sdcMicro (version 5.5.1)

dataGen: Fast generation of synthetic data

Description

Fast generation of (primitive) synthetic multivariate normal data.

Usage

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'.

Value

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.

References

Have a look at http://crises2-deim.urv.cat/docs/publications/lncs/443.pdf

See Also

sdcMicroObj-class, shuffle

Examples

# NOT RUN {
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)
# }