dot-IdentificationRiskContinuousC: This function will compute the identification risk for a...

In RyanHornby/IdentificationRisk: Calculates Identification Risk for Synthetic Data

Description

This function will compute the identification risk for a dataset with synthetic continuous and categorical variables.

Usage

.IdentificationRiskContinuousC(
  dataMatrix,
  rows,
  cols,
  syndataMatrices,
  num,
  knowncols,
  numKnown,
  syncols,
  numSyn,
  radius,
  percentage,
  euclideanDist,
  categoricalVector
)

Arguments

radius	radius to compare with for continous variables. Radius is either percentage (default) or fixed. Radius can be the same for all continuous variables or specific to each. To specify for each use a vector, with the radii ordered in the same order those columns appear in the dataset.
percentage	true for a percentage radius, false for a constant radius
euclideanDist	true for a euclidean distance radius, false otherwise
categoricalVector	Boolean vector corresponding to the number of columns in the data, true means that column is categorical.
origdata	dataframe of the origonal data
syndata	list of the different synthetic dataframes
known	vector of the names of the columns in the dataset assumed to be known
syn	vector of the names of the columns in the dataset that are synthetic

RyanHornby/IdentificationRisk documentation built on May 8, 2021, 5:23 a.m.