syn.rf

Generates univariate synthetic data using Breiman's random forest algorithm 
classification and regression. It uses <code><a href="/link/randomForest?package=synthpop&version=1.9-0" data-mini-rdoc="synthpop::randomForest">randomForest</a></code> function 
from the randomForest package.

datagen

A tool for producing synthetic versions of microdata containing confidential information so that they are safe to be released to users for exploratory analysis. The key objective of generating synthetic data is to replace sensitive original values with synthetic ones causing minimal distortion of the statistical information contained in the data set. Variables, which can be categorical or continuous, are synthesised one-by-one using sequential modelling. Replacements are generated by drawing from conditional distributions fitted to the original data using parametric or classification and regression trees models. Data are synthesised via the function syn() which can be largely automated, if default settings are used, or with methods defined by the user. Optional parameters can be used to influence the disclosure risk and the analytical quality of the synthesised data. For a description of the implemented method see Nowok, Raab and Dibben (2016) <doi:10.18637/jss.v074.i11>. Functions to assess identity and attribute disclosure for the original and for the synthetic data are included in the package, and their use is illustrated in a vignette on disclosure (Practical Privacy Metrics for Synthetic Data).

Beata Nowok

synthpop

Generating Synthetic Versions of Sensitive Microdata for
Statistical Disclosure Control

Chris Dibben

Joshua Snoke

Caspar van Lissa

Lotte Pater

syn.rf function

<dl> <dt>y</dt>
<dd>an original data vector of length <code>n</code>.</dd> <dt>x</dt>
<dd>a matrix (<code>n</code> x <code>p</code>) of original covariates.</dd> <dt>xp</dt>
<dd>a matrix (<code>k</code> x <code>p</code>) of synthesised covariates.</dd> <dt>smoothing</dt>
<dd>smoothing method for numeric variable. See 
 <code>syn.smooth</code>.</dd> <dt>proper</dt>
<dd>for proper synthesis (<code>proper = TRUE</code>) a model is fitted 
 to a bootstrapped sample of the original data.</dd> <dt>ntree</dt>
<dd>number of trees to grow.</dd> <dt>...</dt>
<dd>additional parameters passed to 
 <code><a href="/link/randomForest?package=synthpop&version=1.9-0" data-mini-rdoc="synthpop::randomForest">randomForest</a></code>.</dd>
 
</dl>

Arguments

Generates univariate synthetic data using Breiman's random forest algorithm 
classification and regression. It uses <code><a href='https://rdrr.io/pkg/randomForest/man/randomForest.html'>randomForest</a></code> function 
from the randomForest package.

Synthesis with random forest — syn.rf

<dl>

 <dt>y</dt>
<dd>an original data vector of length <code>n</code>.</dd>

 <dt>x</dt>
<dd>a matrix (<code>n</code> x <code>p</code>) of original covariates.</dd>

 <dt>xp</dt>
<dd>a matrix (<code>k</code> x <code>p</code>) of synthesised covariates.</dd>

 <dt>smoothing</dt>
<dd>smoothing method for numeric variable. See 
 <code>syn.smooth</code>.</dd>

 <dt>proper</dt>
<dd>for proper synthesis (<code>proper = TRUE</code>) a model is fitted 
 to a bootstrapped sample of the original data.</dd>

 <dt>ntree</dt>
<dd>number of trees to grow.</dd>

 <dt>...</dt>
<dd>additional parameters passed to 
 <code><a href='https://rdrr.io/pkg/randomForest/man/randomForest.html'>randomForest</a></code>.</dd>
 
</dl>

syn.rf: Synthesis with random forest

Description

Usage

Value

Arguments

Details

References

See Also