Package 'GaussSuppression'

GaussSuppression from data and suppressed data

Description

Extended version of GaussSuppressionFromData that takes into account suppression pattern in suppressed data sent as input

Usage

AdditionalSuppression(
  data,
  ...,
  fun = GaussSuppressionFromData,
  primary = GetDefault(fun, "primary"),
  suppressedData = NULL,
  makePrimary = TRUE,
  makeForced = TRUE,
  forceNotPrimary = TRUE
)

Arguments

data	Input data as a data frame
...	Further parameters to GaussSuppressionFromData
fun	A function: GaussSuppressionFromData or one of its wrappers such as SuppressSmallCounts and SuppressDominantCells.
primary	As input to GaussSuppressionFromData before possible extension caused by suppressedData. Supply NULL if all primary suppressions are retrieved form suppressedData.
suppressedData	A data frame or a list of data frames as output from GaussSuppressionFromData.
makePrimary	When TRUE, suppression in suppressedData is preserved.
makeForced	When TRUE, non-suppression in suppressedData is preserved. An exception is possible primary suppression which has priority over forced. Use forceNotPrimary to avoid this exception.
forceNotPrimary	When TRUE, non-suppression in suppressedData is forced to be not primary suppressed.

Details

This function is an easy alternative to using PrimaryFromSuppressedData and the relating functions manually. See the examples of PrimaryFromSuppressedData. By default, the suppression pattern in suppressedData is preserved. The behavior can be tuned by the parameters.

Note that the variables used in suppressedData in addition to "suppressed" are those with matching names in crossTable. Others are ignored. See examples (d3, d4, d5). NOW A FIX IS INCLUDED by attribute totCode. EXAMPLES NOT YET CHANGED.

Value

Aggregated data with suppression information

Examples

z1 <- SSBtoolsData("z1")
z2 <- SSBtoolsData("z2")
z3 <- SSBtoolsData("z3")

# Ordinary suppressions
a <- GaussSuppressionFromData(z1, 1:2, 3, maxN = 5)
b <- GaussSuppressionFromData(z2, 1:4, 5, maxN = 1)

# As b and also suppression pattern in a preserved 
b1 <- AdditionalSuppression(z2, 1:4, 5, maxN = 1, suppressedData = a)

# Rows with differences
cbind(b, b1)[b1$suppressed != b$suppressed, ]

# All primary from a 
b2 <- AdditionalSuppression(z2, 1:4, 5, suppressedData = a, primary = NULL, singleton = NULL)

# Rows with suppression 
b2[b2$suppressed, ]

# All primary from b2
d1 <- AdditionalSuppression(data = z3, 1:6, 7, suppressedData = b2, primary = NULL, 
                            singleton = NULL)

# No suppression since no common codes 
d1[d1$suppressed, ]

# Use another coding of fylke
z3$fylke_ <- z3$fylke - 4
d2 <- AdditionalSuppression(data = z3, c(1, 3:6, 8), 7, suppressedData = b2, primary = NULL, 
                            singleton = NULL)

# Two primary found in b2 -> several secondary
d2[d2$suppressed,]


# Examples demonstrating limitations of AdditionalSuppression
# Variable mnd in suppressedData is not used 

# No suppression since unsuppressed rows used by makeForced and forceNotPrimary
d3 <- AdditionalSuppression(data = z3, c(1, 3:4, 8), 7, suppressedData = d2, primary = NULL, 
                            singleton = NULL)
d3[d3$suppressed, ]

# Now suppression, but not too much
d4 <- AdditionalSuppression(data = z3, c(1, 3:4, 8), 7, suppressedData = d2, 
                            forceNotPrimary = FALSE, primary = NULL, singleton = NULL)
d4[d4$suppressed, ]

# The correct way is to limit the input
d5 <- AdditionalSuppression(data = z3, c(1, 3:4, 8), 7, suppressedData = d2[d2$mnd == "Total", ], 
                            primary = NULL, singleton = NULL)
d5[d5$suppressed, ]

---

Candidates functions

Description

Function for GaussSuppressionFromData

Usage

CandidatesDefault(freq, x, secondaryZeros = FALSE, weight, ...)

CandidatesNum(
  secondaryZeros = FALSE,
  freq = NULL,
  num,
  weight,
  x,
  candidatesVar = NULL,
  removeCodes = character(0),
  removeCodesForCandidates = TRUE,
  data,
  charVar,
  ...
)

Arguments

freq	Vector of output frequencies
x	The model matrix
secondaryZeros	When TRUE, cells with zero frequency or value are prioritized to be published so that they are not secondary suppressed. This is achieved by this function by having the zero frequency indices first in the retuned order.
weight	Vector of output weights
...	Unused parameters
num	Data frame of output aggregates calculated from numVar. When several variables, and without specifying candidatesVar, only first is used.
candidatesVar	One of the variable names from numVar to be used in the calculations. Specifying candidatesVar helps avoid warnings when multiple numVar variables are present.
removeCodes	Same parameter as used in suppression rules, e.g. NContributorsRule. It is often assumed that cells where all contributors (charVar) are present in removeCodes should be published. Here, such cells will be prioritized to achieve this. Note that this functionality is redundant if the same cells are specified by forced.
removeCodesForCandidates	removeCodes ignored when set to FALSE.
data	Input data as a data frame (needed for removeCodes calculations)
charVar	Variable(s) with contributor codes (needed for removeCodes calculations)

Details

CandidatesDefault orders the indices decreasingly according to freq or, when weight is non-NULL, (freq+1)*weight. Ties are handled by prioritizing output cells that are calculated from many input cells. In addition, zeros are handled according to parameter secondaryZeros. When freq is negative (special hierarchy), abs(freq)*weight is used.

CandidatesNum orders the indices decreasingly according to absolute values of the numeric variable (according to abs(num[[1]])). In practice this is done by running CandidatesDefault with manipulated weights.

Value

candidates, GaussSuppression input

---

Repeated GaussSuppression with forwarding of previous results

Description

AdditionalSuppression is called several times. Each time with all previous results as suppressedData.

Usage

ChainedSuppression(..., withinArg = NULL)

ChainedSuppressionHi(..., hierarchies)

ChainedSuppressionHi1(..., hierarchies)

Arguments

...	Arguments to AdditionalSuppression/GaussSuppressionFromData that are kept constant.
withinArg	A list of named lists. Arguments to AdditionalSuppression/GaussSuppressionFromData that are not kept constant. List elements with suppressed data are also allowed.
hierarchies	In the wrapper ChainedSuppressionHi, this argument will be used to generate the withinArg to ChainedSuppression with the same length (see examples). Then, element number i of withinArg is list(hierarchies = hierarchies[1:i]). In the similar wrapper, ChainedSuppressionHi1, withinArg has always two elements: list(hierarchies = hierarchies[1]) and list(hierarchies = hierarchies).

Value

List of data frames. The wrappers, ChainedSuppressionHi and ChainedSuppressionHi1, return a single data frame, which is the last list item.

Examples

z1 <- SSBtoolsData("z1")
z2 <- SSBtoolsData("z2")
z2b <- z2[3:5]
names(z2b)[1] <- "region"

# As GaussSuppressionFromData when a single element within withinArg
a1 <- ChainedSuppression(z1, 1:2, 3, maxN = 5)
a2 <- ChainedSuppression(z1, withinArg = list(list(dimVar = 1:2, freqVar = 3, maxN = 5)))
identical(a1, a2[[1]])

# b[[3]] include results from b[[1]] and b[[2]]
b <- ChainedSuppression(z1, freqVar = 3, withinArg = list(
       list(dimVar = 1,   maxN = 55), 
       list(dimVar = 2,   maxN = 55), 
       list(dimVar = 1:2, maxN = 5)))

# d[[2]] is same as b1 in AdditionalSuppression examples
d <- ChainedSuppression(withinArg = list(
       list(data = z1,  dimVar = 1:2, freqVar = 3, maxN = 5), 
       list(data = z2,  dimVar = 1:4, freqVar = 5, maxN = 1)))

# Common variable names important. 
# Therefore kostragr renamed to region in z2b. 
f <- ChainedSuppression(withinArg = list(
       list(data = z1,  dimVar = 1:2, freqVar = 3, maxN = 5), 
       list(data = z2b, dimVar = 1:2, freqVar = 3, maxN = 5), 
       list(data = z2,  dimVar = 1:4, freqVar = 5, maxN = 1)))

# Parameters so that only suppressions are forwarded. 
# This is first iteration in linked tables by iterations. 
e <- ChainedSuppression(withinArg = list(
       list(data = z1,  dimVar = 1:2, freqVar = 3, maxN = 5), 
       list(data = z2b, dimVar = 1:2, freqVar = 3, maxN = 5), 
       list(data = z2,  dimVar = 1:4, freqVar = 5, maxN = 1)), 
       makeForced = FALSE, forceNotPrimary = FALSE)
       
# "A" "annet"/"arbeid" could be suppressed here, but not in f since f[[1]]      
e[[3]][which(e[[3]]$suppressed != f[[3]]$suppressed), ]  


#### Demonstrate SuppressionByChainedHierarchies

dimLists <- SSBtools::FindDimLists(z2[, 4:1])


# Two ways of doing the same calculations
g1 <- ChainedSuppressionHi(z2, c(1, 3), 5, maxN = 1, hierarchies = dimLists)
g1b <-  ChainedSuppression(z2, c(1, 3), 5, maxN = 1, withinArg = list(
         list(hierarchies = dimLists[1]),
         list(hierarchies = dimLists[1:2]),
         list(hierarchies = dimLists[1:3])))[[3]]      
       
# Results different after combining hierarchies      
g2 <- ChainedSuppressionHi(z2, c(1, 3), 5, maxN = 1, 
         hierarchies = SSBtools::AutoHierarchies(dimLists))        
       
# In this case, the same results can be obtained by:         
g3 <- ChainedSuppressionHi1(z2, c(1, 3), 5, maxN = 1, hierarchies = dimLists)

---

Function for calculating intervals for suppressed tables.

Description

This function solves linear programs to determine interval boundaries for suppressed cells.

Usage

ComputeIntervals(
  x,
  z,
  primary,
  suppressed,
  minVal = NULL,
  lpPackage = "lpSolve",
  gaussI = TRUE,
  allInt = FALSE,
  sparseConstraints = TRUE
)

Arguments

x	ModelMatrix, as output from SSBtools::ModelMatrix
z	numerical vector with length ncol(x). Corresponds to table cell values
primary	Vector indicating primary suppressed cells. Can be logical or integer. If integer vector, indicates the columns of x which are considered primary suppressed.
suppressed	Vector indicating all suppressed cells. Can be logical or integer. If integer vector, indicates the columns of x which are considered suppressed.
minVal	a known minimum value for table cells. Default NULL. Note that 'minVal' is interpreted as the limiting value for all suppressed cells. Specifying 'minVal=0' would be redundant, as a minimum value of 0 is anyway assumed for inner cells (see details).
lpPackage	The name of the package used to solve linear programs. Currently, 'lpSolve' (default), 'Rsymphony', 'Rglpk' and 'highs' are supported.
gaussI	Boolean vector. If TRUE (default), GaussIndependent is used to reduce size of linear program.
allInt	Integer variables when TRUE. See all.int parameter in lpSolve and types parameter in Rsymphony and Rglpk.
sparseConstraints	When TRUE, a sparse constraint matrix will be input to the solver. In the case of lpSolve, the sparse matrix is represented in triplet form as a dense matrix with three columns, and the dense.const parameter is utilized.

Details

This function is still experimental.

Default in for bounds parameter in Rsymphony_solve_LP and Rglpk_solve_LP: The default for each variable is a bound between 0 and Inf. Details in lpSolve: Note that every variable is assumed to be ⁠>= 0⁠!

Author(s)

Øyvind Langsrud and Daniel Lupp

---

Method for finding dominant cells according to (possibly multiple) n,k dominance rules.

Description

Supports functionality for grouping contributions according to holding variables, as well as calculating dominance in surveys with a given sampling weight. Two methods are implemented, depending on whether the sampling weights sum to total population. The parameter tauArgusDominance determines this. If FALSE, unweighted contributions are compared to weighted cell values. If TRUE, the method described in in the book "Statistical Disclosure Control" (Hundepool et al 2012, p. 151) is used.

Usage

FindDominantCells(
  x,
  inputnum,
  num,
  n,
  k,
  charVar_groups,
  samplingWeight,
  tauArgusDominance = FALSE,
  returnContrib = FALSE
)

Arguments

x	model matrix describing relationship between input and published cells
inputnum	vector of numeric contributions for each of the input records
num	vector of numeric values for each of the published cells
n	vector of integers describing n parameters in n,k rules. Must be same length as k parameter.
k	vector of numeric values describing k parameters in n,k rules, where percentages are described as numbers less than 100. Must be same length as n parameter.
charVar_groups	vector describing which input records should be grouped
samplingWeight	vector of sampling weights associated to input records
tauArgusDominance	logical value, default FALSE. determines how to handle sampling weights in the dominance rule (see details).
returnContrib	logical value, default FALSE. If TRUE return value is the percentage of the first n contributors

Value

logical vector describing which publish-cells need to be suppressed.

---

New primary cells to fix risky intervals

Description

Indices to new primary cells are returned

Usage

FixRiskyIntervals(
  x,
  z,
  primary,
  suppressed,
  candidates = NULL,
  minVal = NULL,
  lpPackage = "lpSolve",
  gaussI = FALSE,
  allInt = FALSE,
  sparseConstraints = TRUE,
  rangeLimits
)

Arguments

x	ModelMatrix, as output from SSBtools::ModelMatrix
z	numerical vector with length ncol(x). Corresponds to table cell values
primary	Vector indicating primary suppressed cells. Can be logical or integer. If integer vector, indicates the columns of x which are considered primary suppressed.
suppressed	Vector indicating all suppressed cells. Can be logical or integer. If integer vector, indicates the columns of x which are considered suppressed.
candidates	candidates as indices
minVal	a known minimum value for table cells. Default NULL. Note that 'minVal' is interpreted as the limiting value for all suppressed cells. Specifying 'minVal=0' would be redundant, as a minimum value of 0 is anyway assumed for inner cells (see details).
lpPackage	The name of the package used to solve linear programs. Currently, 'lpSolve' (default), 'Rsymphony', 'Rglpk' and 'highs' are supported.
gaussI	Boolean vector. If TRUE (default), GaussIndependent is used to reduce size of linear program.
allInt	Integer variables when TRUE. See all.int parameter in lpSolve and types parameter in Rsymphony and Rglpk.
sparseConstraints	When TRUE, a sparse constraint matrix will be input to the solver. In the case of lpSolve, the sparse matrix is represented in triplet form as a dense matrix with three columns, and the dense.const parameter is utilized.
rangeLimits	As computed by RangeLimitsDefault

Details

Code in this function started from a copy of ComputeIntervals

---

Cell suppression with synthetic decimal numbers

Description

GaussSuppressionFromData is run and decimal numbers are added to output by a modified (for sparse matrix efficiency) version of SuppressDec.

Usage

GaussSuppressDec(
  data,
  ...,
  output = NULL,
  digits = 9,
  nRep = NULL,
  rmse = pi/3,
  sparseLimit = 500,
  rndSeed = 123,
  runIpf = FALSE,
  eps = 0.01,
  iter = 100,
  mismatchWarning = TRUE,
  whenDuplicatedInner = NULL,
  whenMixedDuplicatedInner = warning
)

Arguments

data	Input daata as a data frame
...	Further parameters to GaussSuppressionFromData
output	NULL (default), "publish", "inner", "publish_inner", or "publish_inner_x" (x also).
digits	Parameter to RoundWhole. Values close to whole numbers will be rounded.
nRep	NULL or an integer. When >1, several decimal numbers will be generated.
rmse	Desired root mean square error of decimal numbers. Variability around the expected, according to the linear model, inner frequencies. The expected frequencies are calculated from the non-suppressed publishable frequencies.
sparseLimit	Limit for the number of rows of a reduced x-matrix within the algorithm. When exceeded, a new sparse algorithm is used.
rndSeed	If non-NULL, a random generator seed to be used locally within the function without affecting the random value stream in R.
runIpf	When TRUE, additional frequencies are generated by iterative proportional fitting using Mipf.
eps	Parameter to Mipf.
iter	Parameter to Mipf.
mismatchWarning	Whether to produce the warning "⁠Mismatch between whole numbers and suppression⁠", when relevant. When nRep>1, all replicates must satisfy the whole number requirement for non-suppressed cells. When mismatchWarning is integer (⁠>0⁠), this will be used as parameter digits to RoundWhole when doing mismatch checking (can be quite low when nRep>1).
whenDuplicatedInner	Function to be called when default output and when cells marked as inner correspond to several input cells (aggregated) since they correspond to published cells.
whenMixedDuplicatedInner	Function to be called in the case above when some inner cells correspond to published cells (aggregated) and some not (not aggregated).

Value

A data frame where inner cells and cells to be published are combined or output according to parameter output.

Author(s)

Øyvind Langrsud

Examples

z1 <- SSBtoolsData("z1")
GaussSuppressDec(z1, 1:2, 3)
GaussSuppressDec(z1, freqVar = "ant", formula = ~ region + hovedint, maxN = 10)

---

Cell suppression from input data containing inner cells

Description

Aggregates are generated followed by primary suppression followed by secondary suppression by Gaussian elimination by GaussSuppression

Usage

GaussSuppressionFromData(
  data,
  dimVar = NULL,
  freqVar = NULL,
  ...,
  numVar = NULL,
  weightVar = NULL,
  charVar = NULL,
  hierarchies = NULL,
  formula = NULL,
  maxN = suppressWarnings(formals(c(primary)[[1]])$maxN),
  protectZeros = suppressWarnings(formals(c(primary)[[1]])$protectZeros),
  secondaryZeros = suppressWarnings(formals(candidates)$secondaryZeros),
  candidates = CandidatesDefault,
  primary = PrimaryDefault,
  forced = NULL,
  hidden = NULL,
  singleton = SingletonDefault,
  singletonMethod = ifelse(secondaryZeros, "anySumNOTprimary", "anySum"),
  printInc = TRUE,
  output = "publish",
  x = NULL,
  crossTable = NULL,
  preAggregate = is.null(freqVar),
  extraAggregate = preAggregate & !is.null(charVar),
  structuralEmpty = FALSE,
  extend0 = FALSE,
  spec = NULL,
  specLock = FALSE,
  freqVarNew = rev(make.unique(c(names(data), "freq")))[1],
  nUniqueVar = rev(make.unique(c(names(data), "nUnique")))[1],
  forcedInOutput = "ifNonNULL",
  unsafeInOutput = "ifForcedInOutput",
  lpPackage = NULL,
  aggregatePackage = "base",
  aggregateNA = TRUE,
  aggregateBaseOrder = FALSE,
  rowGroupsPackage = aggregatePackage
)

Arguments

data	Input data as a data frame
dimVar	The main dimensional variables and additional aggregating variables. This parameter can be useful when hierarchies and formula are unspecified.
freqVar	A single variable holding counts (name or number).
...	Further arguments to be passed to the supplied functions and to ModelMatrix (such as inputInOutput and removeEmpty).
numVar	Other numerical variables to be aggregated
weightVar	weightVar Weights (costs) to be used to order candidates for secondary suppression
charVar	Other variables possibly to be used within the supplied functions
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
maxN	Suppression parameter. Cells with frequency ⁠<= maxN⁠ are set as primary suppressed. Using the default primary function, maxN is by default set to 3. See details.
protectZeros	Suppression parameter. When TRUE, cells with zero frequency or value are set as primary suppressed. Using the default primary function, protectZeros is by default set to TRUE. See details.
secondaryZeros	Suppression parameter. When TRUE, cells with zero frequency or value are prioritized to be published so that they are not secondary suppressed. Using the default candidates function, secondaryZeros is by default set to FALSE. See details.
candidates	GaussSuppression input or a function generating it (see details) Default: CandidatesDefault
primary	GaussSuppression input or a function generating it (see details) Default: PrimaryDefault
forced	GaussSuppression input or a function generating it (see details)
hidden	GaussSuppression input or a function generating it (see details)
singleton	GaussSuppression input or a function generating it (see details) Default: SingletonDefault
singletonMethod	GaussSuppression input. The default value depends on parameter secondaryZeros which depends on candidates (see details).
printInc	GaussSuppression input
output	One of "publish" (default), "inner", "publish_inner", "publish_inner_x", "publish_x", "inner_x", "input2functions" (input to supplied functions), "inputGaussSuppression", "inputGaussSuppression_x", "outputGaussSuppression" "outputGaussSuppression_x", "primary", "secondary" and "all". Here "inner" means input data (possibly pre-aggregated) and "x" means dummy matrix (as input parameter x). All input to and output from GaussSuppression, except ..., are returned when "outputGaussSuppression_x". Excluding x and only input are also possible. The code "all" means all relevant output after all the calculations. Currently, this means the same as "publish_inner_x" extended with the matrices (or NULL) xExtraPrimary and unsafe. The former matrix is usually made by KDisclosurePrimary. This latter matrix contains the columns representing unsafe primary suppressions. In addition to x columns corresponding to unsafe in ordinary output (see parameter unsafeInOutput below), possible columns from xExtraPrimary may also be included in the unsafe matrix (see details).
x	x (modelMatrix) and crossTable can be supplied as input instead of generating it from ModelMatrix
crossTable	See above.
preAggregate	When TRUE, the data will be aggregated within the function to an appropriate level. This is defined by the dimensional variables according to dimVar, hierarchies or formula and in addition charVar.
extraAggregate	When TRUE, the data will be aggregated by the dimensional variables according to dimVar, hierarchies or formula. The aggregated data and the corresponding x-matrix will only be used as input to the singleton function and GaussSuppression. This extra aggregation is useful when parameter charVar is used. Supply "publish_inner", "publish_inner_x", "publish_x" or "inner_x" as output to obtain extra aggregated results. Supply "inner" or "input2functions" to obtain other results.
structuralEmpty	When TRUE, output cells with no contributing inner cells (only zeros in column of x) are forced to be not primary suppressed. Thus, these cells are considered as structural zeros. When structuralEmpty is TRUE, the following error message is avoided: Suppressed cells with empty input will not be protected. Extend input data with ⁠zeros?⁠. When removeEmpty is TRUE (see "..." below), structuralEmpty is superfluous
extend0	Data is automatically extended by Extend0 when TRUE. Can also be set to "all" which means that input codes in hierarchies are considered in addition to those in data. Parameter extend0 can also be specified as a list meaning parameter varGroups to Extend0.
spec	NULL or a named list of arguments that will act as default values.
specLock	When TRUE, arguments in spec cannot be changed.
freqVarNew	Name of new frequency variable generated when input freqVar is NULL and preAggregate is TRUE. Default is "freq" provided this is not found in names(data).
nUniqueVar	Name of variable holding the number of unique contributors. This variable will be generated in the extraAggregate step. Default is "nUnique" provided this is not found in names(data). If an existing variable is passed as input, this variable will apply only when preAggregate/extraAggregate is not done.
forcedInOutput	Whether to include forced as an output column. One of "ifNonNULL" (default), "always", "ifany" and "no". In addition, TRUE and FALSE are allowed as alternatives to "always" and "no".
unsafeInOutput	Whether to include usafe as an output column. One of "ifForcedInOutput" (default), "always", "ifany" and "no". In addition, TRUE and FALSE are allowed as alternatives to "always" and "no". see details.
lpPackage	lpPackage: When non-NULL, intervals by ComputeIntervals will be included in the output. See its documentation for valid parameter values for 'lpPackage'. If, additionally, at least one of the two RangeLimitsDefault parameters below is specified, further suppression will be performed to satisfy the interval width requirements. Then, the values in the output variable suppressed_integer means: no suppression (0), primary suppression (1), secondary suppression (2), additional suppression applied by an interval algorithm limited to linearly independent cells (3), and further suppression according to the final gauss algorithm (4). Intervals, ⁠[lo_1, up_1]⁠, are intervals calculated prior to additional suppression. rangePercent: Required interval width expressed as a percentage rangeMin: Minimum required width of the interval Please note that interval calculations may have a different interface in future versions.
aggregatePackage	Package used to preAggregate/extraAggregate. Parameter pkg to aggregate_by_pkg.
aggregateNA	Whether to include NAs in the grouping variables while preAggregate/extraAggregate. Parameter include_na to aggregate_by_pkg.
aggregateBaseOrder	Parameter base_order to aggregate_by_pkg, used when preAggregate/extraAggregate. The parameter does not affect the ordering of ordinary output. Therefore, the default is set to FALSE to avoid unnecessary sorting operations. The parameter will have impact when, e.g output = "inner".
rowGroupsPackage	Parameter pkg to RowGroups. The parameter is input to Formula2ModelMatrix via ModelMatrix.

Details

The supplied functions for generating GaussSuppression input takes the following arguments: crossTable, x, freq, num, weight, maxN, protectZeros, secondaryZeros, data, freqVar, numVar, weightVar, charVar, dimVar aggregatePackage, aggregateNA, aggregateBaseOrder, rowGroupsPackage, and .... where the two first are ModelMatrix outputs (modelMatrix renamed to x). The vector, freq, is aggregated counts (t(x) %*% data[[freqVar]]). In addition, the supplied singleton function also takes nUniqueVar and (output from) primary as input.

Similarly, num, is a data frame of aggregated numerical variables. It is possible to supply several primary functions joined by c, e.g. (c(FunPrim1, FunPrim2)). All NAs returned from any of the functions force the corresponding cells not to be primary suppressed.

The effect of maxN , protectZeros and secondaryZeros depends on the supplied functions where these parameters are used. Their default values are inherited from the default values of the first primary function (several possible) or, in the case of secondaryZeros, the candidates function. When defaults cannot be inherited, they are set to NULL. In practice the function formals are still used to generate the defaults when primary and/or candidates are not functions. Then NULL is correctly returned, but suppressWarnings are needed.

Singleton handling can be turned off by singleton = NULL or singletonMethod = "none". Both of these choices are identical in the sense that singletonMethod is set to "none" whenever singleton is NULL and vice versa.

Information about uncertain primary suppressions due to forced cells can be found as described by parameters unsafeInOutput and output (⁠= "all"⁠). When forced cells affect singleton problems, this is not implemented. Some information can be seen from warnings. This can also be seen by choosing output = "secondary" together with unsafeInOutput = "ifany" or unsafeInOutput = "always". Then, negative indices from GaussSuppression using unsafeAsNegative = TRUE will be included in the output. Singleton problems may, however, be present even if it cannot be seen as warning/output. In some cases, the problems can be detected by GaussSuppressDec.

In some cases, cells that are forced, hidden, or primary suppressed can overlap. For these situations, forced has precedence over hidden and primary. That is, if a cell is both forced and hidden, it will be treated as a forced cell and thus published. Similarly, any primary suppression of a forced cell will be ignored (see parameter whenPrimaryForced to GaussSuppression). It is, however, meaningful to combine primary and hidden. Such cells will be protected while also being assigned the NA value in the suppressed output variable.

Value

Aggregated data with suppression information

Author(s)

Øyvind Langsrud and Daniel Lupp

Examples

z1 <- SSBtoolsData("z1")
GaussSuppressionFromData(z1, 1:2, 3)

z2 <- SSBtoolsData("z2")
GaussSuppressionFromData(z2, 1:4, 5, protectZeros = FALSE)


# Data as in GaussSuppression examples
df <- data.frame(values = c(1, 1, 1, 5, 5, 9, 9, 9, 9, 9, 0, 0, 0, 7, 7), 
                 var1 = rep(1:3, each = 5), var2 = c("A", "B", "C", "D", "E"))

GaussSuppressionFromData(df, c("var1", "var2"), "values")
GaussSuppressionFromData(df, c("var1", "var2"), "values", formula = ~var1 + var2, maxN = 10)
GaussSuppressionFromData(df, c("var1", "var2"), "values", formula = ~var1 + var2, maxN = 10,
      protectZeros = TRUE, # Parameter needed by SingletonDefault and default not in primary  
      primary = function(freq, crossTable, maxN, ...) 
                   which(freq <= maxN & crossTable[[2]] != "A" & crossTable[, 2] != "C"))
                   
# Combining several primary functions 
# Note that NA & c(TRUE, FALSE) equals c(NA, FALSE)                      
GaussSuppressionFromData(df, c("var1", "var2"), "values", formula = ~var1 + var2, maxN = 10, 
       primary = c(function(freq, maxN, protectZeros = TRUE, ...) freq >= 45,
                   function(freq, maxN, ...) freq <= maxN,
                   function(crossTable, ...) NA & crossTable[[2]] == "C",  
                   function(crossTable, ...) NA & crossTable[[1]]== "Total" 
                                                & crossTable[[2]]== "Total"))                    
                   
# Similar to GaussSuppression examples
GaussSuppressionFromData(df, c("var1", "var2"), "values", formula = ~var1 * var2, 
       candidates = NULL, singleton = NULL, protectZeros = FALSE, secondaryZeros = TRUE)
GaussSuppressionFromData(df, c("var1", "var2"), "values", formula = ~var1 * var2, 
       singleton = NULL, protectZeros = FALSE, secondaryZeros = FALSE)
GaussSuppressionFromData(df, c("var1", "var2"), "values", formula = ~var1 * var2, 
       protectZeros = FALSE, secondaryZeros = FALSE)

              
# Examples with zeros as singletons
z <- data.frame(row = rep(1:3, each = 3), col = 1:3, freq = c(0, 2, 5, 0, 0, 6:9))
GaussSuppressionFromData(z, 1:2, 3, singleton = NULL) 
GaussSuppressionFromData(z, 1:2, 3, singletonMethod = "none") # as above 
GaussSuppressionFromData(z, 1:2, 3)
GaussSuppressionFromData(z, 1:2, 3, protectZeros = FALSE, secondaryZeros = TRUE, singleton = NULL)
GaussSuppressionFromData(z, 1:2, 3, protectZeros = FALSE, secondaryZeros = TRUE)

---

Two-way iteration variant of GaussSuppressionFromData

Description

Internally, data is organized in a two-way table.

Use parameter colVar to choose hierarchies for columns (others will be rows). Iterations start by column by column suppression. The algorithm utilizes HierarchyCompute2.

With two-way iterations, larger data can be handled, but there is a residual risk. The method is a special form of linked-table iteration. Separately, the rows and columns are protected by GaussSuppression and they have common suppressed cells.

Usage

GaussSuppressionTwoWay(
  data,
  dimVar = NULL,
  freqVar = NULL,
  numVar = NULL,
  weightVar = NULL,
  charVar = NULL,
  hierarchies,
  formula = NULL,
  maxN = suppressWarnings(formals(c(primary)[[1]])$maxN),
  protectZeros = suppressWarnings(formals(c(primary)[[1]])$protectZeros),
  secondaryZeros = suppressWarnings(formals(candidates)$secondaryZeros),
  candidates = CandidatesDefault,
  primary = PrimaryDefault,
  forced = NULL,
  hidden = NULL,
  singleton = SingletonDefault,
  singletonMethod = ifelse(secondaryZeros, "anySumNOTprimary", "anySum"),
  printInc = TRUE,
  output = "publish",
  preAggregate = is.null(freqVar),
  colVar = names(hierarchies)[1],
  removeEmpty = TRUE,
  inputInOutput = TRUE,
  candidatesFromTotal = TRUE,
  structuralEmpty = FALSE,
  freqVarNew = rev(make.unique(c(names(data), "freq")))[1],
  ...
)

Arguments

data	Input data as a data frame
dimVar	The main dimensional variables and additional aggregating variables. This parameter can be useful when hierarchies and formula are unspecified.
freqVar	A single variable holding counts (name or number).
numVar	Other numerical variables to be aggregated
weightVar	weightVar Weights (costs) to be used to order candidates for secondary suppression
charVar	Other variables possibly to be used within the supplied functions
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
maxN	Suppression parameter. See GaussSuppressionFromData.
protectZeros	Suppression parameter. See GaussSuppressionFromData.
secondaryZeros	Suppression parameter. See GaussSuppressionFromData.
candidates	GaussSuppression input or a function generating it (see details) Default: CandidatesDefault
primary	GaussSuppression input or a function generating it (see details) Default: PrimaryDefault
forced	GaussSuppression input or a function generating it (see details)
hidden	GaussSuppression input or a function generating it (see details)
singleton	NULL or a function generating GaussSuppression input (logical vector not possible) Default: SingletonDefault
singletonMethod	GaussSuppression input
printInc	GaussSuppression input
output	One of "publish" (default), "inner". Here "inner" means input data (possibly pre-aggregated).
preAggregate	When TRUE, the data will be aggregated within the function to an appropriate level. This is defined by the dimensional variables according to dimVar, hierarchies or formula and in addition charVar.
colVar	Hierarchy variables for the column groups (others in row group).
removeEmpty	When TRUE (default) empty output corresponding to empty input is removed. When NULL, removal only within the algorithm (x matrices) so that such empty outputs are never secondary suppressed.
inputInOutput	Logical vector (possibly recycled) for each element of hierarchies. TRUE means that codes from input are included in output. Values corresponding to "rowFactor" or "" are ignored.
candidatesFromTotal	When TRUE (default), same candidates for all rows and for all columns, computed from row/column totals.
structuralEmpty	See GaussSuppressionFromData.
freqVarNew	Name of new frequency variable generated when input freqVar is NULL and preAggregate is TRUE. Default is "freq" provided this is not found in names(data).
...	Further arguments to be passed to the supplied functions.

Details

The supplied functions for generating GaussSuppression input behave as in GaussSuppressionFromData with some exceptions. When candidatesFromTotal is TRUE (default) the candidate function will be run locally once for rows and once for columns. Each time based on column or row totals. The global x-matrix will only be generated if one of the functions supplied needs it. Non-NULL singleton can only be supplied as a function. This function will be run locally within the algorithm before each call to GaussSuppression.

Note that a difference from GaussSuppressionFromData is that parameter removeEmpty is set to TRUE by default.

Another difference is that duplicated combinations is not allowed. Normally duplicates are avoided by setting preAggregate to TRUE. When the charVar parameter is used, this can still be a problem. See the examples for a possible workaround.

Value

Aggregated data with suppression information

Examples

z3 <- SSBtoolsData("z3")

dimListsA <- SSBtools::FindDimLists(z3[, 1:6])
dimListsB <- SSBtools::FindDimLists(z3[, c(1, 4, 5)])

set.seed(123)
z <- z3[sample(nrow(z3),250),]

## Not run: 
out1 <- GaussSuppressionTwoWay(z, freqVar = "ant", hierarchies = dimListsA, 
                               colVar = c("hovedint"))

## End(Not run)                                
out2 <- GaussSuppressionTwoWay(z, freqVar = "ant", hierarchies = dimListsA, 
                               colVar = c("hovedint", "mnd"))
out3 <- GaussSuppressionTwoWay(z, freqVar = "ant", hierarchies = dimListsB, 
                               colVar = c("region"))
out4 <- GaussSuppressionTwoWay(z, freqVar = "ant", hierarchies = dimListsB, 
                               colVar = c("hovedint", "region"))
                               
# "mnd" not in  hierarchies -> duplicated combinations in input 
# Error when  preAggregate is FALSE: Index method failed. Duplicated combinations?
out5 <- GaussSuppressionTwoWay(z, freqVar = "ant", hierarchies = dimListsA[1:3], 
                               protectZeros = FALSE, colVar = c("hovedint"), preAggregate = TRUE)


# charVar needed -> Still problem when preAggregate is TRUE
# Possible workaround by extra hierarchy 
out6 <- GaussSuppressionTwoWay(z, freqVar = "ant", charVar = "mnd2",
                               hierarchies = c(dimListsA[1:3], mnd2 = "Total"), # include charVar 
                               inputInOutput = c(TRUE, TRUE, FALSE),  # FALSE -> only Total 
                               protectZeros = FALSE, colVar = c("hovedint"),
                               preAggregate = TRUE,  
                               hidden = function(x, data, charVar, ...) 
                                 as.vector((t(x) %*% as.numeric(data[[charVar]] == "M06M12")) == 0))

---

Construct primary suppressed difference matrix

Description

Function for constructing model matrix columns representing primary suppressed difference cells

Usage

KDisclosurePrimary(
  data,
  x,
  crossTable,
  freqVar,
  mc_hierarchies = NULL,
  coalition = 1,
  upper_bound = Inf,
  ...
)

Arguments

data	a data.frame representing the data set
x	ModelMatrix generated by parent function
crossTable	crossTable generated by parent function
freqVar	name of the frequency variable in data
mc_hierarchies	a hierarchy representing meaningful combinations to be protected. Default value is NULL.
coalition	numeric vector of length one, representing possible size of an attacking coalition. This parameter corresponds to the parameter k in the definition of k-disclosure.
upper_bound	numeric value representing minimum count considered safe. Default set to Inf
...	parameters passed to children functions

Value

dgCMatrix corresponding to primary suppressed cells

Author(s)

Daniel P. Lupp

---

Linked tables by full GaussSuppressionFromData iterations

Description

AdditionalSuppression is called several times as in ChainedSuppression

Usage

LazyLinkedTables(..., withinArg = NULL, maxIterLinked = 1000)

Arguments

...	Arguments to GaussSuppressionFromData that are kept constant.
withinArg	A list of named lists. Arguments to GaussSuppressionFromData that are not kept constant.
maxIterLinked	Maximum number of GaussSuppressionFromData calls for each table.

Details

This function is created as a spin-off from AdditionalSuppression and ChainedSuppression. The calculations run GaussSuppressionFromData from the input each time. There is no doubt that this can be done more efficiently.

A consequence of this lazy implementation is that, in output, primary and suppressed are identical.

Note that there is a residual risk when suppression linked tables by iterations.

Value

List of data frames

Note

In this function, the parameters makeForced and forceNotPrimary to AdditionalSuppression are forced to be FALSE.

Examples

z1 <- SSBtoolsData("z1")
z2 <- SSBtoolsData("z2")
z2b <- z2[3:5]  # As in ChainedSuppression example 
names(z2b)[1] <- "region"

# The two region hierarchies as two linked tables
a <- LazyLinkedTables(z2, freqVar = 5, withinArg = list(
       list(dimVar = c(1, 2, 4)), 
       list(dimVar = c(1, 3, 4))))

# As 'f' and 'e' in ChainedSuppression example. 
# 'A' 'annet'/'arbeid' suppressed in b[[1]], since suppressed in b[[3]].
b <- LazyLinkedTables(withinArg = list(
       list(data = z1, dimVar = 1:2, freqVar = 3, maxN = 5), 
       list(data = z2b, dimVar = 1:2, freqVar = 3, maxN = 5), 
       list(data = z2, dimVar = 1:4, freqVar = 5, maxN = 1)))

---

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")

---

Find major contributions to aggregates

Description

Assuming aggregates are calculated via a dummy matrix by z = t(x) %*% y, the n largest contributions are found (value or index) for each aggregate.

Usage

MaxContribution(
  x,
  y,
  n = 1,
  decreasing = TRUE,
  index = FALSE,
  groups = NULL,
  return2 = FALSE
)

Arguments

x	A (sparse) dummy matrix
y	Vector of input values (contributors)
n	Number of contributors to be found
decreasing	Ordering parameter. Smallest contributors found when FALSE.
index	Indices to y returned when TRUE
groups	When non-NULL, major contributions after aggregation within groups. Cannot be combined with index = TRUE. The missing group category is excluded.
return2	When TRUE, two matrices are returned, value and id. The latter contains indices when group is NULL and otherwise a character matrix of groups.

Value

Matrix with lagest contributions in first column, second largest in second column and so on. Alternative output when using parameters index or return2.

Author(s)

Øyvind Langsrud

See Also

ModelMatrix

Examples

library(SSBtools)

z <- SSBtoolsData("sprt_emp_withEU")
z$age[z$age == "Y15-29"] <- "young"
z$age[z$age == "Y30-64"] <- "old"

a <- ModelMatrix(z, formula = ~age + geo, crossTable = TRUE)

cbind(as.data.frame(a$crossTable), MaxContribution(a$modelMatrix, z$ths_per, 1))
cbind(a$crossTable, MaxContribution(a$modelMatrix, z$ths_per, 10))
cbind(a$crossTable, MaxContribution(a$modelMatrix, z$ths_per, 10, index = TRUE))

# Both types of output can be achieved with return2 = TRUE)
identical(MaxContribution(a$modelMatrix, z$ths_per, 10, return2 = TRUE),
          list(value =  MaxContribution(a$modelMatrix, z$ths_per, 10), 
               id =  MaxContribution(a$modelMatrix, z$ths_per, 10, index = TRUE)))

b <- ModelMatrix(z[, -4], crossTable = TRUE, inputInOutput = c(TRUE, FALSE, TRUE))

k <- cbind(b$crossTable, MaxContribution(b$modelMatrix, z$ths_per, 10))

gr18 <- paste0("g", 1:18)                          # Each row is a group
k18 <- cbind(b$crossTable, MaxContribution(b$modelMatrix, z$ths_per, 10, groups = gr18))
identical(k, k18) # TRUE

gr9 <- paste0("g", as.integer(10 * z$ths_per)%%10) # 9 groups from decimal
k9 <- cbind(b$crossTable, MaxContribution(b$modelMatrix, z$ths_per, 10, groups = gr9))

k18[c(4, 13, 17, 33), ]
k9[c(4, 13, 17, 33), ]

# Group info obtained with return2 = TRUE
k9_id <- cbind(b$crossTable, MaxContribution(b$modelMatrix, z$ths_per, 10, groups = gr9, 
                                             return2 = TRUE)$id)
k9_id[c(4, 13, 17, 33), ]


# Verify similarity
z$y <- z$ths_per + (1:nrow(z))/100  # to avoid equal values
id1 <- MaxContribution(b$modelMatrix, z$y, 10, index = TRUE)
id1[!is.na(id1)] <- paste0("g", id1[!is.na(id1)])
mc2 <- MaxContribution(b$modelMatrix, z$y, 10, groups = gr18, return2 = TRUE)
id2 <- mc2$id
identical(id1, id2)

---

Find the number of unique groups contributing to aggregates

Description

Assuming aggregates are calculated via a dummy matrix by z = t(x) %*% y, the the number of unique contributing groups, according to a grouping variable, are found for each aggregate. The missing group category is not counted.

Usage

Ncontributors(x, groups)

Arguments

x	A (sparse) dummy matrix
groups	Vector of group categories

Value

Vector of numbers of unique groups

Author(s)

Øyvind Langsrud

See Also

ModelMatrix

Examples

library(SSBtools)

z <- SSBtoolsData("sprt_emp_withEU")
z$age[z$age == "Y15-29"] <- "young"
z$age[z$age == "Y30-64"] <- "old"
z$groups <- c("A", "A", "B", "A", "B", "C")

a <- ModelMatrix(z, formula = ~age*eu + geo + year, crossTable = TRUE)

cbind(as.data.frame(a$crossTable), nGroups = Ncontributors(a$modelMatrix, z$groups))
cbind(as.data.frame(a$crossTable), nYears = Ncontributors(a$modelMatrix, z$year))
cbind(as.data.frame(a$crossTable), nUnique_ths_per = Ncontributors(a$modelMatrix, z$ths_per))

---

Ncontributors with holding-indicator

Description

The aggregates (columns of x) are grouped by a holding indicator. Within each holding group, the number of unique groups (output) is set to be equal.

Usage

NcontributorsHolding(x, groups, holdingInd = NULL)

Arguments

x	A (sparse) dummy matrix
groups	Vector of group categories
holdingInd	Vector of holding group categories

Details

A representative within the holding group is used to calculate output by Ncontributors. The one with maximal column sum of x is chosen as the representative. Normally this will be an aggregate representing the holding group total. When holdingInd is NULL (default), the function is equivalent to Ncontributors.

Value

Vector of numbers of unique groups

Author(s)

Øyvind Langsrud

---

Number of contributors suppression rule

Description

The number of contributors is the number unique contributing 'charVar' codes.

Usage

NContributorsRule(
  data,
  freq,
  numVar,
  x,
  maxN = 3,
  protectZeros = FALSE,
  charVar = NULL,
  removeCodes = character(0),
  remove0 = TRUE,
  ...
)

Arguments

data	Input data as a data frame
freq	Vector of aggregate frequencies
numVar	Numerical variables. When several variables, only first is used.
x	Model matrix generated by parent function
maxN	Primary suppression when number of contributors ⁠<= maxN⁠.
protectZeros	Suppression parameter. Only TRUE (default) is used implemented.
charVar	Variable(s) with contributor codes. When empty, unique contributor in each row is assumed. When several variables, see details.
removeCodes	Vector of codes to be omitted when counting contributors. With empty charVar row indices are assumed and conversion to integer is performed.
remove0	When set to TRUE (default), data rows in which the first numVar (if any) is zero are excluded from the count of contributors. Alternatively, remove0 can be specified as one or more variable names. In this case, all data rows with a zero in any of the specified variables are omitted from the contributor count. Specifying remove0 as variable name(s) is useful for avoiding warning when there are multiple numVar variables.
...	unused parameters

Details

When several charVar variables, the rule is applied independently to each variable. Primary suppression in at least one case results in primary suppression in the output. It is possible to specify maxN and removeCodes independently for each charVar by using a named list as input with charVar as names. E.g. maxN = list(char1 = 3, char2 = 2).

Value

List where first element is logical vector defining primary suppressions. The second element is data frame where nRule is number contributors used in rule and where nAll is similar, but without omitting codes in removeCodes.

---

Function for viewing built-in GaussSuppression specs

Description

Functions to retrieve the built-in specs. These can be retrieved using either numerical indices or by specifying the spec name, see Details.

Usage

PackageSpecs(x = NULL, printTable = FALSE)

Arguments

x	the character name or index of the spec to be returned. If NULL (default), returns list of all specs
printTable	Logical value (default FALSE). If TRUE, prints a table description of all specs. Primarily used for documentation purposes.

Details

The following table summarizes the built-in specs. Columns represent different specs, and rows represent the parameter settings.

	smallCountSpec	dominanceSpec	fewContributorsSpec	kDisclosureSpec
primary	PrimaryDefault	MagnitudeRule	NContributorsRule	KDisclosurePrimary
protectZeros	TRUE	FALSE	FALSE	FALSE
candidates	CandidatesDefault	CandidatesNum	CandidatesNum	DirectDisclosureCandidates
singleton	SingletonDefault	SingletonUniqueContributor	SingletonUniqueContributor	SingletonDefault
extend0	TRUE	FALSE	FALSE	TRUE
preAggregate	is.null(freqVar)	!is.null(charVar)	!is.null(charVar)	is.null(freqVar)
extraAggregate	FALSE	TRUE	TRUE	FALSE
secondaryZeros	FALSE	FALSE	FALSE	1
domWeightMethod		"default"
singletonMethod		"numttHTT"	"numttHTT"	"anySumNOTprimary"

Value

returns a spec (if !is.null(x)), list of all specs (if is.null(x) and printTable = FALSE), or markdown table describing all specs (if printTable = TRUE).

Examples

PackageSpecs()
PackageSpecs(1)
PackageSpecs("smallCountSpec")
PackageSpecs(printTable = TRUE)

---

Default primary function

Description

Function for GaussSuppressionFromData

Usage

PrimaryDefault(freq, maxN = 3, protectZeros = TRUE, ...)

Arguments

freq	Vector of output frequencies
maxN	Cells with frequency ⁠<= maxN⁠ are set as primary suppressed.
protectZeros	When TRUE, cells with zero frequency are set as primary suppressed.
...	Unused parameters

Value

primary, GaussSuppression input

---

primary and forced from suppressed data

Description

Function for GaussSuppressionFromData

Usage

PrimaryFromSuppressedData(
  x,
  crossTable,
  suppressedData,
  forcedData = FALSE,
  totCode = FindTotCode2(x, crossTable),
  ...
)

ForcedFromSuppressedData(..., forcedData = TRUE)

NotPrimaryFromSuppressedData(..., forcedData = TRUE)

Arguments

x	A (sparse) dummy matrix
crossTable	crossTable generated by parent function
suppressedData	A data frame or a list of data frames as output from GaussSuppressionFromData. If the variable suppressed is not included, all rows are considered suppressed.
forcedData	When TRUE, the suppressed coding is swapped.
totCode	A named list of totals codes
...	Unused parameters

Details

ForcedFromSuppressedData uses forcedData = TRUE and hence a vector to be use as forced is generated. NotPrimaryFromSuppressedData is similar, but TRUE elements are replaced by NA's. Hence the result can be used as an extra primary vector to ensure that code combinations not suppressed according to suppressedData are forced not to be primary suppressed.

The variables used in suppressedData in addition to "suppressed" are those with matching names in crossTable. Others are ignored. For variables in crossTable not in suppressedData, only totals are considered. Others rows are ignored when mathing with suppressedData.

When suppressedData is a list, the final result is the union of individual results of each data frame.

Value

Logical vector to be used as GaussSuppression input

Examples

z2 <- SSBtoolsData("z2")

# Data to be used as suppressedData
a <- GaussSuppressionFromData(z2, c(1, 3, 4), 5, protectZeros = FALSE)

# For alternative ways to suppress the same table
b1 <- GaussSuppressionFromData(z2, 1:4, 5)
b2 <- GaussSuppressionFromData(z2, 1:4, 5, primary = c(PrimaryDefault, PrimaryFromSuppressedData),
                               suppressedData = a)
b3 <- GaussSuppressionFromData(z2, 1:4, 5, primary = c(PrimaryDefault, PrimaryFromSuppressedData),
                               suppressedData = a, forced = ForcedFromSuppressedData)
b4 <- GaussSuppressionFromData(z2, 1:4, 5, 
             primary = c(PrimaryDefault, PrimaryFromSuppressedData, NotPrimaryFromSuppressedData),
                               suppressedData = a, forced = ForcedFromSuppressedData)

# Reducing data to rows mathing a
b1r <- b1[SSBtools::Match(a[1:2], b1[1:2]), ]
b2r <- b2[SSBtools::Match(a[1:2], b2[1:2]), ]
b3r <- b3[SSBtools::Match(a[1:2], b3[1:2]), ]
b4r <- b4[SSBtools::Match(a[1:2], b4[1:2]), ]


# Look at rows where new suppression is different from that in a  

# Both TRUE and FALSE changed
cbind(a, b1r)[b1r$suppressed != a$suppressed, c(1:5, 9:10)]

# Only  FALSE changed to TRUE (suppression is preserved)
cbind(a, b2r)[b2r$suppressed != a$suppressed, c(1:5, 9:10)]

# Only change is due to new primary suppression rule (protectZeros = TRUE) 
cbind(a, b3r)[b3r$suppressed != a$suppressed, c(1:5, 9:10)]

# No changes 
cbind(a, b4r)[b4r$suppressed != a$suppressed, c(1:5, 9:10)]

---

Special functions for the avoidance of suppression

Description

The SSBtools function WildcardGlobbing is utilized

Usage

PrimaryRemoveWg(wg = NULL, ..., crossTable)

CandidatesNumWg(wg = NULL, ..., crossTable)

ForcedWg(crossTable, wg = NULL, ...)

Arguments

wg	data.frame with wildcard/globbing. A parameter to WildcardGlobbing
...	unused parameters
crossTable	crossTable generated by parent function

Details

CandidatesNumWg is a generalization of CandidatesNumWg

Value

logical vector or row indices

Examples

dataset <- SSBtoolsData("magnitude1")

a1 <- SuppressDominantCells(data = dataset, numVar = "value", 
       dimVar = c("sector4", "geo"), n = 1:2, k = c(77, 99))

a1

wg <- data.frame(sector4 = "Ind*", geo = c("Ice????", "Portugal"))
wg

# Industry:Portugal not primary, but suppressed
a2 <- SuppressDominantCells(data = dataset, numVar = "value", 
       dimVar = c("sector4", "geo"), n = 1:2, k = c(77, 99), 
       wg = wg, primary = c(DominanceRule, PrimaryRemoveWg))

a2

# Industry:Portugal not primary and not suppressed
a3 <- SuppressDominantCells(data = dataset, numVar = "value", 
       dimVar = c("sector4", "geo"), n = 1:2, k = c(77, 99), 
       wg = wg, primary = c(DominanceRule, PrimaryRemoveWg), 
       candidates = CandidatesNumWg)
a3

# Industry:Portugal primary, but not suppressed
a4 <- SuppressDominantCells(data = dataset, numVar = "value", 
       dimVar = c("sector4", "geo"), n = 1:2, k = c(77, 99), 
       wg = wg, forced = ForcedWg, whenPrimaryForced = message)
a4

---

Default range limit function

Description

Preliminary function

Usage

RangeLimitsDefault(
  ...,
  rangePercent = 0,
  rangeMin = 0,
  primary,
  num,
  freq,
  freqVar,
  dominanceVar = NULL,
  intervalVar = NULL
)

Arguments

...	Unused parameters
rangePercent	Required interval width expressed as a percentage
rangeMin	Minimum required width of the interval
primary	primary
num	num
freq	freq
freqVar	freqVar
dominanceVar	dominanceVar
intervalVar	Numerical variable(s) for interval calculations. When NULL, dominanceVar, first numVar or freqVar will be used.

Value

matrix with named columns

Examples

dat <- SSBtoolsData("magnitude1")
dat["num2"] <- 1:nrow(dat)

SuppressDominantCells(data = dat, 
    numVar = "value", 
    formula = ~sector2 * geo + sector4 * eu, 
    contributorVar = "company", 
    n = 1:2, k = c(80, 99), 
    output = RangeOutputFunction, rangePercent = 10, rangeMin = 1)

SuppressDominantCells(data = dat, 
    numVar = c("value", "num2"), 
    formula = ~sector2 * geo + sector4 * eu, 
    contributorVar = "company", 
    n = 1:2, k = c(80, 99), 
    output = RangeOutputFunction, 
    intervalVar = c("value","freq", "num2"), 
    rangePercent = c(10, 10, 30), rangeMin = c(1, 0.2222, 2.222))

---

Default singleton function

Description

Function for GaussSuppressionFromData

Usage

SingletonDefault(data, freqVar, protectZeros, secondaryZeros, ...)

Arguments

data	Input data, possibly pre-aggregated within GaussSuppressionFromData
freqVar	A single variable holding counts (input to GaussSuppressionFromData)
protectZeros	Suppression parameter (see GaussSuppressionFromData)
secondaryZeros	Suppression parameter (see GaussSuppressionFromData)
...	Unused parameters

Details

This function marks input cells as singletons according to the input frequencies (freqVar). Zero frequencies are set to singletons when protectZeros or secondaryZeros is TRUE. Otherwise, ones are set to singletons. Empty freqVar is treated as all frequencies being ones.

Value

singleton, GaussSuppression input

---

Unique contributor singleton function

Description

Function for GaussSuppressionFromData

Usage

SingletonUniqueContributor(
  data,
  freqVar = NULL,
  nUniqueVar = NULL,
  charVar = NULL,
  removeCodes = character(0),
  integerSingleton = length(charVar) > 0,
  x,
  primary = integer(0),
  whenPrimaryMatters = warning,
  whenNoVar = TRUE,
  specialMultiple = TRUE,
  rowGroupsPackage = "base",
  ...
)

SingletonUniqueContributor0(data, numVar, dominanceVar = NULL, ...)

Arguments

data	Input data, possibly pre-aggregated within GaussSuppressionFromData
freqVar	A single variable holding counts (input to GaussSuppressionFromData)
nUniqueVar	A single variable holding the number of unique contributors.
charVar	Variable with contributor codes.
removeCodes	Vector, list or data frame of codes considered non-singletons. Single element lists and single column data frames behave just like vectors. In other cases, charVar-names must be used. With empty charVar a vector of row indices is assumed and conversion to integer is performed. See examples.
integerSingleton	Integer output when TRUE. See details.
x	ModelMatrix generated by parent function
primary	Vector (integer or logical) specifying primary suppressed cells. It will be ensured that any non-suppressed inner cell is not considered a singleton.
whenPrimaryMatters	Function to be called when primary caused non-singleton. Supply NULL to do nothing.
whenNoVar	When TRUE, and without nUniqueVar and freqVar in input, all cells will be marked as singletons.
specialMultiple	When TRUE, and when ⁠integerSingleton &⁠ length(charVar) > 1 ⁠& length(nUniqueVar)⁠, a special method is used. By re-coding to single charVar and by re-calculating nUnique. To be unique (nUnique=1), uniqueness is only required for a single charvar. Otherwise, the charvar combination must be unique.
rowGroupsPackage	Parameter pkg to RowGroups.
...	Unused parameters
numVar	vector containing numeric values in the data set
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.

Details

This function marks input cells as singletons according to ones in data[[nUniqueVar]], if available, and otherwise according to data[[freqVar]]. The output vector can be logical or integer. When, integer, singletons are given as positive values. Their unique values represent the unique values/combinations of data[[charVar]].

Value

logical or integer vector

Note

SingletonUniqueContributor0 is a special version that produces singleton as a two-element list. See GaussSuppression and SuppressDominantCells.

Examples

S <- function(data, ...) {
  cbind(data, singleton = SingletonUniqueContributor(data, ...))
}
d2 <- SSBtoolsData("d2")
d <- d2[d2$freq < 5, ]
d$nUnique <- round((5 - d$freq)/3)
d$freq <- round(d$freq/2)
d[7:8, 2:4] <- NA
rownames(d) <- NULL

S(d, freqVar = "freq", integerSingleton = FALSE)
S(d, freqVar = "freq", nUniqueVar = "nUnique", integerSingleton = TRUE, charVar = "main_income")
S(d, nUniqueVar = "nUnique", integerSingleton = TRUE, charVar = c("main_income", "k_group"))
S(d, freqVar = "freq", nUniqueVar = "nUnique", integerSingleton = FALSE, 
  charVar = "main_income", removeCodes = "other")
S(d, nUniqueVar = "nUnique", integerSingleton = FALSE, charVar = c("main_income", "k_group"), 
  removeCodes = c("other", "400"))
S(d, nUniqueVar = "nUnique", integerSingleton = FALSE, charVar = c("main_income", "k_group"), 
  removeCodes = data.frame(anyname = c("other", "400")))
S(d, nUniqueVar = "nUnique", integerSingleton = FALSE, charVar = c("main_income", "k_group"), 
  removeCodes = list(main_income = c("other", "pensions"), k_group = "300"))
S(d, nUniqueVar = "nUnique", integerSingleton = FALSE, charVar = c("main_income", "k_group"), 
  removeCodes = data.frame(main_income = "other", k_group = "400"))
S(d, nUniqueVar = "nUnique", integerSingleton = FALSE, removeCodes = 1:5)

x <- SSBtools::ModelMatrix(d, hierarchies = list(region = "Total"))
which(colSums(x) == 1)
which(rowSums(x[, colSums(x) == 1]) > 0)
# columns 2, 3, 4, 5, 7 correspond to inner cells: rows 3, 4, 5, 6, 8 
# with 2:4 not primary rows 3:5 are forced non-singleton
S(d, freqVar = "freq", nUniqueVar = "nUnique", integerSingleton = FALSE, x = x, primary = 5:8)

---

Suppression of directly-disclosive cells

Description

Function for suppressing directly-disclosive cells in frequency tables. The method detects and primary suppresses directly-disclosive cells with the FindDisclosiveCells function, and applies a secondary suppression using Gauss suppression (see GaussSuppressionFromData).

Usage

SuppressDirectDisclosure(
  data,
  dimVar,
  freqVar,
  coalition = 1,
  secondaryZeros = coalition,
  candidates = DirectDisclosureCandidates,
  ...
)

Arguments

data	the input data
dimVar	main dimensional variables for the output table
freqVar	variable containing frequency counts
coalition	numeric variable, parameter for primary suppression. Default value is 1.
secondaryZeros	logical or numeric value for secondary suppression. If logical, it is converted to resp numeric value (0 or 1). If numeric, it describes the largest number that is prioritized over zeroes in secondary suppression. Default value is equal to coalition.
candidates	function parameter for gauss suppression.
...	optional parameters that can be passed to the primary suppression method. See FindDisclosiveCells for details. In the case of SuppressDirectDisclosure2, ... are parameters to GaussSuppressionFromData.

Details

SuppressDirectDisclosure has no support for hierarchical data. SuppressDirectDisclosure2 has, but is less general in other ways.

Value

data.frame containing the result of the suppression

Author(s)

Daniel Lupp

Examples

tex <- data.frame(v1 = rep(c('a', 'b', 'c'), times = 4),
                  v2 = c('i','i', 'i','h','h','h','i','i','i','h','h','h'),
                  v3 = c('y', 'y', 'y', 'y', 'y', 'y','z','z', 'z', 'z', 'z', 'z'),
                  freq = c(0,0,5,0,2,3,1,0,3,1,1,2))
SuppressDirectDisclosure(tex, c("v1", "v2", "v3"), "freq")
SuppressDirectDisclosure(tex, c("v1", "v2", "v3"), "freq", coalition = 2, unknown.threshold = 10)

z3 <- SSBtools::SSBtoolsData("z3")
a1 <- SuppressDirectDisclosure(z3, c(1, 4, 5), 7)
b1 <- try(SuppressDirectDisclosure(z3, 1:6, 7))

---

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"))

---

Few contributors suppression

Description

This function provides functionality for suppressing volume tables based on the few contributors rule (NContributorsRule).

Usage

SuppressFewContributors(
  data,
  maxN,
  numVar = NULL,
  dimVar = NULL,
  hierarchies = NULL,
  formula = NULL,
  contributorVar = NULL,
  removeCodes = character(0),
  remove0 = TRUE,
  candidatesVar = NULL,
  ...,
  spec = PackageSpecs("fewContributorsSpec")
)

Arguments

data	Input data as a data frame
maxN	Suppression parameter. Cells with frequency ⁠<= maxN⁠ are set as primary suppressed. Using the default primary function, maxN is by default set to 3. See details.
numVar	Numerical variable to be aggregated. Any candidatesVar that is specified and not included in numVar will be aggregated accordingly. Additionally, if remove0 is specified as a variable name and it is not included in numVar, it will also be aggregated accordingly. See parameters candidatesVar and remove0 below.
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 when counting contributors. Typically, the variable contains the contributor IDs.
removeCodes	Vector of codes to be omitted when counting contributors. With empty contributorVar row indices are assumed and conversion to integer is performed.
remove0	When set to TRUE (default), data rows in which the first numVar (if any) is zero are excluded from the count of contributors. Alternatively, remove0 can be specified as one or more variable names. In this case, all data rows with a zero in any of the specified variables are omitted from the contributor count. Specifying remove0 as variable name(s) is useful for avoiding warning when there are multiple numVar variables.
candidatesVar	Variable to be used in the candidate function to prioritize cells for publication and thus not suppression. The first numVar variable will be used if it is not specified.
...	Further arguments to be passed to the supplied functions and to ModelMatrix (such as inputInOutput and removeEmpty).
spec	NULL or a named list of arguments that will act as default values.

Value

data.frame containing aggregated data and supppression information. Columns nRule and nAll contain the number of contributors. In the former, removeCodes is taken into account.

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)

SuppressFewContributors(d, formula = ~v1, maxN = 1, numVar = "num")
SuppressFewContributors(d, formula = ~v1, maxN = 2, numVar = "num")
SuppressFewContributors(d, formula = ~v1, maxN = 3, numVar = "num")


d2 <- SSBtoolsData("d2")[-5]
set.seed(123)
d2$v <- round(rnorm(nrow(d2))^2, 1)
d2$family_id <- round(2*as.integer(factor(d2$region)) + runif(nrow(d2)))

# Hierarchical region variables are detected automatically -> same output column
SuppressFewContributors(data = d2, maxN = 2, numVar = "v", contributorVar = "family_id",
                      dimVar = c("region", "county", "k_group"))

# Formula. Hierarchical variables still detected automatically.
# And codes 1:9 not counted 
SuppressFewContributors(data = d2, maxN = 1, numVar = "v", contributorVar = "family_id",
                      formula = ~main_income * k_group + region + county - k_group,
                      removeCodes = 1:9)

# With hierarchies created manually
ml <- data.frame(levels = c("@", "@@", "@@@", "@@@", "@@@", "@@"), 
        codes = c("Total", "not_assistance", "other", "pensions", "wages", "assistance"))
SuppressFewContributors(data = d2, maxN = 2, numVar = "v", contributorVar = "family_id",
                      hierarchies = list(main_income = ml, k_group = "Total_Norway"))

---

Cell suppression from synthetic decimal numbers

Description

Decimal numbers, as calculated by GaussSuppressDec, are used to decide suppression (whole numbers or not). Technically, the calculations are done via GaussSuppressionFromData, but without running GaussSuppression. All suppressed cells are primary suppressed.

Usage

SuppressionFromDecimals(
  data,
  decVar,
  freqVar = NULL,
  numVar = NULL,
  preAggregate = FALSE,
  digits = 9,
  ...
)

Arguments

data	Input data as a data frame
decVar	One ore several (nRep>1) decimal number variables.
freqVar	A single variable holding counts (not needed)
numVar	Other numerical variables to be aggregated
preAggregate	Parameter to GaussSuppressionFromData
digits	Parameter to RoundWhole. Values close to whole numbers will be rounded.
...	Other parameters to GaussSuppressionFromData

Details

Several decimal number variables reduce the probability of obtaining whole numbers by chance.

Value

Aggregated data with suppression information

Author(s)

Øyvind Langsrud

Examples

z2 <- SSBtoolsData("z2")

# Find suppression and decimal numbers with "fylke" in model 
a <- GaussSuppressDec(z2, dimVar = c("region", "fylke", "hovedint"), 
                      freqVar = "ant", protectZeros = FALSE, maxN = 2, 
                      output = "inner")

# Add decimal numbers to data 
z2$freqDec <- a$freqDec

# Find suppression with "kostragr" in model 
b <- SuppressionFromDecimals(z2, dimVar = c("region", "kostragr", "hovedint"), 
                             freqVar = "ant", decVar = "freqDec")

---

K-disclosure suppression

Description

A function for suppressing frequency tables using the k-disclosure method.

Usage

SuppressKDisclosure(
  data,
  coalition = 0,
  mc_hierarchies = NULL,
  upper_bound = Inf,
  dimVar = NULL,
  formula = NULL,
  hierarchies = NULL,
  freqVar = NULL,
  ...,
  spec = PackageSpecs("kDisclosureSpec")
)

Arguments

data	a data.frame representing the data set
coalition	numeric vector of length one, representing possible size of an attacking coalition. This parameter corresponds to the parameter k in the definition of k-disclosure.
mc_hierarchies	a hierarchy representing meaningful combinations to be protected. Default value is NULL.
upper_bound	numeric value representing minimum count considered safe. Default set to Inf
dimVar	The main dimensional variables and additional aggregating variables. This parameter can be useful when hierarchies and formula are unspecified.
formula	A model formula
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.
freqVar	name of the frequency variable in data
...	parameters passed to children functions
spec	NULL or a named list of arguments that will act as default values.

Value

A data.frame containing the publishable data set, with a boolean variable ⁠$suppressed⁠ representing cell suppressions.

Author(s)

Daniel P. Lupp

Examples

# data
data <- SSBtools::SSBtoolsData("mun_accidents")

# hierarchies as DimLists
mun <- data.frame(levels = c("@", rep("@@", 6)),
codes = c("Total", paste("k", 1:6, sep = "")))
inj <- data.frame(levels = c("@", "@@" ,"@@", "@@", "@@"),
codes = c("Total", "serious", "light", "none", "unknown"))
dimlists <- list(mun = mun, inj = inj)

inj2 <- data.frame(levels = c("@", "@@", "@@@" ,"@@@", "@@", "@@"),
codes = c("Total", "injured", "serious", "light", "none", "unknown"))
inj3 <- data.frame(levels = c("@", "@@", "@@" ,"@@", "@@"),
codes = c( "shadowtotal", "serious", "light", "none", "unknown"))
mc_dimlist <- list(inj = inj2)
mc_nomargs <- list(inj = inj3)

#' # Example with formula, no meaningful combination
out <- SuppressKDisclosure(data, coalition = 1, freqVar = "freq", formula = ~mun*inj)

# Example with hierarchy and meaningful combination
out2 <- SuppressKDisclosure(data, coalition = 1, freqVar = "freq",
hierarchies = dimlists, mc_hierarchies = mc_dimlist)

#' # Example of table without mariginals, and mc_hierarchies to protect
out3 <- SuppressKDisclosure(data, coalition = 1, freqVar = "freq",
formula = ~mun:inj, mc_hierarchies = mc_nomargs )

---

Small count frequency table suppression.

Description

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 as a data frame
maxN	Suppression parameter. Cells with frequency ⁠<= maxN⁠ are set as primary suppressed. Using the default primary function, maxN is by default set to 3. 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 as inputInOutput and removeEmpty).
spec	NULL or 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.

Value

data frame containing aggregated data and suppression information.

Examples

mun_accidents <- SSBtoolsData("mun_accidents")

SuppressSmallCounts(data = mun_accidents, maxN = 3, dimVar = 1:2, freqVar = 3)
# override default spec
SuppressSmallCounts(data = mun_accidents, maxN = 3, dimVar = 1:2, freqVar = 3, 
                    protectZeros = 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"))

# Formula. Hierarchical variables still detected automatically.
SuppressSmallCounts(data = d2, maxN = 3, freqVar = "f", 
                    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"))
SuppressSmallCounts(data = d2, maxN = 2, freqVar = "f", 
                    hierarchies = list(main_income = ml, k_group = "Total_Norway"))


# 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)
# -- 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.

---