sdcMicro (version 4.6.0)

suda2: Suda2: Detecting Special Uniques

Description

SUDA risk measure for data from (stratified) simple random sampling.

Usage

suda2(obj, ...)

Arguments

obj

object of class “data.frame” or object of class sdcMicroObj-class

...

see arguments below

• variablesCategorical (key) variables. Either the column names or and index of the variables to be used for risk measurement.
• missingMissing value coding in the given data set.
• DisFractionIt is the sampling fraction for the simple random sampling, and the common sampling fraction for stratified sampling. By default, it's set to 0.01.

Value

A modified sdcMicroObj-class object or the following list

• ContributionPercentThe contribution of each key variable to the SUDA score, calculated for each row.
• score The suda score.
• disscore The dis suda score

Methods

list("signature(obj = \"data.frame\")")

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

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

Details

Suda 2 is a recursive algorithm for finding Minimal Sample Uniques. The algorithm generates all possible variable subsets of defined categorical key variables and scans them for unique patterns in the subsets of variables. The lower the amount of variables needed to receive uniqueness, the higher the risk of the corresponding observation.

References

C. J. Skinner; M. J. Elliot (20xx) A Measure of Disclosure Risk for Microdata. Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 64 (4), pp 855--867.

M. J. Elliot, A. Manning, K. Mayes, J. Gurd and M. Bane (20xx) SUDA: A Program for Detecting Special Uniques, Using DIS to Modify the Classification of Special Uniques

Anna M. Manning, David J. Haglin, John A. Keane (2008) A recursive search algorithm for statistical disclosure assessment. Data Min Knowl Disc 16:165 -- 196

Examples

## Not run: 
# data(testdata2)
# data_suda2 <- suda2(testdata2,variables=c("urbrur","roof","walls","water","sex"))
# data_suda2
# summary(data_suda2)
# 
# ## 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 <- suda2(sdc)
# ## End(Not run)