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

`suda2(obj, ...)`

- obj
a

`data.frame`

or a sdcMicroObj-object- ...
see arguments below

`variables`

Categorical (key) variables. Either the column names or and index of the variables to be used for risk measurement.`missing`

: Missing value coding in the given data set.`DisFraction`

: It 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.`original_scores`

: if this argument is`TRUE`

(the default), the suda-scores are computed as described in paper "SUDA: A Program for Detecting Special Uniques" by Elliot et al., if`FALSE`

, the computation of the scores is slightly different as it was done in the original implementation of the algorithm by the IHSN.

A modified sdcMicroObj object or the following list

`ContributionPercent`

: The contribution of each key variable to the SUDA score, calculated for each row.`score`

: The suda score `disscore: The dis suda score`attribute_contributions:`

a`data.frame`

showing how much of the total risk is contributed by each variable. This information is stored in the following two variables:`variable`

: containing the name of the variable`contribution`

: contains how much risk a variable contributes to the total risk.

`attribute_level_contributions`

: returns risks of each attribute-level as a`data.frame`

with the following three columns:`variable`

: the variable name`attribute`

: holding relevant level-codes`contribution`

: contains the risk of this level within the variable.

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.

Since version >5.0.2, the computation of suda-scores has changed and is now by default as described in the original paper by Elliot et al.

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

Templ, M. Statistical Disclosure Control for Microdata: Methods and Applications in R.
*Springer International Publishing*, 287 pages, 2017. ISBN 978-3-319-50272-4.
doi:10.1007/978-3-319-50272-4