Local recoding via Edmond's maximum weighted matching algorithm

Source: R/LocalRecProg.R

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

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

categorical

Names of categorical variables

numerical

Names of numerical variables

Value

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

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.

Methods

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

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

Author

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

Examples

data(testdata2)
cat_vars <- c("urbrur", "roof", "walls", "water", "sex", "relat")
anc_var <- c("water2", "water3", "relat2")
anc_setting <- c("water","water","relat")
# \donttest{
r1 <- LocalRecProg(
  obj = testdata2,
  categorical = cat_vars,
  missingValue = -99)
r2 <- LocalRecProg(
  obj = testdata2,
  categorical = cat_vars,
  ancestor = anc_var,
  ancestor_setting = anc_setting,
  missingValue = -99)
r3 <- LocalRecProg(
  obj = testdata2,
  categorical = cat_vars,
  ancestor = anc_var,
  ancestor_setting = anc_setting,
  missingValue = -99,
  FindLowestK = FALSE)

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