Package 'SmallCountRounding' reference manual

Title:	Small Count Rounding of Tabular Data
Description:	A statistical disclosure control tool to protect frequency tables in cases where small values are sensitive. The function PLSrounding() performs small count rounding of necessary inner cells so that all small frequencies of cross-classifications to be published (publishable cells) are rounded. This is equivalent to changing micro data since frequencies of unique combinations are changed. Thus, additivity and consistency are guaranteed. The methodology is described in Langsrud and Heldal (2018) <https://www.researchgate.net/publication/327768398_An_Algorithm_for_Small_Count_Rounding_of_Tabular_Data>.
Authors:	Øyvind Langsrud [aut, cre], Johan Heldal [aut]
Maintainer:	Øyvind Langsrud <[email protected]>
License:	MIT + file LICENSE
Version:	1.0.8
Built:	2024-11-22 05:53:30 UTC
Source:	https://github.com/statisticsnorway/ssb-smallcountrounding

Small Count Rounding of Tabular Data

Description

A statistical disclosure control tool to protect frequency tables in cases where small values are sensitive. The main function, PLSrounding, performs small count rounding of necessary inner cells (Heldal, 2017) so that all small frequencies of cross-classifications to be published (publishable cells) are rounded. This is equivalent to changing micro data since frequencies of unique combinations are changed. Thus, additivity and consistency are guaranteed. This is performed by an algorithm inspired by partial least squares regression (Langsrud and Heldal, 2018).

Author(s)

Maintainer: Øyvind Langsrud [email protected]

Authors:

Johan Heldal

References

Heldal, J. (2017): “The European Census Hub 2011 Hypercubes - Norwegian SDC Experiences”. In: Work Session on Statistical Data Confidentiality, Skopje, The former Yugoslav Republic of Macedonia, September 20-22 , 2017.

Langsrud, Ø. and Heldal, J. (2018): “An Algorithm for Small Count Rounding of Tabular Data”. Presented at: Privacy in statistical databases, Valencia, Spain. September 26-28, 2018. https://www.researchgate.net/publication/327768398_An_Algorithm_for_Small_Count_Rounding_of_Tabular_Data

FormulaSelection method for PLSrounded

Description

FormulaSelection method for PLSrounded

Usage

## S3 method for class 'PLSrounded'
FormulaSelection(x, formula = NULL, intercept = NA, logical = FALSE)
## S3 method for class 'PLSrounded'
FormulaSelection(x, formula = NULL, intercept = NA, logical = FALSE)

Arguments

`x`	PLSrounded object
`formula`	`formula` parameter to `FormulaSelection`. When `NULL` (default), the publish data frame is returned without any limitation.
`intercept`	`intercept` parameter to `FormulaSelection`.
`logical`	`logical` parameter to `FormulaSelection`.

Value

Limited version of the publish data frame

Hellinger Distance (Utility)

Description

Hellinger distance (HD) and a related utility measure (HDutility) described in the reference below. The utility measure is made to be bounded between 0 and 1.

Usage

HD(f, g)

HDutility(f, g)
HD(f, g)

HDutility(f, g)

Arguments

`f`	Vector of original counts
`g`	Vector of perturbed counts

Details

HD is defined as "sqrt(sum((sqrt(f) - sqrt(g))^2)/2)" and HDutility is defined as "1 - HD(f, g)/sqrt(sum(f))".

Value

Hellinger distance or related utility measure

References

Shlomo, N., Antal, L., & Elliot, M. (2015). Measuring Disclosure Risk and Data Utility for Flexible Table Generators, Journal of Official Statistics, 31(2), 305-324. doi:10.1515/jos-2015-0019

Examples

f <- 1:6
g <- c(0, 3, 3, 3, 6, 6)
print(c(
  HD = HD(f, g), 
  HDutility = HDutility(f, g), 
  maxdiff = max(abs(g - f)), 
  meanAbsDiff = mean(abs(g - f)), 
  rootMeanSquare = sqrt(mean((g - f)^2))
))
f <- 1:6
g <- c(0, 3, 3, 3, 6, 6)
print(c(
  HD = HD(f, g), 
  HDutility = HDutility(f, g), 
  maxdiff = max(abs(g - f)), 
  meanAbsDiff = mean(abs(g - f)), 
  rootMeanSquare = sqrt(mean((g - f)^2))
))

Two-way table from PLSrounding output

Description

Output from PLSrounding is presented as two-way table(s) in cases where this is possible. A requirement is that the number of main dimensional variables is two.

Usage

PLS2way(obj, variable = c("rounded", "original", "difference", "code"))
PLS2way(obj, variable = c("rounded", "original", "difference", "code"))

Arguments

`obj`	Output object from `PLSrounding`
`variable`	One of `"rounded"` (default), `"original"`, `"difference"` or `"code"`.

Details

When parameter "variable" is "code", output is coded as "#" (publish), "." (inner) and "&" (both).

Value

A data frame

Examples

# Making tables from PLSrounding examples 
z <- SmallCountData("e6")
a <- PLSrounding(z, "freq", formula = ~eu * year + geo)
PLS2way(a, "original")
PLS2way(a, "difference")
PLS2way(a, "code")
PLS2way(PLSrounding(z, "freq", formula = ~eu * year + geo * year), "code")
eHrc2 <- list(geo = c("EU", "@Portugal", "@Spain", "Iceland"), year = c("2018", "2019"))
PLS2way(PLSrounding(z, "freq", hierarchies = eHrc2))
# Making tables from PLSrounding examples 
z <- SmallCountData("e6")
a <- PLSrounding(z, "freq", formula = ~eu * year + geo)
PLS2way(a, "original")
PLS2way(a, "difference")
PLS2way(a, "code")
PLS2way(PLSrounding(z, "freq", formula = ~eu * year + geo * year), "code")
eHrc2 <- list(geo = c("EU", "@Portugal", "@Spain", "Iceland"), year = c("2018", "2019"))
PLS2way(PLSrounding(z, "freq", hierarchies = eHrc2))

PLS inspired rounding

Description

Small count rounding of necessary inner cells are performed so that all small frequencies of cross-classifications to be published (publishable cells) are rounded. The publishable cells can be defined from a model formula, hierarchies or automatically from data.

Usage

PLSrounding(
  data,
  freqVar = NULL,
  roundBase = 3,
  hierarchies = NULL,
  formula = NULL,
  dimVar = NULL,
  maxRound = roundBase - 1,
  printInc = nrow(data) > 1000,
  output = NULL,
  extend0 = FALSE,
  preAggregate = is.null(freqVar),
  aggregatePackage = "base",
  aggregateNA = TRUE,
  aggregateBaseOrder = FALSE,
  rowGroupsPackage = aggregatePackage,
  ...
)

PLSroundingInner(..., output = "inner")

PLSroundingPublish(..., output = "publish")
PLSrounding(
  data,
  freqVar = NULL,
  roundBase = 3,
  hierarchies = NULL,
  formula = NULL,
  dimVar = NULL,
  maxRound = roundBase - 1,
  printInc = nrow(data) > 1000,
  output = NULL,
  extend0 = FALSE,
  preAggregate = is.null(freqVar),
  aggregatePackage = "base",
  aggregateNA = TRUE,
  aggregateBaseOrder = FALSE,
  rowGroupsPackage = aggregatePackage,
  ...
)

PLSroundingInner(..., output = "inner")

PLSroundingPublish(..., output = "publish")

Arguments

`data`	Input data as a data frame (inner cells)
`freqVar`	Variable holding counts (inner cells frequencies). When `NULL` (default), microdata is assumed.
`roundBase`	Rounding base
`hierarchies`	List of hierarchies
`formula`	Model formula defining publishable cells
`dimVar`	The main dimensional variables and additional aggregating variables. This parameter can be useful when hierarchies and formula are unspecified.
`maxRound`	Inner cells contributing to original publishable cells equal to or less than maxRound will be rounded
`printInc`	Printing iteration information to console when TRUE
`output`	Possible non-NULL values are `"input"`, `"inner"` and `"publish"`. Then a single data frame is returned.
`extend0`	When `extend0` is set to `TRUE`, the data is automatically extended. This is relevant when `zeroCandidates = TRUE` (see `RoundViaDummy`). Additionally, `extend0` can be specified as a list, representing the `varGroups` parameter in the `Extend0` function. Can also be set to `"all"` which means that input codes in hierarchies are considered in addition to those in data.
`preAggregate`	When `TRUE`, the data will be aggregated beforehand within the function by the dimensional variables.
`aggregatePackage`	Package used to preAggregate. Parameter `pkg` to `aggregate_by_pkg`.
`aggregateNA`	Whether to include NAs in the grouping variables while preAggregate. Parameter `include_na` to `aggregate_by_pkg`.
`aggregateBaseOrder`	Parameter `base_order` to `aggregate_by_pkg`, used when preAggregate. The default is set to `FALSE` to avoid unnecessary sorting operations. When `TRUE`, an attempt is made to return the same result with `data.table` as with base R. This cannot be guaranteed due to potential variations in sorting behavior across different systems.
`rowGroupsPackage`	Parameter `pkg` to `RowGroups`. The parameter is input to `Formula2ModelMatrix` via `ModelMatrix`.
`...`	Further parameters sent to `RoundViaDummy`

Details

This function is a user-friendly wrapper for RoundViaDummy with data frame output and with computed summary of the results. See RoundViaDummy for more details.

Value

Output is a four-element list with class attribute "PLSrounded", which ensures informative printing and enables the use of FormulaSelection on this object.

`inner`	Data frame corresponding to input data with the main dimensional variables and with cell frequencies (original, rounded, difference).
`publish`	Data frame of publishable data with the main dimensional variables and with cell frequencies (original, rounded, difference).
`metrics`	A named character vector of various statistics calculated from the two output data frames ("`inner_`" used to distinguish). See examples below and the function `HDutility`.
`freqTable`	Matrix of frequencies of cell frequencies and absolute differences. For example, row "`rounded`" and column "`inn.4+`" is the number of rounded inner cell frequencies greater than or equal to `4`.

References

Examples

# Small example data set
z <- SmallCountData("e6")
print(z)

# Publishable cells by formula interface
a <- PLSrounding(z, "freq", roundBase = 5,  formula = ~geo + eu + year)
print(a)
print(a$inner)
print(a$publish)
print(a$metrics)
print(a$freqTable)

# Using FormulaSelection()
FormulaSelection(a$publish, ~eu + year)
FormulaSelection(a, ~eu + year) # same as above
FormulaSelection(a)             # just a$publish

# Recalculation of maxdiff, HDutility, meanAbsDiff and rootMeanSquare
max(abs(a$publish[, "difference"]))
HDutility(a$publish[, "original"], a$publish[, "rounded"])
mean(abs(a$publish[, "difference"]))
sqrt(mean((a$publish[, "difference"])^2))

# Six lines below produce equivalent results 
# Ordering of rows can be different
PLSrounding(z, "freq") # All variables except "freq" as dimVar  
PLSrounding(z, "freq", dimVar = c("geo", "eu", "year"))
PLSrounding(z, "freq", formula = ~eu * year + geo * year)
PLSrounding(z[, -2], "freq", hierarchies = SmallCountData("eHrc"))
PLSrounding(z[, -2], "freq", hierarchies = SmallCountData("eDimList"))
PLSrounding(z[, -2], "freq", hierarchies = SmallCountData("eDimList"), formula = ~geo * year)

# Define publishable cells differently by making use of formula interface
PLSrounding(z, "freq", formula = ~eu * year + geo)

# Define publishable cells differently by making use of hierarchy interface
eHrc2 <- list(geo = c("EU", "@Portugal", "@Spain", "Iceland"), year = c("2018", "2019"))
PLSrounding(z, "freq", hierarchies = eHrc2)

# Also possible to combine hierarchies and formula
PLSrounding(z, "freq", hierarchies = SmallCountData("eDimList"), formula = ~geo + year)

# Single data frame output
PLSroundingInner(z, "freq", roundBase = 5, formula = ~geo + eu + year)
PLSroundingPublish(z, roundBase = 5, formula = ~geo + eu + year)

# Microdata input
PLSroundingInner(rbind(z, z), roundBase = 5, formula = ~geo + eu + year)

# Zero perturbed due to both  extend0 = TRUE and zeroCandidates = TRUE 
set.seed(12345)
PLSroundingInner(z[sample.int(5, 12, replace = TRUE), 1:3], 
                 formula = ~geo + eu + year, roundBase = 5, 
                 extend0 = TRUE, zeroCandidates = TRUE, printInc = TRUE)

# Parameter avoidHierarchical (see RoundViaDummy and ModelMatrix) 
PLSroundingPublish(z, roundBase = 5, formula = ~geo + eu + year, avoidHierarchical = TRUE)

# Package sdcHierarchies can be used to create hierarchies. 
# The small example code below works if this package is available. 
if (require(sdcHierarchies)) {
  z2 <- cbind(geo = c("11", "21", "22"), z[, 3:4], stringsAsFactors = FALSE)
  h2 <- list(
    geo = hier_compute(inp = unique(z2$geo), dim_spec = c(1, 1), root = "Tot", as = "df"),
    year = hier_convert(hier_create(root = "Total", nodes = c("2018", "2019")), as = "df"))
  PLSrounding(z2, "freq", hierarchies = h2)
}

# Use PLS2way to produce tables as in Langsrud and Heldal (2018) and to demonstrate 
# parameters maxRound, zeroCandidates and identifyNew (see RoundViaDummy).   
# Parameter rndSeed used to ensure same output as in reference.
exPSD <- SmallCountData("exPSD")
a <- PLSrounding(exPSD, "freq", 5, formula = ~rows + cols, rndSeed=124)
PLS2way(a, "original")  # Table 1
PLS2way(a)  # Table 2
a <- PLSrounding(exPSD, "freq", 5, formula = ~rows + cols, identifyNew = FALSE, rndSeed=124)
PLS2way(a)  # Table 3
a <- PLSrounding(exPSD, "freq", 5, formula = ~rows + cols, maxRound = 7)
PLS2way(a)  # Values in col1 rounded
a <- PLSrounding(exPSD, "freq", 5, formula = ~rows + cols, zeroCandidates = TRUE)
PLS2way(a)  # (row3, col4): original is 0 and rounded is 5
# Small example data set
z <- SmallCountData("e6")
print(z)

# Publishable cells by formula interface
a <- PLSrounding(z, "freq", roundBase = 5,  formula = ~geo + eu + year)
print(a)
print(a$inner)
print(a$publish)
print(a$metrics)
print(a$freqTable)

# Using FormulaSelection()
FormulaSelection(a$publish, ~eu + year)
FormulaSelection(a, ~eu + year) # same as above
FormulaSelection(a)             # just a$publish

# Recalculation of maxdiff, HDutility, meanAbsDiff and rootMeanSquare
max(abs(a$publish[, "difference"]))
HDutility(a$publish[, "original"], a$publish[, "rounded"])
mean(abs(a$publish[, "difference"]))
sqrt(mean((a$publish[, "difference"])^2))

# Six lines below produce equivalent results 
# Ordering of rows can be different
PLSrounding(z, "freq") # All variables except "freq" as dimVar  
PLSrounding(z, "freq", dimVar = c("geo", "eu", "year"))
PLSrounding(z, "freq", formula = ~eu * year + geo * year)
PLSrounding(z[, -2], "freq", hierarchies = SmallCountData("eHrc"))
PLSrounding(z[, -2], "freq", hierarchies = SmallCountData("eDimList"))
PLSrounding(z[, -2], "freq", hierarchies = SmallCountData("eDimList"), formula = ~geo * year)

# Define publishable cells differently by making use of formula interface
PLSrounding(z, "freq", formula = ~eu * year + geo)

# Define publishable cells differently by making use of hierarchy interface
eHrc2 <- list(geo = c("EU", "@Portugal", "@Spain", "Iceland"), year = c("2018", "2019"))
PLSrounding(z, "freq", hierarchies = eHrc2)

# Also possible to combine hierarchies and formula
PLSrounding(z, "freq", hierarchies = SmallCountData("eDimList"), formula = ~geo + year)

# Single data frame output
PLSroundingInner(z, "freq", roundBase = 5, formula = ~geo + eu + year)
PLSroundingPublish(z, roundBase = 5, formula = ~geo + eu + year)

# Microdata input
PLSroundingInner(rbind(z, z), roundBase = 5, formula = ~geo + eu + year)

# Zero perturbed due to both  extend0 = TRUE and zeroCandidates = TRUE 
set.seed(12345)
PLSroundingInner(z[sample.int(5, 12, replace = TRUE), 1:3], 
                 formula = ~geo + eu + year, roundBase = 5, 
                 extend0 = TRUE, zeroCandidates = TRUE, printInc = TRUE)

# Parameter avoidHierarchical (see RoundViaDummy and ModelMatrix) 
PLSroundingPublish(z, roundBase = 5, formula = ~geo + eu + year, avoidHierarchical = TRUE)

# Package sdcHierarchies can be used to create hierarchies. 
# The small example code below works if this package is available. 
if (require(sdcHierarchies)) {
  z2 <- cbind(geo = c("11", "21", "22"), z[, 3:4], stringsAsFactors = FALSE)
  h2 <- list(
    geo = hier_compute(inp = unique(z2$geo), dim_spec = c(1, 1), root = "Tot", as = "df"),
    year = hier_convert(hier_create(root = "Total", nodes = c("2018", "2019")), as = "df"))
  PLSrounding(z2, "freq", hierarchies = h2)
}

# Use PLS2way to produce tables as in Langsrud and Heldal (2018) and to demonstrate 
# parameters maxRound, zeroCandidates and identifyNew (see RoundViaDummy).   
# Parameter rndSeed used to ensure same output as in reference.
exPSD <- SmallCountData("exPSD")
a <- PLSrounding(exPSD, "freq", 5, formula = ~rows + cols, rndSeed=124)
PLS2way(a, "original")  # Table 1
PLS2way(a)  # Table 2
a <- PLSrounding(exPSD, "freq", 5, formula = ~rows + cols, identifyNew = FALSE, rndSeed=124)
PLS2way(a)  # Table 3
a <- PLSrounding(exPSD, "freq", 5, formula = ~rows + cols, maxRound = 7)
PLS2way(a)  # Values in col1 rounded
a <- PLSrounding(exPSD, "freq", 5, formula = ~rows + cols, zeroCandidates = TRUE)
PLS2way(a)  # (row3, col4): original is 0 and rounded is 5

Small count rounding with post-processing to expected frequencies

Description

The counts rounded by PLSrounding Thereafter, based on the publishable rounded data, expected inner cell frequencies are generated by iterative proportional fitting using Mipf. To ensure that empty cells missing in input data are included in the fitting process, the data is first extended using Extend0.

Usage

PLSroundingFits(
  data,
  freqVar = NULL,
  roundBase = 3,
  hierarchies = NULL,
  formula = NULL,
  dimVar = NULL,
  preAggregate = is.null(freqVar),
  printInc = nrow(data) > 1000,
  xReturn = FALSE,
  extend0 = FALSE,
  extend0Fits = TRUE,
  limit = 1e-10,
  viaQR = FALSE,
  iter = 1000,
  eps = 0.01,
  tol = 1e-13,
  reduceBy0 = TRUE,
  reduceByColSums = TRUE,
  reduceByLeverage = FALSE,
  ...
)
PLSroundingFits(
  data,
  freqVar = NULL,
  roundBase = 3,
  hierarchies = NULL,
  formula = NULL,
  dimVar = NULL,
  preAggregate = is.null(freqVar),
  printInc = nrow(data) > 1000,
  xReturn = FALSE,
  extend0 = FALSE,
  extend0Fits = TRUE,
  limit = 1e-10,
  viaQR = FALSE,
  iter = 1000,
  eps = 0.01,
  tol = 1e-13,
  reduceBy0 = TRUE,
  reduceByColSums = TRUE,
  reduceByLeverage = FALSE,
  ...
)

Arguments

`data`	data frame (inner cells)
`freqVar`	Variable holding counts
`roundBase`	Rounding base
`hierarchies`	List of hierarchies
`formula`	Model formula
`dimVar`	Dimensional variables
`preAggregate`	Aggregation
`printInc`	Printing iteration information
`xReturn`	Dummy matrix in output when `TRUE`. To return crossTable as well, use `xReturn = 2`.
`extend0`	`PLSrounding` parameter. See below.
`extend0Fits`	When `extend0Fits` is set to `TRUE` (default), the data is automatically extended. Additionally, `extend0Fits` can be specified as a list, or set to `"all"` (see `PLSrounding`). Previously, this functionality was controlled by a parameter called `extend0`, but now `extend0` is specific to the `PLSrounding` function. When both `extend0` and `extend0Fits` are used simultaneously, `extend0Fits` adds an additional extension on top of the one provided by `extend0` (see example).
`limit`	`LSfitNonNeg` parameter
`viaQR`	`LSfitNonNeg` parameter
`iter`	`Mipf` parameter
`eps`	`Mipf` parameter
`tol`	`Mipf` parameter
`reduceBy0`	`Mipf` parameter
`reduceByColSums`	`Mipf` parameter
`reduceByLeverage`	`Mipf` parameter
`...`	Further parameters to `PLSrounding`.

Details

The nine first parameters is documented in more detail in PLSrounding. If iterative proportional fitting succeeds, the maximum difference between rounded counts and ipFit is less than input parameter eps.

Value

Output from PLSrounding (class attribute "PLSrounded") with modified versions of inner and publish:

`inner`	Extended with more input data variables and with expected frequencies (`ipFit`).
`publish`	Extended with aggregated expected frequencies (`ipFit`).

Examples

z <- data.frame(geo  = c("Iceland", "Portugal", "Spain"), 
                eu = c("nonEU", "EU", "EU"),
                year = rep(c("2018","2019"), each = 3),
                freq = c(2,3,7,1,5,6), stringsAsFactors = FALSE)
z4 <- z[-c(1:2), ]

PLSroundingFits(z4, "freq", formula = ~eu * year + geo, extend0 = FALSE)[c("inner", "publish")]
PLSroundingFits(z4, "freq", formula = ~eu * year + geo)[c("inner", "publish")]

my_km2 <- SSBtools::SSBtoolsData("my_km2")

# Default automatic extension (extend0Fits = TRUE)
PLSroundingFits(my_km2, "freq", 
       formula = ~(Sex + Age) * Municipality * Square1000m + Square250m)[c("inner", "publish")]

# Manual specification to avoid Nittedal combined with another_km
PLSroundingFits(my_km2, "freq", formula = ~(Sex + Age) * Municipality * Square1000m + Square250m, 
       extend0Fits = list(c("Sex", "Age"), 
       c("Municipality", "Square1000m", "Square250m")))[c("inner", "publish")]
       
# Example with both extend0 (specified) and extend0Fits (default is TRUE)
PLSroundingFits(my_km2, "freq", formula = ~(Sex + Age) * Municipality * Square1000m + Square250m, 
       printInc = TRUE, zeroCandidates = TRUE, roundBase = 5, extend0 = list(c("Sex", "Age"), 
       c("Municipality", "Square1000m", "Square250m")))[c("inner", "publish")]        
z <- data.frame(geo  = c("Iceland", "Portugal", "Spain"), 
                eu = c("nonEU", "EU", "EU"),
                year = rep(c("2018","2019"), each = 3),
                freq = c(2,3,7,1,5,6), stringsAsFactors = FALSE)
z4 <- z[-c(1:2), ]

PLSroundingFits(z4, "freq", formula = ~eu * year + geo, extend0 = FALSE)[c("inner", "publish")]
PLSroundingFits(z4, "freq", formula = ~eu * year + geo)[c("inner", "publish")]

my_km2 <- SSBtools::SSBtoolsData("my_km2")

# Default automatic extension (extend0Fits = TRUE)
PLSroundingFits(my_km2, "freq", 
       formula = ~(Sex + Age) * Municipality * Square1000m + Square250m)[c("inner", "publish")]

# Manual specification to avoid Nittedal combined with another_km
PLSroundingFits(my_km2, "freq", formula = ~(Sex + Age) * Municipality * Square1000m + Square250m, 
       extend0Fits = list(c("Sex", "Age"), 
       c("Municipality", "Square1000m", "Square250m")))[c("inner", "publish")]
       
# Example with both extend0 (specified) and extend0Fits (default is TRUE)
PLSroundingFits(my_km2, "freq", formula = ~(Sex + Age) * Municipality * Square1000m + Square250m, 
       printInc = TRUE, zeroCandidates = TRUE, roundBase = 5, extend0 = list(c("Sex", "Age"), 
       c("Municipality", "Square1000m", "Square250m")))[c("inner", "publish")]

PLSrounding on portions of data at a time

Description

The PLSrounding runs are coordinated by using preliminary differences as input for the next run (parameter preDifference)

Usage

PLSroundingLoop(
  data,
  loopId,
  ...,
  zeroCandidates = FALSE,
  forceInner = FALSE,
  preRounded = NULL,
  plsWeights = NULL,
  printInc = TRUE,
  preDifference = TRUE,
  preOutput = NULL,
  rndSeed = 123
)
PLSroundingLoop(
  data,
  loopId,
  ...,
  zeroCandidates = FALSE,
  forceInner = FALSE,
  preRounded = NULL,
  plsWeights = NULL,
  printInc = TRUE,
  preDifference = TRUE,
  preOutput = NULL,
  rndSeed = 123
)

Arguments

`data`	Input data as a data frame (inner cells)
`loopId`	Variable holding id for loops
`...`	`PLSrounding` parameters
`zeroCandidates`	`PLSrounding` parameter (see details)
`forceInner`	`PLSrounding` parameter (see details)
`preRounded`	`PLSrounding` parameter (see details)
`plsWeights`	`PLSrounding` parameter (see details)
`printInc`	Printing iteration information to console when TRUE
`preDifference`	When TRUE, the `preDifference` parameter to `PLSrounding` is used. Each time with the differences obtained so far.
`preOutput`	preOutput The function can continue from output from a previous run
`rndSeed`	If non-NULL, a random generator seed to be set locally at the beginning of `PLSroundingLoop` without affecting the random value stream in R. Within `PLSroundingLoop`, `PLSrounding` is called with `rndSeed = NULL`.

Details

Note that in this function zeroCandidates, forceInner, preRounded and plsWeights cannot be supplied as vectors. They may be specified as functions or as variables in the input data.

Value

As output from PLSrounding

Examples

mf2 <- ~region + fylke * hovedint
z2 <- SmallCountData("z2")
a <- PLSroundingLoop(z2, loopId = "kostragr", freqVar = "ant", formula = mf2)
a
mf2 <- ~region + fylke * hovedint
z2 <- SmallCountData("z2")
a <- PLSroundingLoop(z2, loopId = "kostragr", freqVar = "ant", formula = mf2)
a

Print method for PLSrounded

Description

Print method for PLSrounded

Usage

## S3 method for class 'PLSrounded'
print(x, digits = max(getOption("digits") - 3, 3), ...)
## S3 method for class 'PLSrounded'
print(x, digits = max(getOption("digits") - 3, 3), ...)

Arguments

`x`	PLSrounded object
`digits`	positive integer. Minimum number of significant digits to be used for printing most numbers.
`...`	further arguments sent to the underlying

Value

Invisibly returns the original object.

Small Count Rounding of Tabular Data

Description

Small count rounding via a dummy matrix and by an algorithm inspired by PLS

Usage

RoundViaDummy(
  data,
  freqVar,
  formula = NULL,
  roundBase = 3,
  singleRandom = FALSE,
  crossTable = TRUE,
  total = "Total",
  maxIterRows = 1000,
  maxIter = 1e+07,
  x = NULL,
  hierarchies = NULL,
  xReturn = FALSE,
  maxRound = roundBase - 1,
  zeroCandidates = FALSE,
  forceInner = FALSE,
  identifyNew = TRUE,
  step = 0,
  preRounded = NULL,
  leverageCheck = FALSE,
  easyCheck = TRUE,
  printInc = TRUE,
  rndSeed = 123,
  dimVar = NULL,
  plsWeights = NULL,
  preDifference = NULL,
  allSmall = FALSE,
  ...
)
RoundViaDummy(
  data,
  freqVar,
  formula = NULL,
  roundBase = 3,
  singleRandom = FALSE,
  crossTable = TRUE,
  total = "Total",
  maxIterRows = 1000,
  maxIter = 1e+07,
  x = NULL,
  hierarchies = NULL,
  xReturn = FALSE,
  maxRound = roundBase - 1,
  zeroCandidates = FALSE,
  forceInner = FALSE,
  identifyNew = TRUE,
  step = 0,
  preRounded = NULL,
  leverageCheck = FALSE,
  easyCheck = TRUE,
  printInc = TRUE,
  rndSeed = 123,
  dimVar = NULL,
  plsWeights = NULL,
  preDifference = NULL,
  allSmall = FALSE,
  ...
)

Arguments

`data`	Input data as a data frame (inner cells)
`freqVar`	Variable holding counts (name or number)
`formula`	Model formula defining publishable cells. Will be used to calculate `x` (via `ModelMatrix`). When NULL, x must be supplied.
`roundBase`	Rounding base
`singleRandom`	Single random draw when TRUE (instead of algorithm)
`crossTable`	When TRUE, cross table in output and caculations via FormulaSums()
`total`	String used to name totals
`maxIterRows`	See details
`maxIter`	Maximum number of iterations
`x`	Dummy matrix defining publishable cells
`hierarchies`	List of hierarchies, which can be converted by `AutoHierarchies`. Thus, a single string as hierarchy input is assumed to be a total code. Exceptions are `"rowFactor"` or `""`, which correspond to only using the categories in the data.
`xReturn`	Dummy matrix in output when TRUE (as input parameter `x`)
`maxRound`	Inner cells contributing to original publishable cells equal to or less than maxRound will be rounded.
`zeroCandidates`	When TRUE, inner cells in input with zero count (and multiple of roundBase when maxRound is in use) contributing to publishable cells will be included as candidates to obtain roundBase value. With vector input, the rule is specified individually for each cell. This can be specified as a vector, a variable in data or a function generating it (see details).
`forceInner`	When TRUE, all inner cells will be rounded. Use vector input to force individual cells to be rounded. This can be specified as a vector, a variable in data or a function generating it (see details). Can be combined with parameter zeroCandidates to allow zeros and roundBase multiples to be rounded up.
`identifyNew`	When `TRUE`, new cells may be identified after initial rounding to ensure all rounded publishable cells equal to or less than `maxRound` to be `roundBase` multiples. Use `NA` for the a less conservative behavior (old behavior). Then it is ensured that no nonzero rounded publishable cells are smaller than `roundBase`. When `maxRound` is default, there is no difference between `TRUE` and `NA`.
`step`	When `step>1`, the original forward part of the algorithm is replaced by a kind of stepwise. After `step` steps forward, backward steps may be performed. The `step` parameter is also used for backward-forward iteration at the end of the algorithm; `step` backward steps may be performed. For greater control, the `step` parameter can be specified as a vector. Additionally, it can be provided as a list to trigger a final re-run iteration. See details.
`preRounded`	A vector or a variable in data that contains a mixture of missing values and predetermined values of rounded inner cells. Can also be specified as a function generating it (see details).
`leverageCheck`	When TRUE, all inner cells that depends linearly on the published cells and with small frequencies (`<=maxRound`) will be rounded. The computation of leverages can be very time and memory consuming. The function `Reduce0exact` is called. The default leverage limit is `0.999999`. Another limit can be sent as input instead of `TRUE`. Checking is performed before and after (since new zeros) rounding. Extra iterations are performed when needed.
`easyCheck`	A light version of the above leverage checking. Checking is performed after rounding. Extra iterations are performed when needed. `Reduce0exact` is called with `reduceByLeverage=FALSE` and `reduceByColSums=TRUE`.
`printInc`	Printing iteration information to console when TRUE
`rndSeed`	If non-NULL, a random generator seed to be used locally within the function without affecting the random value stream in R.
`dimVar`	The main dimensional variables and additional aggregating variables. This parameter can be useful when hierarchies and formula are unspecified.
`plsWeights`	A vector of weights for each cell to be published or a function generating it (see details). For use in the algorithm criterion.
`preDifference`	A data.frame with differences already obtained from rounding another subset of data. There must be columns that match `crossTable`. Differences must be in the last column.
`allSmall`	When TRUE, all small inner cells (`⁠<= maxRound⁠`) are rounded. This parameter is a simplified alternative to specifying `forceInner` (see details).
`...`	Further parameters sent to `ModelMatrix`. In particular, one can specify `removeEmpty=TRUE` to omit empty combinations. The parameter `inputInOutput` can be used to specify whether to include codes from input. The parameter `avoidHierarchical` (`Formula2ModelMatrix`) can be combined with formula input.

Details

Small count rounding of necessary inner cells are performed so that all small frequencies of cross-classifications to be published (publishable cells) are rounded. This is equivalent to changing micro data since frequencies of unique combinations are changed. Thus, additivity and consistency are guaranteed. The matrix multiplication formula is: yPublish = t(x) %*% yInner, where x is the dummy matrix.

Parameters zeroCandidates, forceInner, preRounded and plsWeights can be specified as functions. The supplied functions take the following arguments: data, yPublish, yInner, crossTable, x, roundBase, maxRound, and ..., where the first two are numeric vectors of original counts. When allSmall is TRUE, forceInner is set to ⁠function(yInner, maxRound, ...)⁠ yInner <= maxRound.

Details about the step parameter:

step as a numeric vector is converted to three parameters by
- step1 <- step[1]
- step2 <- ifelse(length(step)>=2, step[2], round(step/2))
- step3 <- ifelse(length(step)>=3, step[3], step[1])
After step1 steps forward, up to step2 backward steps may be performed. At the end of the algorithm; up to step3 backward steps may be executed repeatedly.
step when provided as a list (of numeric vectors), is adjusted to a length of 3 using rep_len(step, 3).
- step[[1]] is used in the main iterations.
- step[[2]], when non-NULL, is used in a final re-run iteration.
- step[[3]] is used in extra iterations caused by easyCheck or leverageCheck.
Setting step = list(0) will result in standard behavior, with the exception that an extra re-run iteration is performed. The most detailed setting is achieved by setting step to a length-3 list where each element has length 3.

Value

A list where the two first elements are two column matrices. The first matrix consists of inner cells and the second of cells to be published. In each matrix the first and the second column contains, respectively, original and rounded values. By default the cross table is the third element of the output list.

Note

Iterations are needed since after initial rounding of identified cells, new cells are identified. If cases of a high number of identified cells the algorithm can be too memory consuming (unless singleRandom=TRUE). To avoid problems, not more than maxIterRows cells are rounded in each iteration. The iteration limit (maxIter) is by default set to be high since a low number of maxIterRows may need a high number of iterations.

Examples

# See similar and related examples in PLSrounding documentation
RoundViaDummy(SmallCountData("e6"), "freq")
RoundViaDummy(SmallCountData("e6"), "freq", formula = ~eu * year + geo)
RoundViaDummy(SmallCountData("e6"), "freq", hierarchies = 
   list(geo = c("EU", "@Portugal", "@Spain", "Iceland"), year = c("2018", "2019")))

RoundViaDummy(SmallCountData('z2'), 
              'ant', ~region + hovedint + fylke*hovedint + kostragr*hovedint, 10)
mf <- ~region*mnd + hovedint*mnd + fylke*hovedint*mnd + kostragr*hovedint*mnd
a <- RoundViaDummy(SmallCountData('z3'), 'ant', mf, 5)
b <- RoundViaDummy(SmallCountData('sosialFiktiv'), 'ant', mf, 4)
print(cor(b[[2]]),digits=12) # Correlation between original and rounded

# Demonstrate parameter leverageCheck 
# The 42nd inner cell must be rounded since it can be revealed from the published cells.
mf2 <- ~region + hovedint + fylke * hovedint + kostragr * hovedint
RoundViaDummy(SmallCountData("z2"), "ant", mf2, leverageCheck = FALSE)$yInner[42, ]
RoundViaDummy(SmallCountData("z2"), "ant", mf2, leverageCheck = TRUE)$yInner[42, ]

## Not run: 
# Demonstrate parameters maxRound, zeroCandidates and forceInner 
# by tabulating the inner cells that have been changed.
z4 <- SmallCountData("sosialFiktiv")
for (forceInner in c("FALSE", "z4$ant < 10")) 
  for (zeroCandidates in c(FALSE, TRUE)) 
    for (maxRound in c(2, 5)) {
      set.seed(123)
      a <- RoundViaDummy(z4, "ant", formula = mf, maxRound = maxRound, 
                         zeroCandidates = zeroCandidates, 
                         forceInner = eval(parse(text = forceInner)))
      change <- a$yInner[, "original"] != a$yInner[, "rounded"]
      cat("\n\n---------------------------------------------------\n")
      cat("      maxRound:", maxRound, "\n")
      cat("zeroCandidates:", zeroCandidates, "\n")
      cat("    forceInner:", forceInner, "\n\n")
      print(table(original = a$yInner[change, "original"], rounded = a$yInner[change, "rounded"]))
      cat("---------------------------------------------------\n")
    }

## End(Not run)
# See similar and related examples in PLSrounding documentation
RoundViaDummy(SmallCountData("e6"), "freq")
RoundViaDummy(SmallCountData("e6"), "freq", formula = ~eu * year + geo)
RoundViaDummy(SmallCountData("e6"), "freq", hierarchies = 
   list(geo = c("EU", "@Portugal", "@Spain", "Iceland"), year = c("2018", "2019")))

RoundViaDummy(SmallCountData('z2'), 
              'ant', ~region + hovedint + fylke*hovedint + kostragr*hovedint, 10)
mf <- ~region*mnd + hovedint*mnd + fylke*hovedint*mnd + kostragr*hovedint*mnd
a <- RoundViaDummy(SmallCountData('z3'), 'ant', mf, 5)
b <- RoundViaDummy(SmallCountData('sosialFiktiv'), 'ant', mf, 4)
print(cor(b[[2]]),digits=12) # Correlation between original and rounded

# Demonstrate parameter leverageCheck 
# The 42nd inner cell must be rounded since it can be revealed from the published cells.
mf2 <- ~region + hovedint + fylke * hovedint + kostragr * hovedint
RoundViaDummy(SmallCountData("z2"), "ant", mf2, leverageCheck = FALSE)$yInner[42, ]
RoundViaDummy(SmallCountData("z2"), "ant", mf2, leverageCheck = TRUE)$yInner[42, ]

## Not run: 
# Demonstrate parameters maxRound, zeroCandidates and forceInner 
# by tabulating the inner cells that have been changed.
z4 <- SmallCountData("sosialFiktiv")
for (forceInner in c("FALSE", "z4$ant < 10")) 
  for (zeroCandidates in c(FALSE, TRUE)) 
    for (maxRound in c(2, 5)) {
      set.seed(123)
      a <- RoundViaDummy(z4, "ant", formula = mf, maxRound = maxRound, 
                         zeroCandidates = zeroCandidates, 
                         forceInner = eval(parse(text = forceInner)))
      change <- a$yInner[, "original"] != a$yInner[, "rounded"]
      cat("\n\n---------------------------------------------------\n")
      cat("      maxRound:", maxRound, "\n")
      cat("zeroCandidates:", zeroCandidates, "\n")
      cat("    forceInner:", forceInner, "\n\n")
      print(table(original = a$yInner[change, "original"], rounded = a$yInner[change, "rounded"]))
      cat("---------------------------------------------------\n")
    }

## End(Not run)

Function that returns a dataset

Description

Function that returns a dataset

Usage

SmallCountData(dataset, path = NULL)
SmallCountData(dataset, path = NULL)

Arguments

`dataset`	Name of data set within the SmallCountRounding package
`path`	When non-NULL the data set is read from "path/dataset.RData"

Value

The dataset

Note

Except for "europe6", "eHrc", "eDimList" and "exPSD", the function returns the same datasets as SSBtoolsData.

Examples

 SmallCountData("z1")
 SmallCountData("e6")
 SmallCountData("eHrc")      #  TauArgus coded hierarchies
 SmallCountData("eDimList")  #  sdcTable coded hierarchies
 SmallCountData("exPSD")     #  Example data in presentation at Privacy in statistical databases

SmallCountData("z1")
 SmallCountData("e6")
 SmallCountData("eHrc")      #  TauArgus coded hierarchies
 SmallCountData("eDimList")  #  sdcTable coded hierarchies
 SmallCountData("exPSD")     #  Example data in presentation at Privacy in statistical databases

Package 'SmallCountRounding'

Help Index

Small Count Rounding of Tabular Data

Description

Author(s)

References

See Also

FormulaSelection method for PLSrounded

Description

Usage

Arguments

Value

Hellinger Distance (Utility)

Description

Usage

Arguments

Details

Value

References

Examples

Two-way table from PLSrounding output

Description

Usage

Arguments

Details

Value

Examples

PLS inspired rounding

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Small count rounding with post-processing to expected frequencies

Description

Usage

Arguments

Details

Value

Examples

PLSrounding on portions of data at a time

Description

Usage

Arguments

Details

Value

Examples

Print method for PLSrounded

Description

Usage

Arguments

Value

Small Count Rounding of Tabular Data

Description

Usage

Arguments

Details

Value

Note

See Also

Examples

Function that returns a dataset

Description

Usage

Arguments

Value

Note

See Also

Examples