sdcMicro (version 5.0.2)

valTable: Comparison of different microaggregation methods

Description

A Function for the comparison of different perturbation methods.

Usage

valTable(x, method = c("simple", "onedims", "clustpppca",
  "addNoise: additive", "swappNum"), measure = "mean",
  clustermethod = "clara", aggr = 3, nc = 8, transf = "log", p = 15,
  noise = 15, w = 1:dim(x)[2], delta = 0.1)

Arguments

x

data frame or matrix

method

microaggregation methods or adding noise methods or rank swapping.

measure

FUN for aggregation. Possible values are mean (default), median, trim, onestep.

clustermethod

clustermethod, if a method will need a clustering procedure

aggr

aggregation level (default=3)

nc

number of clusters. Necessary, if a method will need a clustering procedure

transf

Transformation of variables before clustering.

p

Swapping range, if method swappNum has been chosen

noise

noise addition, if an addNoise method has been chosen

w

variables for swapping, if method swappNum has been chosen

delta

parameter for adding noise method ‘correlated2’

Value

Measures of information loss splitted for the comparison of different methods.

Methods for adding noise should be named via “addNoise: method”, e.g. “addNoise: correlated”, i.e. the term ‘at first’ then followed by a ‘:’ and a blank and then followed by the name of the method as described in function ‘addNoise’.

Details

Tabelarise the output from summary.micro. Will be enhanced to all perturbation methods in future versions.

References

Templ, M. and Meindl, B., Software Development for SDC in R, Lecture Notes in Computer Science, Privacy in Statistical Databases, vol. 4302, pp. 347-359, 2006.

See Also

microaggregation, summary.micro

Examples

# NOT RUN {
data(Tarragona)
# }
# NOT RUN {
valTable(Tarragona[100:200,],
method=c("simple","onedims","pca","addNoise: additive"))
valTable(Tarragona,
method=c("simple","onedims","pca","clustpppca",
"mdav", "addNoise: additive", "swappNum"))
## clustpppca in combination with Mclust outperforms
## the other algorithms for this data set...
# }