valTable {sdcMicro}R Documentation

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’

Details

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

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’.

Author(s)

Matthias Templ

See Also

microaggregation, summary.micro

Examples

data(Tarragona)
## 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...

[Package sdcMicro version 2.5.8 Index]