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)

CHANGES

- Update of the vignette.

BUG FIXES

- compare.synds() for variables with NA values in observed but not in synthetic data returns correct value (0) for NA category in synthetic data.
- Invalid
`times`

argument corrected (lists of numbers coerced to numbers).

NEW FEATURES

- Storing results of CART models when
`models`

set to TRUE. - Function syn.strata() for stratified synthesis.
- Function multi.compare() for multivariate comparison of synthesised and observed data.
- Synthesising method "nested" for a variable nested within another variable.
- Tabular utility function tab.utility() for comparing contingency tables from observed and synthesized data.
- Parameter
`uniques.exclude`

for the sdc() function, which can be used to remove some variables from the identification of uniques. - Function replicated.uniques() returns a number of unique individuals in the original data set ($no.uniques).

CHANGES

- Synthetic values of collinear variables are derived based on the one that is synthesised first and their method is set to "collinear". They do not have to be removed prior to synthesis.
- Synthesising method for constant variables is set to "constant" and the
variables are not removed from the synthesised data set when
`drop.not.used = TRUE`

. - Default synthesising
`method`

changed to "cart". - Default
`minnumlevels`

changed to -1 (during synthesis numeric variables are not changed to factors regardless of the number of distinct values). - Coefficient estimates and their confidence intervals are ploted in the same order as they are presented in a tabular form.
- No message on the seed value used (it is stored in the result object).
- Formula of the model to be fitted using glm.synds() or lm.synds() can be specified outside the function.
- Massage for sdc() on number of replicated uniques also when it is equal to zero.
- Maximum number of iterations for a multinomial model used in
`polyreg`

and`polr`

method increased to 1000 (`maxit`

parameter). Message if the limit is reached. - write.syn() saves complete synds object into a file synobject_filename.RData.
- Error on exceeding
`maxfaclevels`

in not generated if`method`

for the factor is set to "sample" or "nested". - For constant variables method is changed to
`constant`

. - Year format for variables
`ymarr`

and`ysepdiv`

in SD2011 dataset changed from`yy`

to`yyyy`

.

BUG FIXES

- Types and placement of special signs that are allowed in
`rules`

have been extended and include e.g. initial and closing round bracket. - compare.synds() provides output for logical variables.
- Synthesis of logical variables with missing values.
- Message about a change of method for a variable without predictors.
- Check for
`filetype`

in write.syn()

BUG FIXES

- No calling var(x) on a factor x (in checks).
- No
`contrasts`

attribute for factors synthesised using parametric method. - Misspelled vector name (nlevels) replaced with a correct one (nlevel).

NEW FEATURES

- A new function utility.synds() for distributional comparison of synthesised data with the original (observed) data using propensity scores.
- New measures for comparing model estimates based on synthesised and observed
data implemented in compare.fit.synds() function: standardized differences
in coefficient values(
`coef.diff`

) and confidence interval overlap (`ci.overlap`

).

CHANGES

- No dependency on
`coefplot`

package. - Default for
`drop.not.used`

changed to FALSE.

CHANGES

- Both variable names and their column indices can be used in
`visit.sequence`

. - Arguments
`rules`

,`rvalues`

,`cont.na`

,`semicont`

,`smoothing`

,`event`

,`denom`

are specified as named lists, e.g. rules = list(marital = "age < 18") and do not have to be specified for all variables. - Optional arguments can be passed to synthesising functions by specifying
`funname.argname`

arguments, e.g. ctree.minbucket = 5; they are function-specific;`minbucket`

removed from arguments. - Smoothing is possible for numeric variables when synthesised with the method 'sample'.
- compare() is a generic function with two methods (for class
`synds`

and`fit.synds`

); it replaced two separate functions. - New argument
`return.plot`

for compare() method for class`fit.synds`

. - New argument
`msel`

for compare() method for class`synds`

, which allows comparison for pooled or selected data set(s). Results for multiple synthetic data sets can be plotted on the same graph. - New argument
`nrow`

for compare() method for class`synds`

;`nrow`

and`ncol`

determine number of plots per screen. - Argument
`plot.na`

for compare() method for class`synds`

is no longer required and missing data categories for numeric variables are ploted on the same plot as non-missing values. - Argument
`object`

of lm.synds() and glm.synds() functions changed to`data`

. - print() method for class
`fit.synds`

gives by default combined coefficient estimates only. - summary() method for class
`fit.synds`

gives combined coefficient estimates and their standard errors. - summary() method for class
`synds`

with multiple synthetic data sets provides by default summaries that are calculated by averaging summary values for all synthetic data copies. - Argument
`obs.data`

of compare.fit.synds() function changed to`data`

. - Method
`surv.ctree`

and`cart.bboot`

changed to`survctree`

and`cartbboot`

.

BUG FIXES

`denom`

and`event`

for variables with missing data.`maxfaclevels`

can be increased.- Continuous variables with missing data when zero is a non-missing value.
- Synthesis of a single variable (with or without auxiliary predictors) now works.

NEW FEATURES

- Function sdc() for statistical disclosure control of the synthesised data set(s); function replicated.uniques() to determine which unique units in the synthesised data set(s) replicates unique units in the original data set.
- Function read.obs() to import original data sets form external files.
- Function write.syn() to export synthetic data sets to external files and create a text file with information about the synthesis.
- syn() has new
`semicont`

parameter that allows to define spike(s) for semi-continuous variables in order to synthesise them separately. `lognorm`

,`sqrtnorm`

and`cubertnorm`

methods for synthesis by linear regression after natural logarithm, square root or cube root transformation of a dependent variable.`seed`

argument for syn() function.

CHANGES

- Revised output of summary.fit.synds() and compare.fit.synds(); standard errors of Z scores corrected (se(Z.syn)) (thanks to Joerg Drechsler).
- Figures for compare.fit.synds() and compare.synds() functions plotted using ggplot2 functions.
- period.separated or alllowercase naming convention has been adopted and
parameter names
`populationInference`

,`visitSequence`

,`predictorMatrix`

,`contNA`

,`defaultMethod`

,`printFlag`

and`nlevelmax`

have been changed to`population.inference`

,`visit.sequence`

,`predictor.matrix`

,`cont.na`

,`default.method`

,`print.flag`

and`minnumlevels`

respectively. - Default for drop.pred.only changed to FALSE.

BUG FIXES

- Rounding procedure (thanks to bug report by Joerg Drechsler).
- Warning about extra disregarded argument
`family`

in compare.fit.synds().