H 4.1469497 0.3859174 0.7650037 8 30
#> 18 I 0.9203999 0.5125673 0.7554309 9 20
#> 19 J 3.0271572 0.5289543 0.9056270 43 21
#> 20 K 7.0515092 0.6671437 0.8796339 44 11
#> n_contr n_non0_contr primary suppressed
#> 1 8 8 FALSE FALSE
#> 2 8 8 FALSE FALSE
#> 3 32 32 FALSE FALSE
#> 4 4 4 FALSE FALSE
#> 5 12 12 FALSE FALSE
#> 6 8 8 FALSE FALSE
#> 7 8 8 FALSE FALSE
#> 8 8 8 FALSE FALSE
#> 9 44 44 FALSE FALSE
#> 10 4 4 FALSE FALSE
#> 11 4 4 FALSE FALSE
#> 12 4 4 TRUE TRUE
#> 13 4 4 TRUE TRUE
#> 14 4 4 TRUE TRUE
#> 15 4 4 TRUE TRUE
#> 16 4 4 TRUE TRUE
#> 17 4 4 FALSE TRUE
#> 18 4 4 FALSE FALSE
#> 19 4 4 FALSE FALSE
#> 20 4 4 FALSE FALSE
# 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)
#> [extraAggregate 44*5->44*6] Checking ....
#> GaussSuppression_numttHTT: .::::::::::::::::::::
#> main_income region v primary suppressed
#> 1 Total Total 38.3477816 FALSE FALSE
#> 2 assistance Total 9.4825828 FALSE FALSE
#> 3 other Total 9.7414023 FALSE FALSE
#> 4 pensions Total 9.3008137 FALSE FALSE
#> 5 wages Total 9.8229828 FALSE FALSE
#> 6 Total A 2.2674025 FALSE FALSE
#> 7 Total B 1.4198773 FALSE FALSE
#> 8 Total C 3.3067218 TRUE TRUE
#> 9 Total D 3.4656899 TRUE TRUE
#> 10 Total E 3.9154941 TRUE TRUE
#> 11 Total F 3.3064395 TRUE TRUE
#> 12 Total G 5.5201407 TRUE TRUE
#> 13 Total H 4.1469497 FALSE TRUE
#> 14 Total I 0.9203999 FALSE FALSE
#> 15 Total J 3.0271572 FALSE FALSE
#> 16 Total K 7.0515092 FALSE FALSE
#> 17 Total 1 3.1878024 FALSE FALSE
#> 18 Total 4 1.4198773 FALSE FALSE
#> 19 Total 5 6.7724116 FALSE FALSE
#> 20 Total 6 7.2219335 FALSE FALSE
#> 21 Total 8 9.6670903 FALSE FALSE
#> 22 Total 10 10.0786664 FALSE FALSE
#> 23 assistance 300 8.4123940 FALSE TRUE
#> 24 assistance 400 1.0701889 TRUE TRUE
#> 25 other 300 7.5726446 FALSE FALSE
#> 26 other 400 2.1687577 FALSE FALSE
#> 27 pensions 300 2.9519862 FALSE TRUE
#> 28 pensions 400 6.3488275 TRUE TRUE
#> 29 wages 300 8.4116906 FALSE TRUE
#> 30 wages 400 1.4112922 TRUE TRUE
# 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"))
#> [extraAggregate 44*3->8*4] Checking ....
#> GaussSuppression_numttHTT: ...............
#> main_income k_group v primary suppressed
#> 1 Total Total_Norway 38.347782 FALSE FALSE
#> 2 Total 300 27.348715 FALSE FALSE
#> 3 Total 400 10.999066 FALSE FALSE
#> 4 not_assistance Total_Norway 28.865199 FALSE FALSE
#> 5 not_assistance 300 18.936321 FALSE TRUE
#> 6 not_assistance 400 9.928877 FALSE TRUE
#> 7 assistance Total_Norway 9.482583 FALSE FALSE
#> 8 assistance 300 8.412394 FALSE TRUE
#> 9 assistance 400 1.070189 TRUE TRUE
#> 10 other Total_Norway 9.741402 FALSE FALSE
#> 11 other 300 7.572645 FALSE FALSE
#> 12 other 400 2.168758 FALSE FALSE
#> 13 pensions Total_Norway 9.300814 FALSE FALSE
#> 14 pensions 300 2.951986 FALSE TRUE
#> 15 pensions 400 6.348828 TRUE TRUE
#> 16 wages Total_Norway 9.822983 FALSE FALSE
#> 17 wages 300 8.411691 FALSE TRUE
#> 18 wages 400 1.411292 TRUE TRUE
# With contributorVar and p% rule
SuppressDominantCells(data= SSBtoolsData("magnitude1"),
numVar = "value",
dimVar= c("sector4", "geo"),
contributorVar = "company",
pPercent = 10,
allDominance = TRUE)
#> [preAggregate 20*6->20*5]
#> [extraAggregate 20*5->10*5] Checking .....
#> GaussSuppression_numttHTT: .........
#> sector4 geo freq value dominant1 dominant2 max1contributor
#> 1 Total Total 20 462.3 0.5405581 0.8866537 A
#> 2 Total Iceland 4 37.1 0.7115903 0.9487871 B
#> 3 Total Portugal 8 162.5 0.4855385 0.9526154 B
#> 4 Total Spain 8 262.7 0.6623525 0.8705748 A
#> 5 Agriculture Total 4 240.2 0.7181515 1.0000000 A
#> 6 Agriculture Iceland 0 0.0 0.0000000 0.0000000 <NA>
#> 7 Agriculture Portugal 2 100.4 0.7559761 1.0000000 A
#> 8 Agriculture Spain 2 139.8 0.6909871 1.0000000 A
#> 9 Entertainment Total 6 131.5 0.5885932 0.8577947 A
#> 10 Entertainment Iceland 1 16.8 1.0000000 1.0000000 B
#> 11 Entertainment Portugal 2 9.4 0.7553191 1.0000000 B
#> 12 Entertainment Spain 3 105.3 0.7350427 0.8907882 A
#> 13 Governmental Total 4 32.8 0.6585366 0.8567073 B
#> 14 Governmental Iceland 0 0.0 0.0000000 0.0000000 <NA>
#> 15 Governmental Portugal 2 23.6 0.9152542 1.0000000 B
#> 16 Governmental Spain 2 9.2 0.7065217 1.0000000 C
#> 17 Industry Total 6 57.8 0.6107266 0.9083045 B
#> 18 Industry Iceland 3 20.3 0.4729064 0.9064039 B
#> 19 Industry Portugal 2 29.1 0.8831615 1.0000000 B
#> 20 Industry Spain 1 8.4 1.0000000 1.0000000 C
#> max2contributor n_contr n_non0_contr primary suppressed
#> 1 B 4 4 FALSE FALSE
#> 2 C 3 3 TRUE TRUE
#> 3 A 3 3 TRUE TRUE
#> 4 B 4 4 FALSE FALSE
#> 5 B 2 2 TRUE TRUE
#> 6 <NA> 0 0 FALSE FALSE
#> 7 B 2 2 TRUE TRUE
#> 8 B 2 2 TRUE TRUE
#> 9 B 4 4 FALSE FALSE
#> 10 <NA> 1 1 TRUE TRUE
#> 11 D 2 2 TRUE TRUE
#> 12 C 3 3 FALSE TRUE
#> 13 C 3 3 FALSE TRUE
#> 14 <NA> 0 0 FALSE FALSE
#> 15 D 2 2 TRUE TRUE
#> 16 D 2 2 TRUE TRUE
#> 17 C 3 3 FALSE FALSE
#> 18 C 3 3 FALSE TRUE
#> 19 D 2 2 TRUE TRUE
#> 20 <NA> 1 1 TRUE TRUE
# Using formula followed by FormulaSelection
output <- SuppressDominantCells(data = SSBtoolsData("magnitude1"),
numVar = "value",
formula = ~sector2 * geo + sector4 * eu,
contributorVar = "company",
k = c(80, 99))
#> [preAggregate 20*6->20*7]
#> [extraAggregate 20*7->10*7] Checking .....
#> GaussSuppression_numttHTT: .........:::::
FormulaSelection(output, ~sector2 * geo)
#> geo sector4 freq value primary suppressed
#> 1 Total Total 20 462.3 FALSE FALSE
#> 2 Total private 16 429.5 FALSE FALSE
#> 3 Total public 4 32.8 FALSE FALSE
#> 4 Iceland Total 4 37.1 FALSE FALSE
#> 5 Portugal Total 8 162.5 FALSE FALSE
#> 6 Spain Total 8 262.7 FALSE FALSE
#> 13 Iceland private 4 37.1 FALSE FALSE
#> 14 Portugal private 6 138.9 FALSE TRUE
#> 15 Spain private 6 253.5 FALSE TRUE
#> 16 Portugal public 2 23.6 TRUE TRUE
#> 17 Spain public 2 9.2 TRUE TRUE
# This example is similar to the one in the documentation of tables_by_formulas,
# but it uses SuppressDominantCells with the pPercent and contributorVar parameters.
tables_by_formulas(SSBtoolsData("magnitude1"),
table_fun = SuppressDominantCells,
table_formulas = list(table_1 = ~region * sector2,
table_2 = ~region1:sector4 - 1,
table_3 = ~region + sector4 - 1),
substitute_vars = list(region = c("geo", "eu"), region1 = "eu"),
collapse_vars = list(sector = c("sector2", "sector4")),
dominanceVar = "value", pPercent = 10, contributorVar = "company")
#> [preAggregate 20*6->20*7]
#> [extraAggregate 20*7->10*7] Checking .....
#> GaussSuppression_numttHTT: ........::::
#> region sector freq value primary suppressed table_1 table_2 table_3
#> 1 Total Total 20 462.3 FALSE FALSE TRUE FALSE FALSE
#> 2 Iceland Total 4 37.1 TRUE TRUE TRUE FALSE TRUE
#> 3 Portugal Total 8 162.5 TRUE TRUE TRUE FALSE TRUE
#> 4 Spain Total 8 262.7 FALSE FALSE TRUE FALSE TRUE
#> 5 EU Total 16 425.2 FALSE TRUE TRUE FALSE TRUE
#> 6 nonEU Total 4 37.1 TRUE TRUE TRUE FALSE TRUE
#> 7 Total private 16 429.5 FALSE FALSE TRUE FALSE FALSE
#> 8 Total public 4 32.8 FALSE FALSE TRUE FALSE FALSE
#> 9 Total Agriculture 4 240.2 TRUE TRUE FALSE FALSE TRUE
#> 10 Total Entertainment 6 131.5 FALSE FALSE FALSE FALSE TRUE
#> 11 Total Governmental 4 32.8 FALSE FALSE FALSE FALSE TRUE
#> 12 Total Industry 6 57.8 FALSE TRUE FALSE FALSE TRUE
#> 13 Iceland private 4 37.1 TRUE TRUE TRUE FALSE FALSE
#> 14 Portugal private 6 138.9 TRUE TRUE TRUE FALSE FALSE
#> 15 Portugal public 2 23.6 TRUE TRUE TRUE FALSE FALSE
#> 16 Spain private 6 253.5 FALSE TRUE TRUE FALSE FALSE
#> 17 Spain public 2 9.2 TRUE TRUE TRUE FALSE FALSE
#> 18 EU private 12 392.4 FALSE TRUE TRUE FALSE FALSE
#> 19 EU public 4 32.8 FALSE FALSE TRUE FALSE FALSE
#> 20 nonEU private 4 37.1 TRUE TRUE TRUE FALSE FALSE
#> 21 EU Agriculture 4 240.2 TRUE TRUE FALSE TRUE FALSE
#> 22 EU Entertainment 5 114.7 FALSE TRUE FALSE TRUE FALSE
#> 23 EU Governmental 4 32.8 FALSE FALSE FALSE TRUE FALSE
#> 24 EU Industry 3 37.5 FALSE FALSE FALSE TRUE FALSE
#> 25 nonEU Entertainment 1 16.8 TRUE TRUE FALSE TRUE FALSE
#> 26 nonEU Industry 3 20.3 FALSE TRUE FALSE TRUE FALSE