R: Comparison of different microaggregation methods

valTable {sdcMicro} | R Documentation

## Comparison of different microaggregation methods

### Description

A Function for the comparison of different perturbation methods.

### Usage

    valTable(
      x,
      method = c("simple", "onedims", "clustpppca", "addNoise: additive", "swappNum"),
      measure = "mean",
      clustermethod = "clara",
      aggr = 3,
      nc = 8,
      transf = "log",
      p = 15,
      noise = 15,
      w = 1:dim(x)[2],
      delta = 0.1
    )

### Arguments

`x` | a `data.frame` or a `matrix`
`method` | character vector defining names of microaggregation-, adding-noise or rank swapping methods.
`measure` | FUN for aggregation. Possible values are mean (default), median, trim, onestep.
`clustermethod` | clustermethod, if a method will need a clustering procedure
`aggr` | aggregation level (default=3)
`nc` | number of clusters. Necessary, if a method will need a clustering procedure
`transf` | Transformation of variables before clustering.
`p` | Swapping range, if method swappNum has been chosen
`noise` | noise addition, if an addNoise method has been chosen
`w` | variables for swapping, if method swappNum has been chosen
`delta` | parameter for adding noise method `"correlated2"`

### Details

Tabularize the output from `summary.micro()`. Will be enhanced to all perturbation methods in future versions.

Methods for adding noise should be named via `addNoise:{method}`, e.g. `addNoise:correlated`, where `{method}` specifies the desired method as described in `addNoise()`.

### Value

Measures of information loss splitted for the comparison of different methods.

### Author(s)

Matthias Templ

### References

Templ, M. and Meindl, B., `Software Development for SDC in R`, Lecture Notes in Computer Science, Privacy in Statistical Databases, vol. 4302, pp. 347-359, 2006.

### See Also

`microaggregation()`, `summary.micro()`

### Examples

    data(Tarragona)
    ## Not run:
    valTable(
      x = Tarragona[100:200, ],
      method=c("simple", "onedims", "pca"))

    valTable(
      x = Tarragona,
      method = c("simple", "onedims", "pca", "clustpppca", "mdav", "swappNum"))

    ## clustpppca in combination with Mclust outperforms
    ## the other algorithms for this data set...

    ## End(Not run)

[Package sdcMicro version 5.6.1 Index]