CandidatesDefault {GaussSuppression}

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