dataGen function

Fast generation of synthetic data

Fast generation of (primitive) synthetic multivariate normal data. methods

dataGen(obj, ...)

Arguments

• obj: an sdcMicroObj-class-object or a data.frame

• ...: see possible arguments below

  • n:: amount of observations for the generated data, defaults to 200
  • use:: howto compute covariances in case of missing values, see also argument use in cov. The default choice is 'everything', other possible choices are 'all.obs', 'complete.obs', 'na.or.complete' or 'pairwise.complete.obs'.

Returns

the generated synthetic data.

Details

Uses the cholesky decomposition to generate synthetic data with approx. the same means and covariances. For details see at the reference.

Note

With this method only multivariate normal distributed data with approxiomately the same covariance as the original data can be generated without reflecting the distribution of real complex data, which are, in general, not follows a multivariate normal distribution.

Examples

data(mtcars)

cov(mtcars[,4:6])
cov(dataGen(mtcars[,4:6]))
pairs(mtcars[,4:6])
pairs(dataGen(mtcars[,4:6]))

## for objects of class sdcMicro:
data(testdata2)
sdc <- createSdcObj(testdata2,
  keyVars=c('urbrur','roof','walls','water','electcon','relat','sex'),
  numVars=c('expend','income','savings'), w='sampling_weight')
sdc <- dataGen(sdc)

References

Mateo-Sanz, Martinez-Balleste, Domingo-Ferrer. Fast Generation of Accurate Synthetic Microdata. International Workshop on Privacy in Statistical Databases PSD 2004: Privacy in Statistical Databases, pp 298-306.

See Also

sdcMicroObj-class, shuffle

Author(s)

Matthias Templ