sdcMicro (version 2.6.3)

microaggregation: Microaggregation

Description

Function to perform various methods of microaggregation.

Usage

microaggregation(x, method = "pca", aggr = 3, nc = 8, clustermethod = "clara", opt = FALSE, measure = "mean", trim = 0, varsort = 1, transf = "log", blow = TRUE, blowxm = 0)

Arguments

x

data frame or matrix

method

pca, onedims, single, simple, clustpca, pppca, clustpppca, mdav, clustmcdpca, influence, mcdpca

aggr

aggregation level (default=3)

nc

number of cluster, if the chosen method performs cluster analysis

clustermethod

clustermethod, if necessary

opt

measure

aggregation statistic, mean, median, trim, onestep (default = mean)

trim

trimming percentage, if measure=trim

varsort

variable for sorting, if method= single

transf

transformation for data x

blow

if TRUE, the microaggregated data will have the same dimension as the original data set

blowxm

the microaggregated data with the same dimension as the original one.

Value

• x
  original data
• method
  method
• clustering
  TRUE, if a clustering is done before microaggregation
• aggr
  aggregation level
• nc
  number of clusters, if a clustering method is chosen
• xm
  aggregated data set
• roundxm
  rounded aggregated data set (to integers)
• clustermethod
  clustermethod, if a cluster method is chosen
• measure
  proximity measure for aggregation
• trim
  trimming, if proximity measure trim is chosen
• varsort
  information about the variable which is chosen when using method single
• transf
  transformation used, when clustering is applied first
• blow
  TRUE, blowxm is calculated
• blowxm
  microaggregated data with the same dimension as the original data set
• fot
  correction factor, necessary if totals calculated and n divided by aggr is not an integer.

Details

On http://neon.vb.cbs.nl/casc/Glossary.htm one can found the official definition of microaggregation: Records are grouped based on a proximity measure of variables of interest, and the same small groups of records are used in calculating aggregates for those variables. The aggregates are released instead of the individual record values. While for the proximity measure very different concepts can be used, the aggregation itself is naturally done with the arithmetic mean. Nevertheless, other measures of location can be used for aggregation, especially when the group size for aggregation has been taken higher than 3. Since the median seems to be unsuitable for microaggregation because of being highly robust, other mesures which are included can be chosen. This function contains also a method with which the data can be clustered with a variety of different clustering algorithms. Clustering observations before applying microaggregation might be useful. Note, that the data are automatically standardised before clustering. The usage of clustering method Mclust requires package mclust02, which must be loaded first. The package is not loaded automatically, since the package is not under GPL but comes with a different licence. The are also some projection methods for microaggregation included. The robust version pppca or clustpppca (clustering at first) are fast implementations and provide almost everytime the best results. Univariate statistics are preserved best with the individual ranking method (we called them onedims, however, often this method is named individual ranking), but multivariate statistics are strong affected. With method simple one can apply microaggregation directly on the (unsorted) data. It is useful for the comparison with other methods as a benchmark, i.e. replies the question how much better is a sorting of the data before aggregation. If blow is set to FALSE, the result will be a data set with dimension n divided by aggr.

References

http://www.springerlink.com/content/v257655u88w2/?sortorder=asc&p_o=20 Templ, M. and Meindl, B., Robust Statistics Meets {SDC}: New Disclosure Risk Measures for Continuous Microdata Masking, Lecture Notes in Computer Science, Privacy in Statistical Databases, vol. 5262, pp. 113-126, 2008. Templ, M. Statistical Disclosure Control for Microdata Using the R-Package sdcMicro, Transactions on Data Privacy, vol. 1, number 2, pp. 67-85, 2008. http://www.tdp.cat/issues/abs.a004a08.php Templ, M. New Developments in Statistical Disclosure Control and Imputation: Robust Statistics Applied to Official Statistics, Suedwestdeutscher Verlag fuer Hochschulschriften, 2009, ISBN: 3838108280, 264 pages.

See Also

summary.micro, plotMicro, valTable

Examples

data(Tarragona)
m1 <- microaggregation(Tarragona, method="onedims", aggr=3)
## summary(m1)