R: Local recoding via Edmond's maximum weighted matching...

LocalRecProg {sdcMicro} | R Documentation

## Local recoding via Edmond's maximum weighted matching algorithm

### Description

To be used on both categorical and numeric input variables, although usage on categorical variables is the focus of the development of this software.

### Usage

    LocalRecProg(
      obj,
      ancestors = NULL,
      ancestor_setting = NULL,
      k_level = 2,
      FindLowestK = TRUE,
      weight = NULL,
      lowMemory = FALSE,
      missingValue = NA,
      ...
    )

### Arguments

`obj` | a `data.frame` or a `sdcMicroObj-class`-object
`ancestors` | Names of ancestors of the cateorical variables
`ancestor_setting` | For each ancestor the corresponding categorical variable
`k_level` | Level for k-anonymity
`FindLowestK` | requests the program to look for the smallest k that results in complete matches of the data.
`weight` | A weight for each variable (Default=1)
`lowMemory` | Slower algorithm with less memory consumption
`missingValue` | The output value for a suppressed value.
`...` | see arguments below categoricalNames of categorical variables numericalNames of numerical variables

### Details

Each record in the data represents a category of the original data, and hence all records in the input data should be unique by the N Input Variables. To achieve bigger category sizes (k-anoymity), one can form new categories based on the recoding result and repeatedly apply this algorithm.

### Value

dataframe with original variables and the supressed variables (suffix _lr). / the modified `sdcMicroObj-class`

### Methods

list("signature(obj=\"sdcMicroObj\")")

### Author(s)

Alexander Kowarik, Bernd Prantner, IHSN C++ source, Akimichi Takemura

### References

Kowarik, A. and Templ, M. and Meindl, B. and Fonteneau, F. and Prantner, B.: Testing of IHSN Cpp Code and Inclusion of New Methods into sdcMicro, in: Lecture Notes in Computer Science, J. Domingo-Ferrer, I. Tinnirello (editors.); Springer, Berlin, 2012, ISBN: 978-3-642-33626-3, pp. 63-77. doi: 10.1007/978-3-642-33627-0_6

### Examples

    # LocalRecProg
    data(testdata2)
    r1=LocalRecProg(testdata2,
      categorical=c("urbrur", "roof", "walls", "water", "sex", "relat"),
      missingValue=-99)
    r2=LocalRecProg(testdata2,
      categorical=c("urbrur", "roof", "walls", "water", "sex", "relat"),
        ancestor=c("water2", "water3", "relat2"),
        ancestor_setting=c("water","water","relat"),missingValue=-99)
    r3=LocalRecProg(testdata2,
      categorical=c("urbrur", "roof", "walls", "water", "sex", "relat"),
        ancestor=c("water2", "water3", "relat2"),
        ancestor_setting=c("water","water","relat"),missingValue=-99,
        FindLowestK=FALSE)

    ## for objects of class sdcMicro:
    data(testdata2)
    sdc <- createSdcObj(testdata2,
      keyVars=c('urbrur','roof','walls','water','electcon','relat','sex'),
      numVars=c('expend','income','savings'), w='sampling_weight')
    sdc <- LocalRecProg(sdc)

[Package sdcMicro version 5.6.1 Index]