syn.normrank: Synthesis by normal linear regression preserving the marginal...

In synthpop: Generating Synthetic Versions of Sensitive Microdata for Statistical Disclosure Control

Synthesis by normal linear regression preserving the marginal distribution

Description

Generates univariate synthetic data using linear regression analysis and preserves the marginal distribution. Regression is carried out on Normal deviates of ranks in the original variable. Synthetic values are assigned from the original values based on the synthesised ranks that are transformed from their synthesised Normal deviates.

Usage

syn.normrank(y, x, xp, smoothing = "", proper = FALSE, ...)

Arguments

y	an original data vector of length n.
x	a matrix (n x p) of original covariates.
xp	a matrix (k x p) of synthesised covariates.
smoothing	smoothing method. See syn.smooth.
proper	a logical value specifying whether proper synthesis should be conducted. See details.
...	additional parameters.

Details

First generates synthetic values of Normal deviates of ranks of the values in y using the spread around the fitted linear regression line of Normal deviates of ranks given x. Then synthetic Normal deviates of ranks are transformed back to get synthetic ranks which are used to assign values from y. For proper synthesis first the regression coefficients are drawn from normal distribution with mean and variance from the fitted model. A smoothing methods can be applied by setting smoothing parameter (see syn.smooth). It is recommended as a tool to decrease the disclosure risk.

Value

A list with two components:

res	a vector of length k with synthetic values of y.
fit	a data frame with regression coefficients and error estimates.

See Also

syn, syn.norm, syn.lognorm, syn.smooth

synthpop documentation built on Aug. 31, 2022, 5:06 p.m.