Cell suppression from input data containing inner cells — GaussSuppressionFromData • GaussSuppression

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,
  intervalSuppression = TRUE,
  aggregatePackage = "base",
  aggregateNA = TRUE,
  aggregateBaseOrder = FALSE,
  rowGroupsPackage = aggregatePackage,
  linkedGauss = NULL,
  linkedIntervals = ifelse(linkedGauss == "local-bdiag", "local-bdiag",
    "super-consistent"),
  recordAware = TRUE,
  collapseAware = FALSE,
  linkedTables = NULL,
  action_unused_dots = getOption("GaussSuppression.action_unused_dots", "inform"),
  allowed_unused_dots = getOption("GaussSuppression.allowed_unused_dots", character(0))
)

Arguments

data

Input data, typically a data frame, tibble, or data.table. If data is not a classic data frame, it will be coerced to one internally unless preAggregate is TRUE and aggregatePackage is "data.table".

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 forwarded to the supplied functions. With the default primary function, PrimaryDefault(), cells with frequency <= maxN are marked as primary suppressed, and the default value of maxN is 3. See details below. The parameter is also used by NContributorsRule(). For advanced use cases, including setups with multiple primary functions, maxN can be specified as a named list or vector. See each primary function’s documentation for 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. When FALSE, no aggregation is performed. When NA, the function will automatically decide whether to aggregate: aggregation is applied unless freqVar is present and the data contain no duplicated rows with respect to the dimensional variables and charVar. Exception: if a non-NULL x (the model matrix) is supplied, NA is treated as FALSE.

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

When non-NULL, intervals computed by ComputeIntervals() will be included in the output. Valid values are the names of supported R packages for linear programming backends: "highs", "Rsymphony", "Rglpk", or "lpSolve".
If interval requirements are specified, additional suppression will be performed to satisfy those requirements. Interval requirements can be set either through arguments of IntervalLimits() or by enabling protectionIntervals = TRUE in the primary suppression functions. See IntervalLimits() for a full description of the parameters (protectionPercent, protectionLimit, loProtectionPercent, loProtectionLimit, rangePercent, rangeMin) and how interval requirements are calculated.
- In the output variable suppressed_integer, suppression status is coded as: 0 = no suppression, 1 = primary suppression, 2 = secondary suppression, 3 = additional suppression applied by an interval algorithm limited to linearly independent cells, 4 = further suppression according to the final gauss algorithm.
- Intervals [lo_1, up_1] are calculated prior to additional suppression.
- To disable additional suppression, set intervalSuppression = FALSE.
```
         Please note that additional suppression based on parameters other than
         rangePercent and rangeMin is currently considered experimental.
         In particular, the names of the newer parameters may still change.
```

intervalSuppression

Logical. If FALSE, additional suppression to satisfy interval requirements is disabled (default is TRUE). See description of lpPackage above.

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. Note that NAs will not be present in the output table's dimensions regardless of the value of aggregateNA. When using the formula interface, this is controlled by the NAomit parameter (default TRUE), which is passed to the function SSBtools::Formula2ModelMatrix(). It is through this use of the formula interface that NAs in the input data make sense. Note that under normal circumstances, grouping variables should not use NA to represent a category. As such, if NAs are present in the grouping variables, using the dimVar or hierarchies interfaces will result in errors.

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.

linkedGauss

Controls linked table suppression. Accepted values are described in the documentation for SuppressLinkedTables(). See also the note and the corresponding examples, which demonstrate usage with alternative function interfaces. In addition, linkedGauss = "global" is allowed and corresponds to standard execution (i.e., when linkedGauss is not specified). When linkedGauss is used, the formula parameter should be provided as a list of formulas. Alternatively, formula may have an attribute "table_formulas" containing such a list. See also the linkedTables parameter below.

linkedIntervals

Determines how interval calculations, triggered by the lpPackage parameter, are performed when linkedGauss is not "global". When linkedGauss = "global", interval settings in linkedIntervals are ignored. For allowed values and detailed behaviour, see the documentation of SuppressLinkedTables().

Note: With linkedIntervals = "local-bdiag", common cells may have different table-specific intervals. Since the output shows one interval per cell, it is constructed using the maximum lower bound and minimum upper bound across the tables.

recordAware

Parameter associated with linkedGauss. See SuppressLinkedTables().

collapseAware

Parameter associated with linkedGauss. In the linked‑tables algorithm, the model matrix is first collapsed by removing duplicate rows. When collapseAware = TRUE, every cell that remains numerically derivable after a pre‑aggregation corresponding to this row reduction will be treated as a common cell. This maximizes coordination across tables, given the duplicate‑row removal, while adding limited additional computational overhead. In particular, the suppression algorithm automatically accounts for cells in one table that are sums of cells in another table. Note that any cell that recordAware = TRUE would introduce is already included automatically when collapseAware = TRUE.

linkedTables

A list specifying how the tables referenced in the formula parameter should be combined for use in the linked-tables algorithm. Each element in the list contains one or more names of the tables in formula. The corresponding tables will be combined and treated as a single table by the algorithm. For example: linkedTables = list(c("table_1", "table_3"), "table_2"). If NULL (default), each table in formula is used individually.

action_unused_dots

Character string controlling how unused arguments in ... are handled. Internally uses ellipsis::check_dots_used() with a custom action. One of "warn", "abort", "inform", or "none". The value "none" disables the check entirely. The default is taken from getOption("GaussSuppression.action_unused_dots"), falling back to "inform" if the option is not set. Users can change the default globally with e.g. options(GaussSuppression.action_unused_dots = "abort").

allowed_unused_dots

Character vector of argument names ignored by the unused-argument check. May be useful when this function is wrapped by another function, or in other cases where a correctly spelled argument is nevertheless not registered as used. The default is taken from getOption("GaussSuppression.allowed_unused_dots"), falling back to character(0) if the option is not set. Users can change the default globally with e.g. options(GaussSuppression.allowed_unused_dots = c("plotColor", "lineType")).

Value

Aggregated data with suppression information

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, structuralEmpty, 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.

Author

Øyvind Langsrud and Daniel Lupp

Examples


z1 <- SSBtoolsData("z1")
GaussSuppressionFromData(z1, 1:2, 3)
#> GaussSuppression_anySum: ..........................................
#>    region hovedint ant primary suppressed
#> 1   Total    Total 596   FALSE      FALSE
#> 2   Total    annet  72   FALSE      FALSE
#> 3   Total   arbeid  52   FALSE      FALSE
#> 4   Total soshjelp 283   FALSE      FALSE
#> 5   Total    trygd 189   FALSE      FALSE
#> 6       A    Total 113   FALSE      FALSE
#> 7       A    annet  11   FALSE      FALSE
#> 8       A   arbeid  11   FALSE      FALSE
#> 9       A soshjelp  55   FALSE      FALSE
#> 10      A    trygd  36   FALSE      FALSE
#> 11      B    Total  55   FALSE      FALSE
#> 12      B    annet   7   FALSE       TRUE
#> 13      B   arbeid   1    TRUE       TRUE
#> 14      B soshjelp  29   FALSE      FALSE
#> 15      B    trygd  18   FALSE      FALSE
#> 16      C    Total  73   FALSE      FALSE
#> 17      C    annet   5   FALSE      FALSE
#> 18      C   arbeid   8   FALSE      FALSE
#> 19      C soshjelp  35   FALSE      FALSE
#> 20      C    trygd  25   FALSE      FALSE
#> 21      D    Total  45   FALSE      FALSE
#> 22      D    annet  13   FALSE       TRUE
#> 23      D   arbeid   2    TRUE       TRUE
#> 24      D soshjelp  17   FALSE      FALSE
#> 25      D    trygd  13   FALSE      FALSE
#> 26      E    Total 138   FALSE      FALSE
#> 27      E    annet   9   FALSE      FALSE
#> 28      E   arbeid  14   FALSE      FALSE
#> 29      E soshjelp  63   FALSE      FALSE
#> 30      E    trygd  52   FALSE      FALSE
#> 31      F    Total  67   FALSE      FALSE
#> 32      F    annet  12   FALSE      FALSE
#> 33      F   arbeid   9   FALSE      FALSE
#> 34      F soshjelp  24   FALSE      FALSE
#> 35      F    trygd  22   FALSE      FALSE
#> 36      G    Total  40   FALSE      FALSE
#> 37      G    annet   6   FALSE      FALSE
#> 38      G   arbeid   4   FALSE      FALSE
#> 39      G soshjelp  22   FALSE      FALSE
#> 40      G    trygd   8   FALSE      FALSE
#> 41      H    Total  65   FALSE      FALSE
#> 42      H    annet   9   FALSE       TRUE
#> 43      H   arbeid   3    TRUE       TRUE
#> 44      H soshjelp  38   FALSE      FALSE
#> 45      H    trygd  15   FALSE      FALSE

z2 <- SSBtoolsData("z2")
GaussSuppressionFromData(z2, 1:4, 5, protectZeros = FALSE)
#> GaussSuppression_anySum: .............................
#>     region hovedint ant primary suppressed
#> 1        1    Total 127   FALSE      FALSE
#> 2        1    annet  14   FALSE      FALSE
#> 3        1   arbeid  11   FALSE      FALSE
#> 4        1 soshjelp  64   FALSE      FALSE
#> 5        1    trygd  38   FALSE      FALSE
#> 6       10    Total  96   FALSE      FALSE
#> 7       10    annet  13   FALSE      FALSE
#> 8       10   arbeid   2    TRUE       TRUE
#> 9       10 soshjelp  50   FALSE      FALSE
#> 10      10    trygd  31   FALSE       TRUE
#> 11     300    Total 596   FALSE      FALSE
#> 12     300    annet  72   FALSE       TRUE
#> 13     300   arbeid  52   FALSE       TRUE
#> 14     300 soshjelp 283   FALSE      FALSE
#> 15     300    trygd 189   FALSE      FALSE
#> 16       4    Total  55   FALSE      FALSE
#> 17       4    annet   7   FALSE      FALSE
#> 18       4   arbeid   1    TRUE       TRUE
#> 19       4 soshjelp  29   FALSE      FALSE
#> 20       4    trygd  18   FALSE       TRUE
#> 21     400    Total 110   FALSE      FALSE
#> 22     400    annet  16   FALSE       TRUE
#> 23     400   arbeid   2    TRUE       TRUE
#> 24     400 soshjelp  59   FALSE      FALSE
#> 25     400    trygd  33   FALSE      FALSE
#> 26       5    Total 118   FALSE      FALSE
#> 27       5    annet  18   FALSE      FALSE
#> 28       5   arbeid  10   FALSE      FALSE
#> 29       5 soshjelp  52   FALSE      FALSE
#> 30       5    trygd  38   FALSE      FALSE
#> 31       6    Total 205   FALSE      FALSE
#> 32       6    annet  21   FALSE      FALSE
#> 33       6   arbeid  23   FALSE      FALSE
#> 34       6 soshjelp  87   FALSE      FALSE
#> 35       6    trygd  74   FALSE      FALSE
#> 36       8    Total 105   FALSE      FALSE
#> 37       8    annet  15   FALSE      FALSE
#> 38       8   arbeid   7   FALSE      FALSE
#> 39       8 soshjelp  60   FALSE      FALSE
#> 40       8    trygd  23   FALSE      FALSE
#> 41   Total    Total 706   FALSE      FALSE
#> 42   Total    annet  88   FALSE      FALSE
#> 43   Total   arbeid  54   FALSE      FALSE
#> 44   Total soshjelp 342   FALSE      FALSE
#> 45   Total    trygd 222   FALSE      FALSE
#> 46       A    Total 113   FALSE      FALSE
#> 47       A    annet  11   FALSE       TRUE
#> 48       A   arbeid  11   FALSE      FALSE
#> 49       A soshjelp  55   FALSE      FALSE
#> 50       A    trygd  36   FALSE       TRUE
#> 51       B    Total  55   FALSE      FALSE
#> 52       B    annet   7   FALSE      FALSE
#> 53       B   arbeid   1    TRUE       TRUE
#> 54       B soshjelp  29   FALSE      FALSE
#> 55       B    trygd  18   FALSE       TRUE
#> 56       C    Total  73   FALSE      FALSE
#> 57       C    annet   5   FALSE       TRUE
#> 58       C   arbeid   8   FALSE       TRUE
#> 59       C soshjelp  35   FALSE      FALSE
#> 60       C    trygd  25   FALSE      FALSE
#> 61       D    Total  45   FALSE      FALSE
#> 62       D    annet  13   FALSE       TRUE
#> 63       D   arbeid   2    TRUE       TRUE
#> 64       D soshjelp  17   FALSE      FALSE
#> 65       D    trygd  13   FALSE      FALSE
#> 66       E    Total 138   FALSE      FALSE
#> 67       E    annet   9   FALSE      FALSE
#> 68       E   arbeid  14   FALSE      FALSE
#> 69       E soshjelp  63   FALSE      FALSE
#> 70       E    trygd  52   FALSE      FALSE
#> 71       F    Total  67   FALSE      FALSE
#> 72       F    annet  12   FALSE      FALSE
#> 73       F   arbeid   9   FALSE      FALSE
#> 74       F soshjelp  24   FALSE      FALSE
#> 75       F    trygd  22   FALSE      FALSE
#> 76       G    Total  40   FALSE      FALSE
#> 77       G    annet   6   FALSE       TRUE
#> 78       G   arbeid   4   FALSE       TRUE
#> 79       G soshjelp  22   FALSE      FALSE
#> 80       G    trygd   8   FALSE      FALSE
#> 81       H    Total  65   FALSE      FALSE
#> 82       H    annet   9   FALSE       TRUE
#> 83       H   arbeid   3    TRUE       TRUE
#> 84       H soshjelp  38   FALSE      FALSE
#> 85       H    trygd  15   FALSE      FALSE
#> 86       I    Total  14   FALSE      FALSE
#> 87       I    annet   3    TRUE       TRUE
#> 88       I   arbeid   0   FALSE      FALSE
#> 89       I soshjelp   9   FALSE      FALSE
#> 90       I    trygd   2    TRUE       TRUE
#> 91       J    Total  61   FALSE      FALSE
#> 92       J    annet   9   FALSE      FALSE
#> 93       J   arbeid   0   FALSE      FALSE
#> 94       J soshjelp  32   FALSE      FALSE
#> 95       J    trygd  20   FALSE      FALSE
#> 96       K    Total  35   FALSE      FALSE
#> 97       K    annet   4   FALSE      FALSE
#> 98       K   arbeid   2    TRUE       TRUE
#> 99       K soshjelp  18   FALSE      FALSE
#> 100      K    trygd  11   FALSE       TRUE


# 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")
#> GaussSuppression_anySum: ..................
#>     var1  var2 values primary suppressed
#> 1  Total Total     72   FALSE      FALSE
#> 2  Total     A     10   FALSE      FALSE
#> 3  Total     B     10   FALSE      FALSE
#> 4  Total     C     10   FALSE      FALSE
#> 5  Total     D     21   FALSE      FALSE
#> 6  Total     E     21   FALSE      FALSE
#> 7      1 Total     13   FALSE      FALSE
#> 8      1     A      1    TRUE       TRUE
#> 9      1     B      1    TRUE       TRUE
#> 10     1     C      1    TRUE       TRUE
#> 11     1     D      5   FALSE      FALSE
#> 12     1     E      5   FALSE       TRUE
#> 13     2 Total     45   FALSE      FALSE
#> 14     2     A      9   FALSE      FALSE
#> 15     2     B      9   FALSE      FALSE
#> 16     2     C      9   FALSE      FALSE
#> 17     2     D      9   FALSE      FALSE
#> 18     2     E      9   FALSE      FALSE
#> 19     3 Total     14   FALSE      FALSE
#> 20     3     A      0    TRUE       TRUE
#> 21     3     B      0    TRUE       TRUE
#> 22     3     C      0    TRUE       TRUE
#> 23     3     D      7   FALSE      FALSE
#> 24     3     E      7   FALSE       TRUE
GaussSuppressionFromData(df, c("var1", "var2"), "values", formula = ~var1 + var2, maxN = 10)
#> GaussSuppression_anySum: ......
#>    var1  var2 values primary suppressed
#> 1 Total Total     72   FALSE      FALSE
#> 2     1 Total     13   FALSE      FALSE
#> 3     2 Total     45   FALSE      FALSE
#> 4     3 Total     14   FALSE      FALSE
#> 5 Total     A     10    TRUE       TRUE
#> 6 Total     B     10    TRUE       TRUE
#> 7 Total     C     10    TRUE       TRUE
#> 8 Total     D     21   FALSE      FALSE
#> 9 Total     E     21   FALSE      FALSE
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"))
#> GaussSuppression_anySum: ........
#>    var1  var2 values primary suppressed
#> 1 Total Total     72   FALSE      FALSE
#> 2     1 Total     13   FALSE      FALSE
#> 3     2 Total     45   FALSE      FALSE
#> 4     3 Total     14   FALSE      FALSE
#> 5 Total     A     10   FALSE      FALSE
#> 6 Total     B     10    TRUE       TRUE
#> 7 Total     C     10   FALSE       TRUE
#> 8 Total     D     21   FALSE      FALSE
#> 9 Total     E     21   FALSE      FALSE
                   
# 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"))                    
#> GaussSuppression_anySum: ......
#>    var1  var2 values primary suppressed
#> 1 Total Total     72   FALSE      FALSE
#> 2     1 Total     13   FALSE       TRUE
#> 3     2 Total     45    TRUE       TRUE
#> 4     3 Total     14   FALSE      FALSE
#> 5 Total     A     10    TRUE       TRUE
#> 6 Total     B     10    TRUE       TRUE
#> 7 Total     C     10   FALSE      FALSE
#> 8 Total     D     21   FALSE      FALSE
#> 9 Total     E     21   FALSE      FALSE
                   
# Similar to GaussSuppression examples
GaussSuppressionFromData(df, c("var1", "var2"), "values", formula = ~var1 * var2, 
       candidates = NULL, singleton = NULL, protectZeros = FALSE, secondaryZeros = TRUE)
#> GaussSuppression_none: .....................
#>     var1  var2 values primary suppressed
#> 1  Total Total     72   FALSE      FALSE
#> 2      1 Total     13   FALSE      FALSE
#> 3      2 Total     45   FALSE      FALSE
#> 4      3 Total     14   FALSE      FALSE
#> 5  Total     A     10   FALSE      FALSE
#> 6  Total     B     10   FALSE      FALSE
#> 7  Total     C     10   FALSE      FALSE
#> 8  Total     D     21   FALSE      FALSE
#> 9  Total     E     21   FALSE      FALSE
#> 10     1     A      1    TRUE       TRUE
#> 11     1     B      1    TRUE       TRUE
#> 12     1     C      1    TRUE       TRUE
#> 13     1     D      5   FALSE      FALSE
#> 14     1     E      5   FALSE      FALSE
#> 15     2     A      9   FALSE      FALSE
#> 16     2     B      9   FALSE      FALSE
#> 17     2     C      9   FALSE      FALSE
#> 18     2     D      9   FALSE      FALSE
#> 19     2     E      9   FALSE      FALSE
#> 20     3     A      0   FALSE       TRUE
#> 21     3     B      0   FALSE       TRUE
#> 22     3     C      0   FALSE       TRUE
#> 23     3     D      7   FALSE      FALSE
#> 24     3     E      7   FALSE      FALSE
GaussSuppressionFromData(df, c("var1", "var2"), "values", formula = ~var1 * var2, 
       singleton = NULL, protectZeros = FALSE, secondaryZeros = FALSE)
#> GaussSuppression_none: .....................
#>     var1  var2 values primary suppressed
#> 1  Total Total     72   FALSE      FALSE
#> 2      1 Total     13   FALSE      FALSE
#> 3      2 Total     45   FALSE      FALSE
#> 4      3 Total     14   FALSE      FALSE
#> 5  Total     A     10   FALSE      FALSE
#> 6  Total     B     10   FALSE      FALSE
#> 7  Total     C     10   FALSE      FALSE
#> 8  Total     D     21   FALSE      FALSE
#> 9  Total     E     21   FALSE      FALSE
#> 10     1     A      1    TRUE       TRUE
#> 11     1     B      1    TRUE       TRUE
#> 12     1     C      1    TRUE       TRUE
#> 13     1     D      5   FALSE      FALSE
#> 14     1     E      5   FALSE      FALSE
#> 15     2     A      9   FALSE       TRUE
#> 16     2     B      9   FALSE       TRUE
#> 17     2     C      9   FALSE       TRUE
#> 18     2     D      9   FALSE      FALSE
#> 19     2     E      9   FALSE      FALSE
#> 20     3     A      0   FALSE      FALSE
#> 21     3     B      0   FALSE      FALSE
#> 22     3     C      0   FALSE      FALSE
#> 23     3     D      7   FALSE      FALSE
#> 24     3     E      7   FALSE      FALSE
GaussSuppressionFromData(df, c("var1", "var2"), "values", formula = ~var1 * var2, 
       protectZeros = FALSE, secondaryZeros = FALSE)
#> GaussSuppression_anySum: .....................
#>     var1  var2 values primary suppressed
#> 1  Total Total     72   FALSE      FALSE
#> 2      1 Total     13   FALSE      FALSE
#> 3      2 Total     45   FALSE      FALSE
#> 4      3 Total     14   FALSE      FALSE
#> 5  Total     A     10   FALSE      FALSE
#> 6  Total     B     10   FALSE      FALSE
#> 7  Total     C     10   FALSE      FALSE
#> 8  Total     D     21   FALSE      FALSE
#> 9  Total     E     21   FALSE      FALSE
#> 10     1     A      1    TRUE       TRUE
#> 11     1     B      1    TRUE       TRUE
#> 12     1     C      1    TRUE       TRUE
#> 13     1     D      5   FALSE      FALSE
#> 14     1     E      5   FALSE       TRUE
#> 15     2     A      9   FALSE       TRUE
#> 16     2     B      9   FALSE       TRUE
#> 17     2     C      9   FALSE       TRUE
#> 18     2     D      9   FALSE      FALSE
#> 19     2     E      9   FALSE       TRUE
#> 20     3     A      0   FALSE      FALSE
#> 21     3     B      0   FALSE      FALSE
#> 22     3     C      0   FALSE      FALSE
#> 23     3     D      7   FALSE      FALSE
#> 24     3     E      7   FALSE      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) 
#> GaussSuppression_none: ............
#>      row   col freq primary suppressed
#> 1  Total Total   37   FALSE      FALSE
#> 2  Total     1    7   FALSE      FALSE
#> 3  Total     2   10   FALSE      FALSE
#> 4  Total     3   20   FALSE      FALSE
#> 5      1 Total    7   FALSE      FALSE
#> 6      1     1    0    TRUE       TRUE
#> 7      1     2    2    TRUE       TRUE
#> 8      1     3    5   FALSE      FALSE
#> 9      2 Total    6   FALSE      FALSE
#> 10     2     1    0    TRUE       TRUE
#> 11     2     2    0    TRUE       TRUE
#> 12     2     3    6   FALSE      FALSE
#> 13     3 Total   24   FALSE      FALSE
#> 14     3     1    7   FALSE      FALSE
#> 15     3     2    8   FALSE      FALSE
#> 16     3     3    9   FALSE      FALSE
GaussSuppressionFromData(z, 1:2, 3, singletonMethod = "none") # as above 
#> GaussSuppression_none: ............
#>      row   col freq primary suppressed
#> 1  Total Total   37   FALSE      FALSE
#> 2  Total     1    7   FALSE      FALSE
#> 3  Total     2   10   FALSE      FALSE
#> 4  Total     3   20   FALSE      FALSE
#> 5      1 Total    7   FALSE      FALSE
#> 6      1     1    0    TRUE       TRUE
#> 7      1     2    2    TRUE       TRUE
#> 8      1     3    5   FALSE      FALSE
#> 9      2 Total    6   FALSE      FALSE
#> 10     2     1    0    TRUE       TRUE
#> 11     2     2    0    TRUE       TRUE
#> 12     2     3    6   FALSE      FALSE
#> 13     3 Total   24   FALSE      FALSE
#> 14     3     1    7   FALSE      FALSE
#> 15     3     2    8   FALSE      FALSE
#> 16     3     3    9   FALSE      FALSE
GaussSuppressionFromData(z, 1:2, 3)
#> GaussSuppression_anySum: ............
#>      row   col freq primary suppressed
#> 1  Total Total   37   FALSE      FALSE
#> 2  Total     1    7   FALSE      FALSE
#> 3  Total     2   10   FALSE      FALSE
#> 4  Total     3   20   FALSE      FALSE
#> 5      1 Total    7   FALSE      FALSE
#> 6      1     1    0    TRUE       TRUE
#> 7      1     2    2    TRUE       TRUE
#> 8      1     3    5   FALSE       TRUE
#> 9      2 Total    6   FALSE      FALSE
#> 10     2     1    0    TRUE       TRUE
#> 11     2     2    0    TRUE       TRUE
#> 12     2     3    6   FALSE       TRUE
#> 13     3 Total   24   FALSE      FALSE
#> 14     3     1    7   FALSE       TRUE
#> 15     3     2    8   FALSE       TRUE
#> 16     3     3    9   FALSE      FALSE
GaussSuppressionFromData(z, 1:2, 3, protectZeros = FALSE, secondaryZeros = TRUE, singleton = NULL)
#> GaussSuppression_none: ...............
#>      row   col freq primary suppressed
#> 1  Total Total   37   FALSE      FALSE
#> 2  Total     1    7   FALSE      FALSE
#> 3  Total     2   10   FALSE      FALSE
#> 4  Total     3   20   FALSE      FALSE
#> 5      1 Total    7   FALSE      FALSE
#> 6      1     1    0   FALSE       TRUE
#> 7      1     2    2    TRUE       TRUE
#> 8      1     3    5   FALSE      FALSE
#> 9      2 Total    6   FALSE      FALSE
#> 10     2     1    0   FALSE       TRUE
#> 11     2     2    0   FALSE       TRUE
#> 12     2     3    6   FALSE      FALSE
#> 13     3 Total   24   FALSE      FALSE
#> 14     3     1    7   FALSE      FALSE
#> 15     3     2    8   FALSE      FALSE
#> 16     3     3    9   FALSE      FALSE
GaussSuppressionFromData(z, 1:2, 3, protectZeros = FALSE, secondaryZeros = TRUE)      
#> GaussSuppression_anySumNOTprimary: ...............
#>      row   col freq primary suppressed
#> 1  Total Total   37   FALSE      FALSE
#> 2  Total     1    7   FALSE      FALSE
#> 3  Total     2   10   FALSE      FALSE
#> 4  Total     3   20   FALSE      FALSE
#> 5      1 Total    7   FALSE      FALSE
#> 6      1     1    0   FALSE      FALSE
#> 7      1     2    2    TRUE       TRUE
#> 8      1     3    5   FALSE       TRUE
#> 9      2 Total    6   FALSE      FALSE
#> 10     2     1    0   FALSE      FALSE
#> 11     2     2    0   FALSE       TRUE
#> 12     2     3    6   FALSE       TRUE
#> 13     3 Total   24   FALSE      FALSE
#> 14     3     1    7   FALSE      FALSE
#> 15     3     2    8   FALSE      FALSE
#> 16     3     3    9   FALSE      FALSE