View source: R/functions.syn.r
syn.ctree, syn.cart  R Documentation 
Generates univariate synthetic data using classification and regression trees (without or with bootstrap).
syn.ctree(y, x, xp, smoothing = "", proper = FALSE, minbucket = 5, mincriterion = 0.9, ...) syn.cart(y, x, xp, smoothing = "", proper = FALSE, minbucket = 5, cp = 1e08, ...)
y 
an original data vector of length 
x 
a matrix ( 
xp 
a matrix ( 
smoothing 
smoothing method for numeric variable. See

proper 
for proper synthesis ( 
minbucket 
the minimum number of observations in
any terminal node. See 
cp 
complexity parameter. Any split that does not
decrease the overall lack of fit by a factor of cp is not
attempted. Small values of 
mincriterion 

... 
additional parameters passed to

The procedure for synthesis by a CART model is as follows:
Fit a classification or regression tree by binary recursive partitioning.
For each xp
find the terminal node.
Randomly
draw a donor from the members of the node and take the observed
value of y
from that draw as the synthetic value.
syn.ctree
uses ctree
function from the
party package and syn.cart
uses rpart
function from the rpart package. They differ, among others,
in a selection of a splitting variable and a stopping rule for the
splitting process.
A Guassian kernel smoothing can be applied to continuous variables
by setting smoothing parameter to "density"
. It is recommended
as a tool to decrease the disclosure risk. Increasing minbucket
is another means of data protection.
CART models were suggested for generation of synthetic data by Reiter (2005) and then evaluated by Drechsler and Reiter (2011).
A list with two components:
res 
a vector of length 
fit 
the fitted model which is an object of class 
Reiter, J.P. (2005). Using CART to generate partially synthetic, public use microdata. Journal of Official Statistics, 21(3), 441–462.
Drechsler, J. and Reiter, J.P. (2011). An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets. Computational Statistics and Data Analysis, 55(12), 3232–3243.
syn
, syn.survctree
,
rpart
, ctree
,
syn.smooth
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