This is a wrapper function of GaussSuppressionFromData
for small count frequency suppression. For common applications, the spec
parameter can be adjusted, see PackageSpecs for more
information. See Details for more information on function call customization.
Usage
SuppressSmallCounts(
data,
maxN,
freqVar = NULL,
dimVar = NULL,
hierarchies = NULL,
formula = NULL,
...,
spec = PackageSpecs("smallCountSpec")
)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".- maxN
Suppression parameter. Cells with frequency
<= maxNare set as primary suppressed. Using the defaultprimaryfunction,maxNis by default set to3. See details.- freqVar
A single variable holding counts (name or number).
- 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
- ...
Further arguments to be passed to the supplied functions and to
ModelMatrix(such asinputInOutputandremoveEmpty).- spec
NULLor a named list of arguments that will act as default values.
Details
The specs provided in the package (see PackageSpecs)
provide common parameter setups for small count suppression. However, it might
be necessary to customize the parameters further. In this case, certain
parameters from GaussSuppressionFromData
might need adjusting from the values provided by the package specs. In
particular, the parameters protectZeros (should zeros be primary
suppressed), extend0 (should empty cells be added before primary
suppression), and secondaryZeros (should zero frequency cells be
candidates for secondary suppression) might be of interest. The examples
below illustrate how to override parameters specified by a spec. Note
that this is only possible if specLock = FALSE.
Examples
mun_accidents <- SSBtoolsData("mun_accidents")
SuppressSmallCounts(data = mun_accidents, maxN = 3, dimVar = 1:2, freqVar = 3)
#> [extend0 24*3->24*3]
#> GaussSuppression_anySum: .............
#> mun inj freq primary suppressed
#> 1 Total Total 32 FALSE FALSE
#> 2 Total light 3 TRUE TRUE
#> 3 Total none 6 FALSE FALSE
#> 4 Total serious 23 FALSE FALSE
#> 5 Total unknown 0 TRUE TRUE
#> 6 k1 Total 4 FALSE TRUE
#> 7 k1 light 0 TRUE TRUE
#> 8 k1 none 0 TRUE TRUE
#> 9 k1 serious 4 FALSE FALSE
#> 10 k1 unknown 0 TRUE TRUE
#> 11 k2 Total 6 FALSE FALSE
#> 12 k2 light 0 TRUE TRUE
#> 13 k2 none 1 TRUE TRUE
#> 14 k2 serious 5 FALSE FALSE
#> 15 k2 unknown 0 TRUE TRUE
#> 16 k3 Total 6 FALSE FALSE
#> 17 k3 light 2 TRUE TRUE
#> 18 k3 none 1 TRUE TRUE
#> 19 k3 serious 3 TRUE TRUE
#> 20 k3 unknown 0 TRUE TRUE
#> 21 k4 Total 9 FALSE FALSE
#> 22 k4 light 1 TRUE TRUE
#> 23 k4 none 4 FALSE FALSE
#> 24 k4 serious 4 FALSE FALSE
#> 25 k4 unknown 0 TRUE TRUE
#> 26 k5 Total 1 TRUE TRUE
#> 27 k5 light 0 TRUE TRUE
#> 28 k5 none 0 TRUE TRUE
#> 29 k5 serious 1 TRUE TRUE
#> 30 k5 unknown 0 TRUE TRUE
#> 31 k6 Total 6 FALSE FALSE
#> 32 k6 light 0 TRUE TRUE
#> 33 k6 none 0 TRUE TRUE
#> 34 k6 serious 6 FALSE TRUE
#> 35 k6 unknown 0 TRUE TRUE
# override default spec
SuppressSmallCounts(data = mun_accidents, maxN = 3, dimVar = 1:2, freqVar = 3,
protectZeros = FALSE)
#> [extend0 24*3->24*3]
#> GaussSuppression_anySum: ...........................
#> mun inj freq primary suppressed
#> 1 Total Total 32 FALSE FALSE
#> 2 Total light 3 TRUE TRUE
#> 3 Total none 6 FALSE TRUE
#> 4 Total serious 23 FALSE FALSE
#> 5 Total unknown 0 FALSE FALSE
#> 6 k1 Total 4 FALSE TRUE
#> 7 k1 light 0 FALSE FALSE
#> 8 k1 none 0 FALSE FALSE
#> 9 k1 serious 4 FALSE TRUE
#> 10 k1 unknown 0 FALSE FALSE
#> 11 k2 Total 6 FALSE FALSE
#> 12 k2 light 0 FALSE FALSE
#> 13 k2 none 1 TRUE TRUE
#> 14 k2 serious 5 FALSE TRUE
#> 15 k2 unknown 0 FALSE FALSE
#> 16 k3 Total 6 FALSE FALSE
#> 17 k3 light 2 TRUE TRUE
#> 18 k3 none 1 TRUE TRUE
#> 19 k3 serious 3 TRUE TRUE
#> 20 k3 unknown 0 FALSE FALSE
#> 21 k4 Total 9 FALSE FALSE
#> 22 k4 light 1 TRUE TRUE
#> 23 k4 none 4 FALSE TRUE
#> 24 k4 serious 4 FALSE FALSE
#> 25 k4 unknown 0 FALSE FALSE
#> 26 k5 Total 1 TRUE TRUE
#> 27 k5 light 0 FALSE FALSE
#> 28 k5 none 0 FALSE FALSE
#> 29 k5 serious 1 TRUE TRUE
#> 30 k5 unknown 0 FALSE FALSE
#> 31 k6 Total 6 FALSE FALSE
#> 32 k6 light 0 FALSE FALSE
#> 33 k6 none 0 FALSE FALSE
#> 34 k6 serious 6 FALSE FALSE
#> 35 k6 unknown 0 FALSE FALSE
d2 <- SSBtoolsData("d2")
d2$f <- round(d2$freq/10) # tenth as frequency in examples
# Hierarchical region variables are detected automatically -> same output column
SuppressSmallCounts(data = d2, maxN = 2, freqVar = "f",
dimVar = c("region", "county", "k_group"))
#> [extend0 44*4->44*4]
#> GaussSuppression_anySum: ..................
#> region f primary suppressed
#> 1 1 13 FALSE FALSE
#> 2 10 9 FALSE TRUE
#> 3 300 59 FALSE FALSE
#> 4 4 6 FALSE TRUE
#> 5 400 10 FALSE FALSE
#> 6 5 11 FALSE FALSE
#> 7 6 19 FALSE FALSE
#> 8 8 11 FALSE FALSE
#> 9 Total 69 FALSE FALSE
#> 10 A 12 FALSE TRUE
#> 11 B 6 FALSE TRUE
#> 12 C 7 FALSE FALSE
#> 13 D 4 FALSE FALSE
#> 14 E 13 FALSE FALSE
#> 15 F 6 FALSE FALSE
#> 16 G 4 FALSE FALSE
#> 17 H 7 FALSE FALSE
#> 18 I 1 TRUE TRUE
#> 19 J 6 FALSE FALSE
#> 20 K 3 FALSE TRUE
# Formula. Hierarchical variables still detected automatically.
SuppressSmallCounts(data = d2, maxN = 3, freqVar = "f",
formula = ~main_income * k_group + region + county - k_group)
#> [extend0 44*5->44*5]
#> GaussSuppression_anySum: ........................
#> main_income region f primary suppressed
#> 1 Total Total 69 FALSE FALSE
#> 2 assistance Total 35 FALSE FALSE
#> 3 other Total 8 FALSE FALSE
#> 4 pensions Total 22 FALSE FALSE
#> 5 wages Total 4 FALSE FALSE
#> 6 Total A 12 FALSE TRUE
#> 7 Total B 6 FALSE FALSE
#> 8 Total C 7 FALSE FALSE
#> 9 Total D 4 FALSE FALSE
#> 10 Total E 13 FALSE FALSE
#> 11 Total F 6 FALSE FALSE
#> 12 Total G 4 FALSE FALSE
#> 13 Total H 7 FALSE FALSE
#> 14 Total I 1 TRUE TRUE
#> 15 Total J 6 FALSE TRUE
#> 16 Total K 3 TRUE TRUE
#> 17 Total 1 13 FALSE FALSE
#> 18 Total 4 6 FALSE FALSE
#> 19 Total 5 11 FALSE FALSE
#> 20 Total 6 19 FALSE FALSE
#> 21 Total 8 11 FALSE FALSE
#> 22 Total 10 9 FALSE FALSE
#> 23 assistance 300 29 FALSE FALSE
#> 24 assistance 400 6 FALSE FALSE
#> 25 other 300 7 FALSE TRUE
#> 26 other 400 1 TRUE TRUE
#> 27 pensions 300 19 FALSE TRUE
#> 28 pensions 400 3 TRUE TRUE
#> 29 wages 300 4 FALSE TRUE
#> 30 wages 400 0 TRUE TRUE
# With hierarchies created manually
ml <- data.frame(levels = c("@", "@@", "@@@", "@@@", "@@@", "@@"),
codes = c("Total", "not_assistance", "other", "pensions", "wages", "assistance"))
SuppressSmallCounts(data = d2, maxN = 2, freqVar = "f",
hierarchies = list(main_income = ml, k_group = "Total_Norway"))
#> [extend0 44*3->44*3]
#> GaussSuppression_anySum: ................
#> main_income k_group f primary suppressed
#> 1 Total Total_Norway 69 FALSE FALSE
#> 2 Total 300 59 FALSE FALSE
#> 3 Total 400 10 FALSE FALSE
#> 4 not_assistance Total_Norway 34 FALSE FALSE
#> 5 not_assistance 300 30 FALSE FALSE
#> 6 not_assistance 400 4 FALSE FALSE
#> 7 assistance Total_Norway 35 FALSE FALSE
#> 8 assistance 300 29 FALSE FALSE
#> 9 assistance 400 6 FALSE FALSE
#> 10 other Total_Norway 8 FALSE FALSE
#> 11 other 300 7 FALSE TRUE
#> 12 other 400 1 TRUE TRUE
#> 13 pensions Total_Norway 22 FALSE FALSE
#> 14 pensions 300 19 FALSE FALSE
#> 15 pensions 400 3 FALSE FALSE
#> 16 wages Total_Norway 4 FALSE FALSE
#> 17 wages 300 4 FALSE TRUE
#> 18 wages 400 0 TRUE TRUE
# Data without pensions in k_group 400
# And assume these are structural zeros (will not be suppressed)
SuppressSmallCounts(data = d2[1:41, ], maxN = 3, freqVar = "f",
hierarchies = list(main_income = ml, k_group = "Total_Norway"),
extend0 = FALSE, structuralEmpty = TRUE)
#> GaussSuppression_anySum: ..............
#> main_income k_group f primary suppressed
#> 1 Total Total_Norway 66 FALSE FALSE
#> 2 Total 300 59 FALSE FALSE
#> 3 Total 400 7 FALSE FALSE
#> 4 not_assistance Total_Norway 31 FALSE FALSE
#> 5 not_assistance 300 30 FALSE TRUE
#> 6 not_assistance 400 1 TRUE TRUE
#> 7 assistance Total_Norway 35 FALSE FALSE
#> 8 assistance 300 29 FALSE TRUE
#> 9 assistance 400 6 FALSE TRUE
#> 10 other Total_Norway 8 FALSE FALSE
#> 11 other 300 7 FALSE TRUE
#> 12 other 400 1 TRUE TRUE
#> 13 pensions Total_Norway 19 FALSE FALSE
#> 14 pensions 300 19 FALSE FALSE
#> 15 pensions 400 0 FALSE FALSE
#> 16 wages Total_Norway 4 FALSE FALSE
#> 17 wages 300 4 FALSE TRUE
#> 18 wages 400 0 TRUE TRUE
# -- Note for the example above --
# With protectZeros = FALSE
# - No zeros suppressed
# With extend0 = FALSE and structuralEmpty = FALSE
# - Primary suppression without protection (with warning)
# With extend0 = TRUE and structuralEmpty = TRUE
# - As default behavior. Suppression/protection of all zeros (since nothing empty)
# With formula instead of hierarchies: Extra parameter needed when extend0 = FALSE.
# - removeEmpty = FALSE, to include empty zeros in output.
# Using formula followed by FormulaSelection
output <- SuppressSmallCounts(data = SSBtoolsData("example1"),
formula = ~age * geo * year + eu * year,
freqVar = "freq",
maxN = 1)
#> [extend0 18*5->18*5]
#> GaussSuppression_anySum: ..............................................
FormulaSelection(output, ~(age + eu) * year)
#> age geo year freq primary suppressed
#> 1 Total Total Total 59 FALSE FALSE
#> 2 old Total Total 38 FALSE FALSE
#> 3 young Total Total 21 FALSE FALSE
#> 7 Total Total 2014 20 FALSE FALSE
#> 8 Total Total 2015 18 FALSE FALSE
#> 9 Total Total 2016 21 FALSE FALSE
#> 10 Total EU Total 46 FALSE FALSE
#> 11 Total nonEU Total 13 FALSE FALSE
#> 18 old Total 2014 13 FALSE FALSE
#> 19 old Total 2015 13 FALSE FALSE
#> 20 old Total 2016 12 FALSE FALSE
#> 21 young Total 2014 7 FALSE FALSE
#> 22 young Total 2015 5 FALSE FALSE
#> 23 young Total 2016 9 FALSE FALSE
#> 33 Total EU 2014 15 FALSE FALSE
#> 34 Total nonEU 2014 5 FALSE FALSE
#> 35 Total EU 2015 15 FALSE FALSE
#> 36 Total nonEU 2015 3 FALSE FALSE
#> 37 Total EU 2016 16 FALSE FALSE
#> 38 Total nonEU 2016 5 FALSE FALSE
# To illustrate hierarchical_extend0
# (parameter to underlying function, SSBtools::Extend0fromModelMatrixInput)
SuppressSmallCounts(data = SSBtoolsData("example1"),
formula = ~age * geo * eu, freqVar = "freq",
maxN = 0, avoidHierarchical = TRUE)
#> [extend0 18*4->24*4]
#> GaussSuppression_anySum: ...........................
#> age geo eu freq primary suppressed
#> 1 Total Total Total 59 FALSE FALSE
#> 2 old Total Total 38 FALSE FALSE
#> 3 young Total Total 21 FALSE FALSE
#> 4 Total Iceland Total 13 FALSE FALSE
#> 5 Total Portugal Total 12 FALSE FALSE
#> 6 Total Spain Total 34 FALSE FALSE
#> 7 Total Total EU 46 FALSE FALSE
#> 8 Total Total nonEU 13 FALSE FALSE
#> 9 old Iceland Total 10 FALSE TRUE
#> 10 old Portugal Total 11 FALSE TRUE
#> 11 old Spain Total 17 FALSE FALSE
#> 12 young Iceland Total 3 FALSE TRUE
#> 13 young Portugal Total 1 FALSE TRUE
#> 14 young Spain Total 17 FALSE FALSE
#> 15 old Total EU 28 FALSE FALSE
#> 16 old Total nonEU 10 FALSE FALSE
#> 17 young Total EU 18 FALSE FALSE
#> 18 young Total nonEU 3 FALSE FALSE
#> 19 Total Iceland EU 0 TRUE TRUE
#> 20 Total Iceland nonEU 13 FALSE TRUE
#> 21 Total Portugal EU 12 FALSE TRUE
#> 22 Total Portugal nonEU 0 TRUE TRUE
#> 23 Total Spain EU 34 FALSE TRUE
#> 24 Total Spain nonEU 0 TRUE TRUE
#> 25 old Iceland EU 0 TRUE TRUE
#> 26 old Iceland nonEU 10 FALSE TRUE
#> 27 old Portugal EU 11 FALSE TRUE
#> 28 old Portugal nonEU 0 TRUE TRUE
#> 29 old Spain EU 17 FALSE TRUE
#> 30 old Spain nonEU 0 TRUE TRUE
#> 31 young Iceland EU 0 TRUE TRUE
#> 32 young Iceland nonEU 3 FALSE TRUE
#> 33 young Portugal EU 1 FALSE TRUE
#> 34 young Portugal nonEU 0 TRUE TRUE
#> 35 young Spain EU 17 FALSE TRUE
#> 36 young Spain nonEU 0 TRUE TRUE
SuppressSmallCounts(data = SSBtoolsData("example1"),
formula = ~age * geo * eu, freqVar = "freq",
maxN = 0, avoidHierarchical = TRUE,
hierarchical_extend0 = TRUE)
#> [extend0 18*4->18*4]
#> age geo eu freq primary suppressed
#> 1 Total Total Total 59 FALSE FALSE
#> 2 old Total Total 38 FALSE FALSE
#> 3 young Total Total 21 FALSE FALSE
#> 4 Total Iceland Total 13 FALSE FALSE
#> 5 Total Portugal Total 12 FALSE FALSE
#> 6 Total Spain Total 34 FALSE FALSE
#> 7 Total Total EU 46 FALSE FALSE
#> 8 Total Total nonEU 13 FALSE FALSE
#> 9 old Iceland Total 10 FALSE FALSE
#> 10 old Portugal Total 11 FALSE FALSE
#> 11 old Spain Total 17 FALSE FALSE
#> 12 young Iceland Total 3 FALSE FALSE
#> 13 young Portugal Total 1 FALSE FALSE
#> 14 young Spain Total 17 FALSE FALSE
#> 15 old Total EU 28 FALSE FALSE
#> 16 old Total nonEU 10 FALSE FALSE
#> 17 young Total EU 18 FALSE FALSE
#> 18 young Total nonEU 3 FALSE FALSE
#> 19 Total Iceland nonEU 13 FALSE FALSE
#> 20 Total Portugal EU 12 FALSE FALSE
#> 21 Total Spain EU 34 FALSE FALSE
#> 22 old Iceland nonEU 10 FALSE FALSE
#> 23 old Portugal EU 11 FALSE FALSE
#> 24 old Spain EU 17 FALSE FALSE
#> 25 young Iceland nonEU 3 FALSE FALSE
#> 26 young Portugal EU 1 FALSE FALSE
#> 27 young Spain EU 17 FALSE FALSE
# This example is similar to the one in the documentation of tables_by_formulas,
# but it uses SuppressSmallCounts, and the input data (SSBtoolsData("magnitude1"))
# is used to generate a frequency table by excluding the "value" variable.
tables_by_formulas(SSBtoolsData("magnitude1"),
table_fun = SuppressSmallCounts,
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")),
maxN = 2)
#> [preAggregate 20*6->10*5]
#> [extend0 10*5->12*5]
#> GaussSuppression_anySum: ...................
#> region sector freq primary suppressed table_1 table_2 table_3
#> 1 Total Total 20 FALSE FALSE TRUE FALSE FALSE
#> 2 Iceland Total 4 FALSE FALSE TRUE FALSE TRUE
#> 3 Portugal Total 8 FALSE FALSE TRUE FALSE TRUE
#> 4 Spain Total 8 FALSE FALSE TRUE FALSE TRUE
#> 5 EU Total 16 FALSE FALSE TRUE FALSE TRUE
#> 6 nonEU Total 4 FALSE FALSE TRUE FALSE TRUE
#> 7 Total private 16 FALSE FALSE TRUE FALSE FALSE
#> 8 Total public 4 FALSE FALSE TRUE FALSE FALSE
#> 9 Total Agriculture 4 FALSE FALSE FALSE FALSE TRUE
#> 10 Total Entertainment 6 FALSE FALSE FALSE FALSE TRUE
#> 11 Total Governmental 4 FALSE FALSE FALSE FALSE TRUE
#> 12 Total Industry 6 FALSE FALSE FALSE FALSE TRUE
#> 13 Iceland private 4 FALSE TRUE TRUE FALSE FALSE
#> 14 Iceland public 0 TRUE TRUE TRUE FALSE FALSE
#> 15 Portugal private 6 FALSE TRUE TRUE FALSE FALSE
#> 16 Portugal public 2 TRUE TRUE TRUE FALSE FALSE
#> 17 Spain private 6 FALSE TRUE TRUE FALSE FALSE
#> 18 Spain public 2 TRUE TRUE TRUE FALSE FALSE
#> 19 EU private 12 FALSE TRUE TRUE FALSE FALSE
#> 20 EU public 4 FALSE TRUE TRUE FALSE FALSE
#> 21 nonEU private 4 FALSE TRUE TRUE FALSE FALSE
#> 22 nonEU public 0 TRUE TRUE TRUE FALSE FALSE
#> 23 EU Agriculture 4 FALSE TRUE FALSE TRUE FALSE
#> 24 EU Entertainment 5 FALSE TRUE FALSE TRUE FALSE
#> 25 EU Governmental 4 FALSE TRUE FALSE TRUE FALSE
#> 26 EU Industry 3 FALSE FALSE FALSE TRUE FALSE
#> 27 nonEU Agriculture 0 TRUE TRUE FALSE TRUE FALSE
#> 28 nonEU Entertainment 1 TRUE TRUE FALSE TRUE FALSE
#> 29 nonEU Governmental 0 TRUE TRUE FALSE TRUE FALSE
#> 30 nonEU Industry 3 FALSE FALSE FALSE TRUE FALSE