valTable {sdcMicro} | R Documentation |
A Function for the comparison of different perturbation methods.
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)
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’ |
Tabelarise the output from summary.micro. Will be enhanced to all perturbation methods in future versions.
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’.
Matthias Templ
microaggregation
, summary.micro
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...