R: riskyCells

riskyCells {sdcMicro} | R Documentation

## riskyCells

### Description

Allows to compute risky (unweighted) combinations of key variables either up to a specified dimension or using identification level. This mimics the approach taken in mu-argus.

### Usage

    riskyCells(obj, useIdentificationLevel = FALSE, threshold, ...)

### Arguments

`obj` | a `data.frame`, `data.table` or an object of class `sdcMicroObj-class`
`useIdentificationLevel` | (logical) specifies if tabulation should be done up to a specific dimension (`useIdentificationLevel=FALSE` using argument `maxDim`) or taking identification levels (`useIdentificationLevel=FALSE` using argument `level`) into account.
`threshold` | a numeric vector specifiying the thresholds at which cells are considered to be unsafe. In case a tabulation is done up to a specific level (`useIdentificationLevel=FALSE`), the thresholds may be specified differently for each dimension. In the other case, the same threshold is used for all tables.
`...` | see possible arguments below keyVars: index or variable-names within `obj` that should be used for tabulation. In case `obj` is of class `sdcMicroObj-class`, this argument is not used and the pre-defined key-variables are used. level: in case `useIdentificationLevel=TRUE`, this numeric vector specifies the importance of the key variables. The construction of output tables follows the implementation in mu-argus, see e.g https://github.com/sdcTools/manuals/raw/master/mu-argus/MUmanual5.1.pdf. The length of this numeric vector must match the number of key variables. maxDim: in case `useIdentificationLevel=FALSE`, this number specifies maximal number of variables to tablulate.

### Value

a `data.table` showing the number of unsafe cells, thresholds for any combination of the key variables. If the input was a `sdcMicroObj-class` object and some modifications have been already applied to the categorical key variables, the resulting output contains the number of unsafe cells both for the original and the modified data.

### Author(s)

Bernhard Meindl

### Examples

    ## Not run:
    ## data.frame method / all combinations up to maxDim
    riskyCells(testdata2, keyVars=c(1:5), threshold=c(50,25,10,5),
      useIdentificationLevel=FALSE, maxDim=4)
    riskyCells(testdata2, keyVars=c(1:5), threshold=10,
      useIdentificationLevel=FALSE, maxDim=3)

    ## data.frame method / using identification levels
    riskyCells(testdata2, keyVars=c(1:6), threshold=20,
      useIdentificationLevel=TRUE, level=c(1,1,2,3,3,5))
    riskyCells(testdata2, keyVars=c(1,3,4,6), threshold=10,
      useIdentificationLevel=TRUE, level=c(1,2,2,4))

    ## sdcMicroObj-method / all combinations up to maxDim
    testdata2[1:6] <- lapply(1:6, function(x) {
      testdata2[[x]] <- as.factor(testdata2[[x]])
    })
    sdc <- createSdcObj(testdata2,
      keyVars=c('urbrur','roof','walls','water','electcon','relat','sex'),
      numVars=c('expend','income','savings'), w='sampling_weight')

    r0 <- riskyCells(sdc, useIdentificationLevel=FALSE, threshold=c(20,10,5), maxDim=3)
    ## in case key-variables have been modified, we get counts for original and modified data
    sdc <- groupAndRename(sdc, var="roof", before=c("5","6","9"), after=c("5+"))
    r1 <- riskyCells(sdc, useIdentificationLevel=FALSE, threshold=c(10,5,3), maxDim=3)

    ## sdcMicroObj-method / using identification levels
    riskyCells(sdc, useIdentificationLevel=TRUE, threshold=10, level=c(c(1,1,3,4,5,5,5)))

    ## End(Not run)

[Package sdcMicro version 5.6.1 Index]