Generating synthetic versions of sensitive microdata for statistical disclosure control

Description

Generate synthetic versions of a data set using parametric or CART methods.

Details

Synthetic data are generated from the original (observed) data by the function syn. The package includes also tools to compare synthetic data with the observed data (compare.synds) and to fit (generalized) linear model to synthetic data (lm.synds, glm.synds) and compare the estimates with those for the observed data (compare.fit.synds). More extensive documentation on how to create synthetic data, with illustrative examples, is provided in the package vignette synthpop. Since that vignette was written more methods have been added to synthpop, including mthods for categorical variables based on log-linear models that can be made differentially private.

Now the package also includes functions to eavaluate the utility and disclosure risk of synthetic data. For details see the vignettes utility and disclosure. You can access all the vignettes via the index link at the bottom of this help page (synthpop-package)

Author(s)

Beata Nowok, Gillian M Raab, and Chris Dibben

References

Elliot, M. (2014) Final report on the disclosure risk associated with the synthetic data produced by the SYLLS team. Report 2015-2, Cathie Marsh Centre for Census and Survey Research (CCSR).

Nowok, B. Utility of synthetic microdata generated using tree-based methods (2015) Paper presented at the Privacy in Statistical Databases Conference 2016; Dubrovnik, Croatia, 14-16 September 2016 .

Nowok, B., Raab, G.M and Dibben, C. (2016). synthpop: Bespoke creation of synthetic data in R. Journal of Statistical Software, 74(11), 1-26. doi:10.18637/jss.v074.i11.

Raab, G.M., Nowok, B., and Dibben, C. (2016) Practical data synthesis for large samples Journal of Privacy and Confidentiality, 7(3):67-97. doi:10.29012/jpc.v7i3.407.

Raab, G.M., Nowok, B., and Dibben, C. (2016) Guidelines for producing useful synthetic data doi:10.48550/arXiv.1712.04078 An earlier version was presented at the Privacy in Statistical Databases Conference 2016; Dubrovnik, Croatia, 14-16 September 2016

Nowok, B., Raab, G.M. and Dibben, C. (2017) Providing bespoke synthetic data for the UK Longitudinal Studies and other sensitive data with the synthpop package for R Statistical Journal of the IAOS, 33(3):785-796. doi:10.3233/SJI-150153.

Raab, G.M., Nowok, B., and Dibben, C. (2021) Assessing, visualizing and improving the utility of synthetic data. Available atdoi:10.48550/arXiv.2109.12717. An earlier version was presented at the Joint UNECE/Eurostat expert meeting on statistical data confidentiality; Poznan, Poland, 1-3 December 2021.

Raab, G.M. (2022) Utility and Disclosure Risk for Differentially Private Synthetic Categorical Data, Chapter in Privacy in Statistical Databases 2022. Published in Springer Series Lecture notes in Computer Science. Also available at doi:10.48550/arXiv.2206.01362.

Raab, G.M., Nowok, B., and Dibben, C. (2024) Practical privacy metrics for synthetic data, Vignette in synthpop package. Also available at doi:10.48550/arXiv.2406.16826.

Raab, G.M. (2024) Privacy risk from synthetic data: practical proposals. Chapter in Privacy in Statistical databases 2024. published in Springer Series Lecture notes in Computer Science. Also available at doi:10.48550/arXiv.2409.04257.

Makes a codebook from a data frame

Description

Describes features of variables in a data frame relevant for synthesis.

Usage

codebook.syn(data, maxlevs = 3)

Arguments

Value

A list with two components.

tab - a data frame with the following information about each variable:

labs - a list of extra tables with labels for each factor with number of levels greater than maxlevs.

Examples

codebook.syn(SD2011)

Comparison of synthesised and observed data

Description

A generic function for comparison of synthesised and observed data. The function invokes particular methods which depend on the class of the first argument.

Usage

 compare(object, data, ...)

Arguments

Details

Compare methods facilitate quality assessment of synthetic data by comapring them with the original observed data sets. The data themselves (for class synds) or models fitted to them (for class fit.synds) are compared.

Value

The value returned by compare depends on the class of its argument. See the documentation of the particular methods for details.

See Also

compare.synds, compare.fit.synds

Compare model estimates based on synthesised and observed data

Description

The same model that was used for the synthesised data set is fitted to the observed data set. The coefficients with confidence intervals for the observed data is plotted together with their estimates from synthetic data. When more than one synthetic data set has been generated (object$m>1) combining rules are applied. Analysis-specific utility measures are used to evaluate differences between synthetic and observed data.

Usage

## S3 method for class 'fit.synds'
compare(object, data, plot = "Z", 
  print.coef = FALSE, return.plot = TRUE, plot.intercept = FALSE, 
  lwd = 1, lty = 1, lcol = c("#1A3C5A","#4187BF"), 
  dodge.height = .5, point.size = 2.5,
  population.inference = FALSE, ci.level = 0.95, ...)

## S3 method for class 'compare.fit.synds'
print(x, print.coef = x$print.coef, ...)

Arguments

Details

This function can be used to evaluate whether the method used for synthesis is appropriate for the fitted model. If this is the case the estimates from the synthetic data of what would be expected from the original data xpct(Beta) xpct(Z) should not differ from the estimates from the observed data (Beta and Z) by more than would be expected from the standard errors (se(Beta) and se(Z)). For more details see the vignette on inference.

Value

An object of class compare.fit.synds which is a list with the following components:

References

Karr, A., Kohnen, C.N., Oganian, A., Reiter, J.P. and Sanil, A.P. (2006). A framework for evaluating the utility of data altered to protect confidentiality. The American Statistician, 60(3), 224-232.

Nowok, B., Raab, G.M and Dibben, C. (2016). synthpop: Bespoke creation of synthetic data in R. Journal of Statistical Software, 74(11), 1-26. doi:10.18637/jss.v074.i11.

Reiter, J.P. (2003) Inference for partially synthetic, public use microdata sets. Survey Methodology, 29, 181-188.

See Also

summary.fit.synds

Examples

ods <- SD2011[,c("sex","age","edu","smoke")]
s1 <- syn(ods, m = 3)
f1 <- glm.synds(smoke ~ sex + age + edu, data = s1, family = "binomial")
compare(f1, SD2011) 
compare(f1, SD2011, print.coef = TRUE, plot = "coef")

Compare univariate distributions of synthesised and observed data

Description

Compare synthesised data set with the original (observed) data set using percent frequency tables and histograms. When more than one synthetic data set has been generated (object$m > 1), by default pooled synthetic data are used for comparison.

This function can be also used with synthetic data NOT created by syn(), but then an additional parameter cont.na might need to be provided.

Usage

## S3 method for class 'synds'
compare(object, data, vars = NULL,
        msel = NULL, stat = "percents", breaks = 20, ngroups =5,
        nrow = 2, ncol = 2, rel.size.x = 1,
        utility.stats = c("pMSE", "S_pMSE", "df"),
        utility.for.plot = "S_pMSE",
        cols = c("#1A3C5A","#4187BF"),
        plot = TRUE, table = FALSE, 
        print.flag = TRUE, ...)

## S3 method for class 'data.frame'
compare(object, data, vars = NULL, cont.na = NULL,
        msel = NULL, stat = "percents", breaks = 20,ngroups = 5,
        nrow = 2, ncol = 2, rel.size.x = 1,
        utility.stats = c("pMSE", "S_pMSE", "df"),
        utility.for.plot = "S_pMSE",
        cols = c("#1A3C5A","#4187BF"),
        plot = TRUE, table = FALSE, 
        print.flag = TRUE, compare.synorig = TRUE, ...)

## S3 method for class 'list'
compare(object, data, vars = NULL, cont.na = NULL,
        msel = NULL, stat = "percents", breaks = 20,ngroups = 5,
        nrow = 2, ncol = 2, rel.size.x = 1,
        utility.stats = c("pMSE", "S_pMSE", "df"),
        utility.for.plot = "S_pMSE",
        cols = c("#1A3C5A","#4187BF"),
        plot = TRUE, table = FALSE, 
        print.flag = TRUE, compare.synorig = TRUE, ...)

## S3 method for class 'compare.synds'
print(x, ...)

Arguments

Details

Missing data categories for numeric variables are plotted on the same plot as non-missing values. They are indicated by miss. suffix.

Numeric variables with fewer than 6 distinct values are changed to factors in order to make plots more readable.

Value

An object of class compare.synds which is a list including a list of comparative frequency tables (tables) and a ggplot object (plots) with bar charts/histograms. If multiple plots are produced they and their corresponding frequency tables are stored as a list.

References

Nowok, B., Raab, G.M and Dibben, C. (2016). synthpop: Bespoke creation of synthetic data in R. Journal of Statistical Software, 74(11), 1-26. doi:10.18637/jss.v074.i11.

See Also

multi.compare

Examples

ods <- SD2011[ , c("sex", "age", "edu", "marital", "ls", "income")]
s1  <- syn(ods, cont.na = list(income = -8))

### synthetic data provided as a 'synds' object
compare(s1, ods, vars = "ls")
compare(s1, ods, vars = "income", stat = "counts",
        table = TRUE, breaks = 10)

### synthetic data provided as 'data.frame'
compare(s1$syn, ods, vars = "ls")
compare(s1$syn, ods, vars = "income", cont.na = list(income = -8),
        stat = "counts", table = TRUE, breaks = 10)

Disclosure measures

Description

Calculates disclosure measures for synthetic data. NOTE: The other function that calculates disclosure results for multiple targets has been renamed as multi.disclosure from disclosure.summary.

Usage

## S3 method for class 'synds'
disclosure(object, data, keys , target , print.flag = TRUE,
           denom_lim = 5, exclude_ov_denom_lim = FALSE, not.targetlev = NULL,
           usetargetNA = TRUE, usekeysNA = TRUE, 
           exclude.keys =NULL, exclude.keylevs = NULL, exclude.targetlevs = NULL,
           ngroups_target = NULL, ngroups_keys = NULL, 
           thresh_1way = c(50, 90),thresh_2way = c(4, 80),
           digits = 2, to.print =c("short"),...) 

## S3 method for class 'data.frame'
disclosure(object, data,cont.na = NULL, keys , target , print.flag = TRUE,
           denom_lim = 5, exclude_ov_denom_lim = FALSE, 
           not.targetlev = NULL,
           usetargetNA = TRUE, usekeysNA = TRUE, 
           exclude.keys =NULL, exclude.keylevs = NULL, exclude.targetlevs = NULL,
           ngroups_target = NULL, ngroups_keys = NULL, 
           thresh_1way = c(50, 90),thresh_2way = c(4, 80),
           digits = 2, to.print =c("short"), compare.synorig = TRUE, ...) 

## S3 method for class 'list'
disclosure(object, data,cont.na = NULL, keys , target , print.flag = TRUE,
           denom_lim = 5, exclude_ov_denom_lim = FALSE, 
           not.targetlev = NULL,
           usetargetNA = TRUE, usekeysNA = TRUE, 
           exclude.keys =NULL, exclude.keylevs = NULL, exclude.targetlevs = NULL,
           ngroups_target = NULL, ngroups_keys = NULL, 
           thresh_1way = c(50, 90),thresh_2way = c(4, 80),
           digits = 2, to.print =c("short"), compare.synorig = TRUE, ...) 
           
## S3 method for class 'disclosure'
print(x,  to.print =NULL, digits = NULL, ...)

Arguments

Details

Calculates identity disclosure measures for a for a set of keys, (quasi identifiers) and attribute disclosure measures for one variable from the same set of keys considered as a target. The function multi.disclosure calls this function and summarises the attribute disclosure measures for multiple targets. See the vignette

Value

An object of class disclosure which is a list with the following components.

Note

See package vignette disclosure.pdf for additional information including formal definitions of all quantities and worked examples.

References

See references in package vignette

See Also

syn multi.disclosure

Examples

library(synthpop)
ods <- SD2011[, c("sex", "age", "edu", "marital", "income")]
odsF <- numtocat.syn(ods, numtocat = "income", catgroups = 7, cont.na = list(income = -8))
s1 <- syn(odsF$data, method = "ctree",seed = 75, m=3, k=1000)
disc1 <- disclosure(s1, odsF$data, target = "income", 
                    keys = c("sex", "age", "edu","marital"))

Fitting (generalized) linear models to synthetic data

Description

Fits generalized linear models or simple linear models to the synthesised data set(s) using glm and lm function respectively.

Usage

glm.synds(formula, family = "binomial", data,  ...)
lm.synds(formula, data, ...)

## S3 method for class 'fit.synds'
print(x, msel = NULL, ...) 

Arguments

Value

The summary function (summary.fit.synds) can be used to obtain the combined results of models fitted to each of the m synthetic data sets.

An object of class fit.synds. It is a list with the following components:

See Also

glm, lm, multinom.synds, polr.synds, compare.fit.synds, summary.fit.synds

Examples

### Logit model 
ods <- SD2011[1:1000, c("sex", "age", "edu", "marital", "ls", "smoke")]
s1 <- syn(ods, m = 3)
f1 <- glm.synds(smoke ~ sex + age + edu + marital + ls, data = s1, family = "binomial")
f1
print(f1, msel = 1:2)
  
### Linear model
ods <- SD2011[1:1000,c("sex", "age", "income", "marital", "depress")]
ods$income[ods$income == -8] <- NA
s2 <- syn(ods, m = 3)
f2 <- lm.synds(depress ~ sex + age + log(income) + marital, data = s2)
f2
print(f2,1:3)

Merge levels of factors in a data frame

Description

merges levels of selected variables in a data frame either according to minimum numbers in a category or according to user-defined rules.

Usage

mergelevels.syn(data, vars = NULL, newlabel = FALSE, addNA = FALSE,
                    print.flag = FALSE, minsize = 10, merge.byhand =
                    FALSE, merge.details = NULL)

Arguments

.

Value

A data frame of the same size and structure as data with levels of selected variables merged.

See Also

sdc

Examples

test <- SD2011[1:20]
data.mlevs1 <- mergelevels.syn(test, vars = c(3,5,18:20),minsize = 20, addNA = TRUE, 
              print.flag = TRUE, newlabel = TRUE)

mlevs <- list(agegr = c("60+", "60-64"  , "65+"),socprof = c("NEW","UNEMPLOYED","FARMER"))
data.mlevs2 <-mergelevels.syn(test, merge.byhand = TRUE, merge.details = mlevs, 
           addNA=TRUE, print.flag = TRUE)  

Multivariate comparison of synthesised and observed data

Description

Graphical comparisons of a variable (var) in the synthesised data set with the original (observed) data set within subgroups defined by the variables in a vector by. var can be a factor or a continuous variable and the plots produced will depend on the class of var. The variables in by will usually be factors or variables with only a few values.

Usage

multi.compare(object, data, var = NULL, by = NULL, msel = NULL, 
  barplot.position = "fill", cont.type = "hist", y.hist = "count", 
  boxplot.point = TRUE, binwidth = NULL, ...)

Arguments

Value

Plots as specified above. A table of the numbers in the subgroups is printed to the R console.

Numeric variables with fewer than 6 distinct values are changed to factors in order to make plots more readable.

See Also

compare.synds, compare.fit.synds

Examples

### default synthesis of selected variables
vars <- c("sex", "age", "edu", "smoke")
ods  <- na.omit(SD2011[1:1000, vars])
s1 <- syn(ods)

### categorical var
multi.compare(s1, ods, var = "smoke", by = c("sex","edu"))

### numeric var
multi.compare(s1, ods, var = "age", by = c("sex"), y.hist = "density", binwidth = 5)
multi.compare(s1, ods, var = "age", by = c("sex", "edu"), cont.type = "boxplot")

Disclosure measures for multiple of target variables.

Description

Calculates, prints and plots tables of disclosure measures for a set of target variables from a fixed set of keys to form quasi-identifiers. The calculations of disclosure measures are done by the function disclosure for each target.

This function can be also used with synthetic data NOT created by syn(), or even made anonymous by other methods such as sampling More details of the measures calculated can be found in the package vignette "Disclosure measures for Synthetic Data".

Usage

## S3 method for class 'synds'
multi.disclosure(object, data, 
           keys , targets = NULL, print.flag = TRUE, 
           denom_lim = 5, exclude_ov_denom_lim = FALSE,
           not.targetslev = NULL,  
           usetargetsNA = TRUE,  usekeysNA = TRUE, 
           exclude.keys = NULL, exclude.keylevs = NULL,  exclude.targetlevs = NULL,
           ngroups_targets = NULL, ngroups_keys = NULL, 
           ident.meas = "repU", attrib.meas = "DiSCO",
           thresh_1way = c(50, 90),thresh_2way = c(4, 80), 
           digits = 2, plot = TRUE,  ...)

               
## S3 method for class 'data.frame'
multi.disclosure(object, data, cont.na = NULL, 
           keys , targets = NULL,  print.flag = TRUE, 
           denom_lim = 5, exclude_ov_denom_lim = FALSE,
           not.targetslev = NULL, 
           usetargetsNA = TRUE,  usekeysNA = TRUE, 
           exclude.keys = NULL, exclude.keylevs = NULL,  exclude.targetlevs = NULL,
           ngroups_targets = NULL, ngroups_keys = NULL, 
           ident.meas = "repU", attrib.meas = "DiSCO",
           thresh_1way = c(50, 90),thresh_2way = c(4, 80), 
           digits = 2, plot = TRUE,  compare.synorig = TRUE,  ...)

## S3 method for class 'list'
multi.disclosure(object, data, cont.na = NULL,
            keys , targets = NULL,  print.flag = TRUE, 
            denom_lim = 5, exclude_ov_denom_lim = FALSE,
           not.targetslev = NULL,  
           usetargetsNA = TRUE,  usekeysNA = TRUE, 
           exclude.keys = NULL, exclude.keylevs = NULL, exclude.targetlevs = NULL,
           ngroups_targets = NULL, ngroups_keys = NULL, 
           ident.meas = "repU", attrib.meas = "DiSCO",
           thresh_1way = c(50, 90),thresh_2way = c(4, 80), 
           digits = 2, plot = TRUE, compare.synorig = TRUE,...)


## S3 method for class 'multi.disclosure'
print(x, digits = NULL, plot = NULL, to.print =  c("ident","attrib"),
       ...)

Arguments

Details

Calculates measures of identity and attribution disclosure from the keys specified in keys with the function disclosure. For attribute disclosure a table with one line for each target can be printed or plotted. Details are in help file for disclosure.

Value

An object of class multi.disclosure which is a list with the following components:

References

to follow link to vignette

See Also

disclosure

Examples

ods <- SD2011[, c("sex", "age", "edu", "marital", "region", "income")]
s1 <- syn(ods)

### synthetic data provided as a 'data.frame' object
t1 <- multi.disclosure(s1$syn, ods,
keys = c("sex", "age", "edu"))

### synthetic data provided as a 'synds' object  
t1 <- multi.disclosure(s1, ods, 
keys = c("sex", "age", "edu"))

Fitting multinomial models to synthetic data

Description

Fits multinomial models to the synthesised data set(s) using the multinom function.

Usage

multinom.synds(formula, data,  ...)

Arguments

Value

To print the results the print function (print.fit.synds) can be used. The summary function (summary.fit.synds) can be used to obtain the combined results of models fitted to each of the m synthetic data sets.

An object of class fit.synds. It is a list with the following components:

See Also

multinom, glm.synds, polr.synds, print.fit.synds, summary.fit.synds, compare.fit.synds

Examples

ods <- SD2011[1:1000, c("sex", "age", "edu", "marital", "ls", "smoke")]
s1 <- syn(ods, m = 3)
f1 <- multinom.synds(edu  ~ sex + age, data = s1)
summary(f1)
print(f1, msel = 1:2)
compare(f1, SD2011)

Group numeric variables before synthesis

Description

Selected numeric variables are grouped into factors with ranges selected from the data.

Usage

numtocat.syn(data, numtocat = NULL, print.flag = TRUE, cont.na = NULL, 
             catgroups = 5, style.groups = "quantile") 

Arguments

Value

A list with the following components:

Examples

SD2011.cat <- numtocat.syn(SD2011, cont.na = list(income = -8 , unempdur = -8, 
nofriend = -8))
summary(SD2011.cat$data)

Fitting ordered logistic models to synthetic data

Description

Fits ordered logistic models to the synthesised data set(s) using the polr function.

Usage

polr.synds(formula, data, ...)

Arguments

Value

To print the results the print function (print.fit.synds) can be used. The summary function (summary.fit.synds) can be used to obtain the combined results of models fitted to each of the m synthetic data sets.

An object of class fit.synds. It is a list with the following components:

See Also

polr, glm.synds, multinom.synds, print.fit.synds, summary.fit.synds, compare.fit.synds

Examples

ods <- SD2011[1:1000, c("sex", "age", "edu", "marital", "ls", "smoke")]
s1 <- syn(ods, m = 3)
f1 <- polr.synds(edu  ~ sex + age, data = s1)
summary(f1)
print(f1, msel = 1:2)
compare(f1, SD2011)

Importing original data sets form external files

Description

Imports data data sets form external files into a data frame. Currently supported files include: sav (SPSS), dta (Stata), xpt (SAS), csv (comma-separated file), tab (tab-delimited file) and txt (delimited text files). For SPSS, Stata and SAS it uses functions from the foreign package with some adjustments where necessary.

Usage

read.obs(file, convert.factors = TRUE, lab.factors = FALSE, 
export.lab = FALSE, ...)

Arguments

Value

A data frame with an imported data set. For SPSS, Stata and SAS it has attributes with labels.

See Also

write.syn

Replications in synthetic data

Description

Determines which unique units in the synthesised data set(s) have combinations of variables in the keys as follows:

1) unique in original data

2) unique in the synthetic data set(s)

3) unique in synthetic data and present,but not necessarily unique in original

4) unique in synthetic and unique in original.

For each of 3) and 4) results are returned that identify the rows in the synthetic data with each type of unique. This function is called by sdc where there are options to include each type of unique.

Usage

replicated.uniques(object, data, keys = names(data))

## S3 method for class 'repuniq.synds'
print(x, ...)

Arguments

Value

A list of class "repuniq.synds" with the following components:

See Also

sdc

Examples

ods <- SD2011[1:1000,c("sex","age","region","edu","marital","smoke")]
s1 <- syn(ods, m = 2)
replicated.uniques(s1,ods, keys = c("sex","age","region"))

Social Diagnosis 2011 - Objective and Subjective Quality of Life in Poland

Description

Sample of 5,000 individuals from the Social Diagnosis 2011 survey; selected variables only.

Usage

SD2011

Format

A data frame with 5,000 observations on the following 35 variables:

sex

Sex

age

Age of person, 2011

agegr

Age group, 2011

placesize

Category of the place of residence

region

Region (voivodeship: a province in Poland, the highest level of administrative division in the country)

edu

Highest educational qualification, 2011

eduspec

Discipline of completed qualification

socprof

Socio-economic status, 2011

unempdur

Total duration of unemployment in the last 2 years (in months)

income

Personal monthly net income

marital

Marital status

mmarr

Month of marriage

ymarr

Year of marriage

msepdiv

Month of separation/divorce

ysepdiv

Year of separation/divorce

ls

Perception of life as a whole

depress

Depression symptoms indicator

trust

View on interpersonal trust

trustfam

Trust in own family members

trustneigh

Trust in neighbours

sport

Active engagement in some form of sport or exercise

nofriend

Number of friends

smoke

Smoking cigarettes

nociga

Number of cigarettes smoked per day

alcabuse

Drinking too much alcohol

alcsol

Starting to use alcohol to cope with troubles

workab

Working abroad in 2007-2011

wkabdur

Total time spent on working abroad

wkabint

Plans to go abroad to work in the next two years

wkabintdur

Intended duration of working abroad

emcc

Intended destination country

englang

Knowledge of English language

height

Height of person

weight

Weight of person

bmi

Body mass index

Note

Please note that the original variable names have been changed to make them more self-explanatory. Some variable labels have been adjusted as well.

Source

Council for Social Monitoring. Social Diagnosis 2000-2011: integrated database. http://www.diagnoza.com/index-en.html [downloaded on 13/12/2013]

References

Czapinski J. and Panek T. (Eds.) (2011). Social Diagnosis 2011. Objective and Subjective Quality of Life in Poland - full report. Contemporary Economics, Volume 5, Issue 3 (special issue) http://ce.vizja.pl/en/issues/volume/5/issue/3#art254

Examples

  spineplot(englang ~ agegr, data = SD2011, xlab = "Age group", ylab = "Knowledge of English")
  boxplot(income ~ sex, data = SD2011[SD2011$income != -8,])

Tools for statistical disclosure control (sdc)

Description

Labeling, top and bottom coding, smoothing numeric data, and removing different types of unique records defined by keys from synthetic data. The function calls replicated.uniques to identify the rows to be excluded from the synthetic data set(s)

Usage

sdc(object, data,keys = NULL, prefix = NULL, suffix = NULL, label = NULL, 
rm.uniques.in.orig = FALSE, rm.replicated.uniques = FALSE, 
recode.vars = NULL, bottom.top.coding = NULL, 
 recode.exclude = NULL, smooth.vars = NULL)

Arguments

Value

An object provided as an argument adjusted in accordance with the other parameters' values.

See Also

replicated.uniques

Examples

ods <- SD2011[1:1000,c("sex","age","region","edu","marital","income")]
s1 <- syn(ods, m = 2)
s1.sdc <- sdc(s1, ods, keys = c("sex","age","region"),suffix = "_synthetic",
label="false_data", rm.uniques.in.orig = TRUE,
recode.vars = c("age","income"),
bottom.top.coding = list(c(20,80),c(NA,2000)),
recode.exclude = list(NA,c(NA,-8)))
head(s1.sdc$syn[[2]])

Inference from synthetic data

Description

Combines the results of models fitted to each of the m synthetic data sets.

Usage

## S3 method for class 'fit.synds'
summary(object, population.inference = FALSE, msel = NULL,
  real.varcov = NULL, incomplete = NULL, ...)

## S3 method for class 'summary.fit.synds'
print(x, ...)

Arguments

Details

The mean of the estimates from each of the m synthetic data sets yields asymptotically unbiased estimates of the coefficients if the observed data conform to the distribution used for synthesis. The standard errors are estimated differently depending whether inference is made for the results that we would expect to obtain from the observed data or for the parameters of the population that we assume the observed data are sampled from. The standard errors also differ according to whether synthetic data were produced using simple or proper synthesis (for details see Raab et al. (2017)).

Value

An object of class summary.fit.synds which is a list with the following components:

References

Nowok, B., Raab, G.M and Dibben, C. (2016). synthpop: Bespoke creation of synthetic data in R. Journal of Statistical Software, 74(11), 1-26. doi:10.18637/jss.v074.i11.

Raab, G.M., Nowok, B. and Dibben, C. (2017). Practical data synthesis for large samples. Journal of Privacy and Confidentiality, 7(3), 67-97. Available at: https://journalprivacyconfidentiality.org/index.php/jpc/article/view/407

Reiter, J.P. (2003) Inference for partially synthetic, public use microdata sets. Survey Methodology, 29, 181-188.

See Also

compare.fit.synds, summary, print

Examples

ods <- SD2011[1:1000,c("sex","age","edu","ls","smoke")]
  
### simple synthesis
s1 <- syn(ods, m = 5)
f1 <- glm.synds(smoke ~ sex + age + edu + ls, data = s1, family = "binomial")
summary(f1)
summary(f1, population.inference = TRUE)
  
### proper synthesis
s2 <- syn(ods, m = 5, method = "parametric", proper = TRUE)
f2 <- glm.synds(smoke ~ sex + age + edu + ls, data = s2, family = "binomial")
summary(f2)
summary(f2, population.inference = TRUE)

Synthetic data object summaries

Description

Produces summaries of the synthesised variables. When more than one synthetic data set has been generated (object$m > 1), by default summaries are calculated by averaging summary values for all synthetic data copies (see msel argument).

Usage

## S3 method for class 'synds'
summary(object, msel = NULL, maxsum = 7, 
  digits = max(3, getOption("digits")-3), ...)

## S3 method for class 'summary.synds'
print(x, ...)

Arguments

Details

See summary for more details.

Value

An object of class summary.synds, which is a list with the following components:

References

Nowok, B., Raab, G.M and Dibben, C. (2016). synthpop: Bespoke creation of synthetic data in R. Journal of Statistical Software, 74(11), 1-26. doi:10.18637/jss.v074.i11.

See Also

summary,print

Examples

s1 <- syn(SD2011[,c("sex","age","edu","marital")], m = 3)
summary(s1)
summary(s1, msel = c(1,3))

Generating synthetic data sets

Description

Generates synthetic version(s) of a data set. Function syn.strata() performs stratified synthesis.

Usage

syn(data, method = "cart", visit.sequence = (1:ncol(data)),
    predictor.matrix = NULL,  
    m = 1, k = nrow(data), proper = FALSE, 
    minnumlevels = 1, maxfaclevels = 60, 
    rules = NULL, rvalues = NULL, 
    cont.na = NULL, semicont = NULL, 
    smoothing = NULL, event = NULL, denom = NULL, 
    drop.not.used = FALSE, drop.pred.only = FALSE, 
    default.method = c("normrank", "logreg", "polyreg", "polr"),
    numtocat = NULL, catgroups = rep(5, length(numtocat)), 
    models = FALSE, print.flag = TRUE, seed = "sample", ...)
    
syn.strata(data, strata = NULL, 
    minstratumsize = 10 + 10 * length(visit.sequence),  
    tab.strataobs = TRUE, tab.stratasyn = FALSE,
    method = "cart", visit.sequence = (1:ncol(data)),
    predictor.matrix = NULL,
    m = 1, k = nrow(data), proper = FALSE, 
    minnumlevels = 1, maxfaclevels = 60, 
    rules = NULL, rvalues = NULL,
    cont.na = NULL, semicont = NULL, 
    smoothing = NULL, event = NULL, denom = NULL, 
    drop.not.used = FALSE, drop.pred.only = FALSE,
    default.method = c("normrank", "logreg", "polyreg", "polr"),
    numtocat = NULL, catgroups = rep(5,length(numtocat)), 
    models = FALSE, print.flag = TRUE, seed = "sample", ...)    
   
## S3 method for class 'synds'
print(x, ...)

Arguments

Details

Only variables that are in visit.sequence with corresponding non-empty method are synthesised. The only exceptions are event indicators. They are synthesised along with the corresponding time to event variables and should not be included in visit.sequence. All other variables (not in visit.sequence or in visit.sequence with a corresponding blank method) can be used as predictors. Including them in visit.sequence generates a default predictor.matrix reflecting the order of variables in the visit.sequence otherwise predictor.matrix has to be adjusted accordingly. All predictors of the variables that are not in visit.sequence or are in visit.sequence but with a blank method are removed from predictor.matrix.

Variables to be synthesised that are not synthesised yet cannot be used as predictors. Also all variables used in passive synthesis or in restricted values rules (rules) have to be synthesised before the variables they apply to.

Mismatch between data type and synthesising method stops execution and print an error message but numeric variables with number of levels less than minnumlevels are changed into factors and methods are changed automatically, if necessary, to methods for categorical variables. Methods for variables not in a visit sequence will be changed into blank.

The built-in elementary synthesising methods defined by conditional distributions include:

ctree, cart

classification and regression trees (CART), see syn.cart

bagging, random forests, ranger

methods using ensembles of CART trees, see syn.bag, syn.rf, and syn.ranger

survctree

classification and regression trees (CART) for duration time data (parametric methods for survival data are not implemented yet), see syn.survctree

norm

normal linear regression, see syn.norm

normrank

normal linear regression preserving the marginal distribution, see syn.normrank

lognorm, sqrtnorm, cubertnorm

normal linear regression after natural logarithmic, square root and cube root transformation of a dependent variable respectively, see syn.lognorm

logreg

logistic regression, see syn.logreg

polyreg

unordered polytomous regression, see syn.polyreg

polr

ordered polytomous regression, see syn.polr

pmm

predictive mean matching, see syn.pmm

sample

random sample from the observed data, see syn.sample

passive

function of other synthesised data, see syn.passive

nested

bootstrap sample within each category of the original grouping variable, see syn.nested

satcat

bootstrap sample within each category of the crosstabulation of all the predictor variables, see syn.satcat

These methods use a group of variables that are synthesised together. They must always be together at the start of the visit sequence:

catall

fit a saturated log-linear model, see syn.catall

ipf

fit a log-linear model, defined by its margins, by iterative proportional fitting see syn.ipf

The functions corresponding to these methods are called syn.method, where method is a string with the name of a synthesising method. For instance a function corresponding to ctree function is called syn.ctree. A new synthesising method can be introduced by writing a function named syn.newmethod and then specifying method parameter of syn() function as "newmethod".

In order to use "nested" sampling, method parameter of syn function has to be specified as "nested.varname", where "varname" is the name of the grouped (less detailed) variable, the only one used in nested synthesis. A variable synthesised using "nested" method is excluded from synthesising other variables except when used for "nested" method.

Additional parameters can be passed to synthesising methods as part of the dots argument. They have to be named using period-separated method and parameter name (method.parameter). For instance, in order to set a minbucket (minimum number of observations in any terminal node of a CART model) for a ctree synthesising method, ctree.minbucket has to be specified. The parameters are method-specific and will be used for all variables to be synthesised using that method. See help for syn.method for further details about the allowed parameters for a specific method.

Value

The summary function (summary.synds) can be used to obtain a summary of the synthesised variables.

An object of class synds, which stands for 'synthesised data set'. It is a list with the following components:

When syn.strata() is used there are two additional components:

Note also that when syn.strata is used most values of the items are matrices with each row corresponding to a stratum or lists with one element per stratum.

Note

See package vignette for additional information.

References

Nowok, B., Raab, G.M and Dibben, C. (2016). synthpop: Bespoke creation of synthetic data in R. Journal of Statistical Software, 74(11), 1-26. doi:10.18637/jss.v074.i11.

See Also

compare.synds, summary.synds

Examples

### selection of variables
vars <- c("sex","age","marital","income","ls","smoke")
ods  <- SD2011[1:1000, vars]
 
### default synthesis
s1 <- syn(ods)
s1
  
### synthesis with default parametric methods
s2 <- syn(ods, method = "parametric", seed = 123)
s2$method
  
### multiple synthesis of selected variables with customised methods
s3 <- syn(ods, visit.sequence = c(2, 1, 4, 5), m = 2,
          method = c("logreg","sample","","normrank","ctree",""),
          ctree.minbucket = 10)
summary(s3)
summary(s3, msel = 1:2)
  
### adjustment to the default predictor matrix 
s4.ini <- syn(data = ods, visit.sequence = c(1, 2, 5, 3),
              m = 0, drop.not.used = FALSE)
pM.cor <- s4.ini$predictor.matrix
pM.cor["marital","ls"] <- 0
s4 <- syn(data = ods, visit.sequence = c(1, 2, 5, 3),
          predictor.matrix = pM.cor)
  
### handling missing values in continuous variables
s5 <- syn(ods, cont.na = list(income = c(NA, -8)))
  
### rules for restricted values - marital status of males under 18 should be 'single'
s6 <- syn(ods, rules = list(marital = "age < 18 & sex == 'MALE'"),
          rvalues = list(marital = 'SINGLE'), method = "parametric", seed = 123)
with(s6$syn, table(marital[age < 18 & sex == 'MALE']))
### results for default parametric synthesis without the rule  
with(s2$syn, table(marital[age < 18 & sex == 'MALE']))

### synthesis with ipf for all variables
s7 <- syn(ods[, 1:3], method = "ipf", numtocat = "age")

### alternatively group the numeric variable before synthesis to save
### the grouped data rather than the numeric in the synthetic data set
ods.cat <- numtocat.syn(ods, numtocat = "age", catgroups = 10)$data
s8 <- syn(ods.cat[, 1:3], method = "ipf")

### stratified synthesis
s9 <- syn.strata(ods, strata = "sex")

Synthesis with bagging

Description

Generates univariate synthetic data using bagging. It uses randomForest function from the randomForest package with number of sampled predictors equal to number of all predictors.

Usage

syn.bag(y, x, xp, smoothing = "", proper = FALSE, ntree = 10, ...)

Arguments

Details

...

Value

A list with two components:

References

...

See Also

syn, syn.rf, syn.cart, randomForest, syn.smooth

Synthesis of a group of categorical variables from a saturated model

Description

A saturated model is fitted to a table produced by cross-tabulating all the variables.

Usage

syn.catall(x, k, proper = FALSE, priorn = 1, structzero = NULL, 
           maxtable = 1e8, epsilon = 0,  delta = 0.05,rand = TRUE,
          noisetype = "",  ...)

Arguments

Details

When used in syn function the group of categorical variables with method = "catall" must all be together at the start of the visit.sequence. Subsequent variables in visit.sequence are then synthesised conditional on the synthesised values of the grouped variables. A saturated model is fitted to a table produced by cross-tabulating all the variables. Prior probabilities for the proportions in each cell of the table are specified from the parameters of a Dirichlet distribution with the same parameter for every cell in the table that is not a structural zero (see above). The sum of these parameters is priorn so that each one is priorn/N where N is the number of cells in the table that are not structural zeros. The default priorn = 1 can be thought of as equivalent to the knowledge that 1 observation would be equally likely to be in any cell that is not a structural zero. The posterior expectation, given the observed counts, for the probability of being in a cell with observed count n_i is thus (n_i + priorn/N) / (N + priorn). The synthetic data are generated from a multinomial distribution with parameters given by these probabilities.

Unlike syn.satcat, which fits saturated conditional models, the synthesised data can include any combination of variables, except those defined by the combinations of variables in structzero.

NOTE that when the function is called by setting elements of method in syn() to "catall", the parameters priorn, structzero, maxtable, epsilon, and rand must be supplied to syn as e.g. catall.priorn.

Value

A list with two components:

Examples

ods <- SD2011[, c(1, 4, 5, 6, 2, 10, 11)]
table(ods[, c("placesize", "region")])

# Each `placesize_region` sublist: 
# for each relevant level of `placesize` defined in the first element, 
# the second element defines regions (variable `region`) that do not 
# have places of that size.

struct.zero <- list(
  placesize_region = list(placesize = "URBAN 500,000 AND OVER", 
                          region = c(2, 4, 5, 8:13, 16)),
  placesize_region = list(placesize = "URBAN 200,000-500,000", 
                          region = c(3, 4, 10:11, 13)),
  placesize_region = list(placesize = "URBAN 20,000-100,000", 
                          region = c(1, 3, 5, 6, 8, 9, 14:15)))
                          
# you could use  the object struct.zero in the command below 
# byt devtools checking did not like it so have added the list instead

syncatall <- syn(ods, method = c(rep("catall", 4), "ctree", "normrank", "ctree"),
                 catall.priorn = 2, catall.structzero = list(
  placesize_region = list(placesize = "URBAN 500,000 AND OVER", 
                          region = c(2, 4, 5, 8:13, 16)),
  placesize_region = list(placesize = "URBAN 200,000-500,000", 
                          region = c(3, 4, 10:11, 13)),
  placesize_region = list(placesize = "URBAN 20,000-100,000", 
                          region = c(1, 3, 5, 6, 8, 9, 14:15))))

Synthesis with classification and regression trees (CART)

Description

Generates univariate synthetic data using classification and regression trees (without or with bootstrap).

Usage

syn.ctree(y, x, xp, smoothing = "", proper = FALSE, 
          minbucket = 5, mincriterion = 0.9, ...)
syn.cart(y, x, xp, smoothing = "", proper = FALSE, 
         minbucket = 5, cp = 1e-08, ...)  

Arguments

Details

The procedure for synthesis by a CART model is as follows:

1. Fit a classification or regression tree by binary recursive partitioning.

2. For each xp find the terminal node.

3. Randomly draw a donor from the members of the node and take the observed value of y from that draw as the synthetic value.

syn.ctree uses ctree function from the party package and syn.cart uses rpart function from the rpart package. They differ, among others, in a selection of a splitting variable and a stopping rule for the splitting process.

A Guassian kernel smoothing can be applied to continuous variables by setting smoothing parameter to "density". It is recommended as a tool to decrease the disclosure risk. Increasing minbucket is another means of data protection.

CART models were suggested for generation of synthetic data by Reiter (2005) and then evaluated by Drechsler and Reiter (2011).

Value

A list with two components:

References

Reiter, J.P. (2005). Using CART to generate partially synthetic, public use microdata. Journal of Official Statistics, 21(3), 441–462.

Drechsler, J. and Reiter, J.P. (2011). An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets. Computational Statistics and Data Analysis, 55(12), 3232–3243.

See Also

syn, syn.survctree, rpart, ctree, syn.smooth

Synthesis of a group of categorical variables by iterative proportional fitting

Description

A fit to the table is obtained from the log-linear fit that matches the numbers in the margins specified by the margin parameters.

Usage

syn.ipf(x, k, proper = FALSE, priorn = 1, structzero = NULL, 
        gmargins = "twoway", othmargins = NULL, tol = 1e-3,
        max.its = 5000, maxtable = 1e8, print.its = FALSE,
        epsilon = 0, delta = 0.05, 
        noisetype = "Laplace", ...)

Arguments

Details

When used in syn function the group of variables with method = "ipf" must all be together at the start of the visit sequence. This function is designed for categorical variables, but it can also be used for numerical variables if they are categorised by specifying them in the numtocat parameter of the main function syn. Subsequent variables in visit.sequence are then synthesised conditional on the synthesised values of the grouped variables. A fit to the table is obtained from the log-linear fit that matches the numbers in the margins specified by the margin parameters. Prior probabilities for the proportions in each cell of the table are given by a Dirichlet distribution with the same parameter for every cell in the table that is not a structural zero. The sum of these parameters is priorn. The default priorn = 1 can be thought of as equivalent to the knowledge that 1 observation would be equally likely to fall in any cell of the table. The synthetic data are generated from a multinomial distribution with parameters given by the expected posterior probabilities for each cell of the table. If the maximum likelihood estimate from the log-linear fit to cell c_i is p_i and the table has N cells that are not structural zeros then the expectation of the posterior probability for this cell is (p_i + priorn/N^2) / (1 + priorn / N^2) or equivalently (N * p_i + priorn/N) / (N + priorn / N).

Unlike syn.satcat, which fits saturated models from their conditional distributions, x can include any combination of variables, including those not present in the original data, except those defined by structzero.

NOTE that when the function is called by setting elements of method in syn to "ipf", the parameters priorn, structzero, gmargins, othmargins, tol, max.its, maxtable, print.its and epsilon, must be supplied to syn as e.g. ipf.priorn.

Value

A list with two components:

Examples

ods <- SD2011[, c(1, 4, 5, 6, 2, 10, 11)]
table(ods[, c("placesize", "region")])

# Each `placesize_region` sublist: 
# for each relevant level of `placesize` defined in the first element, 
# the second element defines regions (variable `region`) that do not 
# have places of that size.

struct.zero <- list(
  placesize_region = list(placesize = "URBAN 500,000 AND OVER", 
                          region = c(2, 4, 5, 8:13, 16)),
  placesize_region = list(placesize = "URBAN 200,000-500,000", 
                          region = c(3, 4, 10:11, 13)),
  placesize_region = list(placesize = "URBAN 20,000-100,000", 
                          region = c(1, 3, 5, 6, 8, 9, 14:15)))
                          
# you could use  the object struct.zero in the command below 
# byt devtools checking did not like it so have added the list instead

synipf <- syn(ods, method = c(rep("ipf", 4), "ctree", "normrank", "ctree"), 
              ipf.gmargins = "twoway", ipf.othmargins = list(c(1, 2, 3)),
              ipf.priorn = 2, ipf.structzero = list(
  placesize_region = list(placesize = "URBAN 500,000 AND OVER", 
                          region = c(2, 4, 5, 8:13, 16)),
  placesize_region = list(placesize = "URBAN 200,000-500,000", 
                          region = c(3, 4, 10:11, 13)),
  placesize_region = list(placesize = "URBAN 20,000-100,000", 
                          region = c(1, 3, 5, 6, 8, 9, 14:15))))

Synthesis by linear regression after transformation of a dependent variable

Description

Generates univariate synthetic data using linear regression of an outcome variable transformed by natural logarithm (lognorm), square root (sqrtnorm) or cube root (cubertnorm).

Usage

syn.lognorm(y, x, xp, proper = FALSE, ...) 
syn.sqrtnorm(y, x, xp, proper = FALSE, ...)
syn.cubertnorm(y, x, xp, proper = FALSE, ...)

Arguments

Details

Generates synthetic values using the spread around the fitted linear regression line of transformed y given x. For proper synthesis first the regression coefficients are drawn from normal distribution with mean and variance from the fitted model. The synthetic values are transformed back to the original scale.

Value

A list with two components:

See Also

syn, syn.norm, syn.normrank

Synthesis by logistic regression

Description

Generates univariate synthetic data for binary or binomial response variable using logistic regression model.

Usage

syn.logreg(y, x, xp, denom = NULL, denomp = NULL, proper = FALSE, ...) 

Arguments

Details

Synthesis for binary response variables by the non-Bayesian or approximate Bayesian logistic regression model. The non-Bayesian method consists of the following steps:

1. Fit a logistic regression to the original data.

2. Calculate predicted inverse logits for synthesied covariates.

3. Compare the inverse logits to a random (0,1) deviate and get synthetic values.

The Bayesian version (for proper synthesis) includes additional step before computing inverse logits, namely drawing coefficients from normal distribution with mean and variance estimated in step 1.

The method relies on the standard glm.fit function. Warnings from glm.fit are suppressed. Perfect prediction is handled by the data augmentation method.

Value

A list with two components:

See Also

syn, glm

Synthesis for a variable nested within another variable.

Description

Synthesizes one variable (y) from another one (x) when y is nested in the categories of x. A bootstrap sample is created from the original values of y within each category of xp (the synthesised values of the grouping variable).

Usage

syn.nested(y, x, xp, smoothing = "", cont.na = NA,  ...) 

Arguments

Details

An example would be when x is a classification of occupations and y is a more detailed sub-classification. It is intended that x is a categorical (factor) variable. A warning will be issued if the original y is not nested within x. A variable synthesised by syn.nested() is automatically excluded from predicting later variables because it will provide no extra information, given its grouping variable. syn.nested() is also used for the final synthesis of variables in syn() when the option numtocat is used to synthesise numerical variables as groups.

Value

A list with two components:

Synthesis by linear regression

Description

Generates univariate synthetic data using linear regression analysis.

Usage

syn.norm(y, x, xp, proper = FALSE, ...) 

Arguments

Details

Generates synthetic values using the spread around the fitted linear regression line of y given x. For proper synthesis first the regression coefficients are drawn from normal distribution with mean and variance from the fitted model.

Value

A list with two components:

See Also

syn, syn.normrank, syn.lognorm

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

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:

See Also

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

Passive synthesis

Description

Derives a new variable according to a specified function of synthesised data.

Usage

syn.passive(data, func)

Arguments

Details

Any function of the synthesised data can be specified. Note that several operators such as +, -, * and ^ have different meanings in formula syntax. Use the identity function I() if they should be interpreted as arithmetic operators, e.g. "~I(age^2)". Function syn() checks whether the passive assignment is correct in the original data and fails with a warning if this is not true. The variables synthesised passively can be used to predict later variables in the synthesis except when they are numeric variables with missing data. A warning is produced in this last case.

Value

A list with two components:

Author(s)

Gillian Raab, 2021 based on Stef van Buuren, Karin Groothuis-Oudshoorn, 2000

References

Van Buuren, S. and Groothuis-Oudshoorn, K. (2011). mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), 1-67. doi:10.18637/jss.v045.i03

See Also

syn

Examples

### the examples shows how inconsistencies in the SD2011 data are picked up 
### by syn.passive()
ods <- SD2011[, c("height", "weight", "bmi", "age", "agegr")]
ods$hsq <- ods$height^2
ods$sex <- SD2011$sex
meth <- c("cart", "cart", "~I(weight / height^2 * 10000)",  
          "cart", "~I(cut(age, c(15, 24, 34, 44, 59, 64, 120)))", 
          "~I(height^2)", "logreg")

## Not run: 
### fails for bmi 
s1 <- syn(ods, method = meth, seed = 6756, models = TRUE)

### fails for agegr
ods$bmi <- ods$weight / ods$height^2 * 10000  
s2 <- syn(ods, method = meth, seed = 6756, models = TRUE) 

### fails because of wrong order 
ods$agegr <- cut(ods$age, c(15, 24, 34, 44, 59, 64, 120))
s3 <- syn(ods, method = meth, visit.sequence = 7:1, 
          seed = 6756, models = TRUE)  

## End(Not run)

### runs without errors
ods$bmi   <- ods$weight / ods$height^2 * 10000  
ods$agegr <- cut(ods$age, c(15, 24, 34, 44, 59, 64, 120))
s4 <- syn(ods, method = meth, seed = 6756, models = TRUE) 
### bmi and hsq do not predict sex because of missing values
s4$models$sex 

### hsq with no missing values used to predict sex  
ods2 <- ods[!is.na(ods$height),]
s5 <- syn(ods2, method = meth, seed = 6756, models = TRUE) 
s5$models$sex

### agegr with missing values used to predict sex because not numeric
ods3 <- ods
ods3$age[1:4] <- NA
ods3$agegr <- cut(ods3$age, c(15, 24, 34, 44, 59, 64, 120))
s6 <- syn(ods3, method = meth, seed = 6756, models = TRUE) 
s6$models$sex  

Synthesis by predictive mean matching

Description

Generates univariate synthetic data using predictive mean matching.

Usage

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

Arguments

Details

Synthesis of y by predictive mean matching. The procedure is as follows:

1. Fit a linear regression to the original data.

2. Compute predicted values y.hat and ysyn.hat for the original x and synthesised xp covariates respectively.

3. For each predicted value ysyn.hat find donor observations with the closest predicted values y.hat (ties are broken by random selection), randomly sample one of them and take its observed value y as the synthetic value.

The Bayesian version (for proper synthesis) includes additional step before computing predicted values:

• Draw coefficients from normal distribution with mean and variance estimated in step 1 and use them to calculate predicted values for the synthesised covariates.

Value

A list with two components:

See Also

syn, syn.smooth

Synthesis by ordered polytomous regression

Description

Generates a synthetic categorical variable using ordered polytomous regression (without or with bootstrap).

Usage

syn.polr(y, x, xp, proper = FALSE, maxit = 1000, trace = FALSE, 
  MaxNWts = 10000, ...)

Arguments

Details

Generates synthetic ordered categorical variables by the proportional odds logistic regression (polr) model. The function repeatedly applies logistic regression on the successive splits. The model is also known as the cumulative link model.

The algorithm of syn.polr uses the function polr from the MASS package.

In order to avoid bias due to perfect prediction, the data are augmented by the method of White, Daniel and Royston (2010).

In case the call to polr fails, usually because the data are very sparse, multinom function is used instead.

Value

A list with two components:

References

White, I.R., Daniel, R. and Royston, P. (2010). Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables. Computational Statistics and Data Analysis, 54, 2267–2275.

See Also

syn,syn.polyreg multinom, polr

Synthesis by unordered polytomous regression

Description

Generates a synthetic categorical variable using unordered polytomous regression (without or with bootstrap).

Usage

syn.polyreg(y, x, xp, proper = FALSE, maxit = 1000, trace = FALSE, 
            MaxNWts = 10000, ...)

Arguments

Details

Generates synthetic categorical variables by the polytomous regression model. The method consists of the following steps:

1. Fit categorical response as a multinomial model.

2. Compute predicted categories.

3. Add appropriate noise to predictions.

The algorithm of syn.polyreg uses the function multinom from the nnet package. Any numerical variables are scaled to cover the range (0,1) before fitting. Warnings are printed if the algorithm fails to converge in maxit iterations and also if the synthesised data has only one category. The latter may occur if the variable being synthesised is sparse so that the algorithm fails to iterate.

In order to avoid bias due to perfect prediction, the data are augmented by the method of White, Daniel and Royston (2010).

NOTE that when the function is called by setting elements of method in syn() to "polyreg", the parameters maxit, trace and MaxNWts can be supplied to syn() as e.g. polyreg.maxit.

Value

A list with two components:

References

White, I.R., Daniel, R. and Royston, P. (2010). Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables. Computational Statistics and Data Analysis, 54, 2267–2275.

See Also

syn, syn.polr, multinom, polr

Synthesis with a fast implementation of random forests

Description

Generates univariate synthetic data using a fast implementation of random forests. It uses ranger function from the ranger package.

Usage

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

Arguments

Details

...

Value

A list with two components:

References

...

See Also

syn, syn.rf, syn.bag, syn.cart, ranger, syn.smooth

Synthesis with random forest

Description

Generates univariate synthetic data using Breiman's random forest algorithm classification and regression. It uses randomForest function from the randomForest package.

Usage

syn.rf(y, x, xp, smoothing = "", proper = FALSE, ntree = 10, ...)

Arguments

Details

...

Value

A list with two components:

References

...

See Also

syn, syn.rf, syn.bag, syn.cart, randomForest, syn.smooth

Synthesis by simple random sampling

Description

Generates a random sample from the observed data.

Usage

syn.sample(y, xp, smoothing = "", cont.na = NA, proper = FALSE, ...)

Arguments

Details

A simple random sample with replacement is taken from the observed values in y and used as synthetic values. A Guassian kernel smoothing can be applied to continuous variables by setting smoothing parameter to "density". It is recommended as a tool to decrease the disclosure risk.

Value

A list with two components:

See Also

syn, syn.smooth

Synthesis from a saturated model based on all combinations of the predictor variables.

Description

Synthesises one variable (y) from all possible combinations of its predictors (x). A bootstrap sample is created from the original values of y within each unique combinations of of xp (the synthesisied values of the grouping variable). Note that only combinations of predictor variable levels that appear in the original data can be in the synthetic data. The related method (syn.catall) overcomes this by adding a small prior probability to all zero cells in the cross tabulation from the original data that are not structural zeros. But it has the limitation of requiring a complete cross tabulation of all the variables.

Usage

syn.satcat(y, x, xp, proper = FALSE, ...)

Arguments

Details

It is intended that the variables in x are categorical (factor) variables. If y is also a categorical variable syn.satcat will give the same results as fitting a saturated polychotomous regression model but will usually be much faster. syn.satcat will fail with an error message if previous syntheses have generated a combination of variables in xp that was not present in x. Use of the syn.catall method for grouped variables can overcome this.

Value

A list with two components:

Examples

ods <- SD2011[, c("region", "sex", "agegr", "placesize")]

s1 <- syn(ods, method =  "satcat", seed = 7856)
s2 <- syn(ods, method = c("sample", "cart", "satcat", "cart"), seed = 7856)

## Not run: 
### mostly fails because previous synthesis has produced 
### combinations not found in the original data
s3 <- syn(ods, method = c("sample", "cart", "cart", "satcat"), seed = 7856)
## End(Not run)

syn.smooth

Description

Implements three different smoothing methods for numeric data.

Usage

syn.smooth(ysyn, yobs = NULL, smoothing = "spline", window = 5, ...)

Arguments

Details

Smooths numeric variables by three methods. Default is "spline" that uses a smoothing spline, others are "density" that uses a Gaussian kernel density estimator with bandwidth selected using the Sheather-Jones 'solve-the-equation' method (see bw.SJ) and "rmean" that smooths with a running mean of width "window" (see runningmean).

Value

A vector of smoothed values of ysyn.

See Also

syn, syn.sample, syn.normrank, syn.pmm, syn.ctree, syn.cart, syn.bag, syn.rf, syn.ranger, syn.nested

Synthesis of survival time by classification and regression trees (CART)

Description

Generates synthetic event indicator and time to event data using classification and regression trees (without or with bootstrap).

Usage

syn.survctree(y, yevent, x, xp, proper = FALSE, minbucket = 5, ...)

Arguments

Details

The procedure for synthesis by a CART model is as follows:

1. Fit a tree-structured survival model by binary recursive partitioning (the terminal nodes include Kaplan-Meier estimates of the survival time).

2. For each xp find the terminal node.

3. Randomly draw a donor from the members of the node and take the observed value of yevent and y from that draw as the synthetic values.

The function is used in syn() to generate survival times by setting elements of method in syn() to "survctree". Additional parameters related to ctree function, e.g. minbucket can be supplied to syn() as survctree.minbucket.

Where the survival variable is censored this information must be supplied to syn() as a named list (event) that gives the name of the variable for each event indicator. Event variables can be a numeric variable with values 1/0 (1 = event), TRUE/FALSE (TRUE = event) or a factor with 2 levels (level 2 = event). The event variable(s) will be synthesised along with the survival time(s).

Value

A list with the following components:

See Also

syn, syn.ctree

Examples

### This example uses the data set 'mgus2' from the survival package.
### It has a follow-up time variable 'futime' and an event indicator 'death'.
library(survival)

### first exclude the 'id' variable and run a dummy synthesis to get 
### a method vector
ods <- mgus2[-1]
s0 <- syn(ods)

### create new method vector including 'survctree' for 'futime' and create 
### an event list for it; the names of the list element must correspond to 
### the name of the follow-up variable for which the event indicator
### need to be specified.
meth <- s0$method
meth[names(meth) == "futime"] <- "survctree"
evlist <- list(futime = "death")

s1 <- syn(ods, method = meth, event = evlist)

### evaluate outputs
## compare selected variables
compare(s1, ods, vars = c("futime", "death", "sex", "creat"))

## compare original and synthetic follow up time by an event indicator
multi.compare(s1, ods, var = "futime", by = "death")

## compare survival curves for original and synthetic data
par(mfrow = c(2,1))
plot(survfit(Surv(futime, death) ~ sex, data = ods), 
     col = 1:2, xlim = c(0,450), main = "Original data")
legend("topright", levels(ods$sex), col = 1:2, lwd = 1, bty = "n")
plot(survfit(Surv(futime, death) ~ sex, data = s1$syn), 
     col = 1:2, xlim = c(0,450), main = "Synthetic data")

check synthetic and original if not produced by synthpop.

Description

Check, and attempt to adjust, synthetic datasets NOT created by syn() if not compatible with the original. The output is a list with 4 components, the first two giving adjusted versions of the input synthetic data and original and the third needsfix an indicator of whether the functions listed below are likely to run correctly on the adjusted data, the fourth unchanged indicates whether any changes have been made to the original or the synthetic data.

Variables that are in both the synthetic and the original data are checked to 1) convert any character variables to R factors 2) check that data types match 3) check differences in whether variables have missing values 4) check if the levels of factors agree and if not use the combination of both sets.

needsfix becomes TRUE if 1) some variables are in the synthetic but not in the original 2) variables have different classes (after characters converted to factor) 3) there are missing values in the synthetic data but not in the original 4) some levels of a factor are in the synthetic data but not in the original. Some warning messages are printed if level differences are not just due to missing values.

The function can be run to compare original and synthetic or it can be called by setting compare.synorig = TRUE when data are not supplied as an object of class synds created by synthpop in the following functions : utility.tab() utility.tables() compare() disclosure() disclosure.summary(). In this case the function attempts to correct differences and continue or, if this is impossible, will prompt the user as to which variables need changing.

Usage

synorig.compare(syn,orig, print.flag = TRUE)

Arguments

Details

Error messages explain briefly what adjustments have been made to the data sets, what could not be fixed and what might need to be checked. Both orig and syn are made into simple data frames for comparison (e.g. if tibbles or matrices)

Value

A list with 3 components

References

to add

See Also

utility.gen utility.tab utility.tables compare.synds disclosure.synds

Examples

library(synthpop)
orig <- SD2011[1:2000,]
pretendsyn <- SD2011[2001:5000, 1:5]
orig[,1] <- as.character(orig[,1])
codebook.syn(orig[,1:5])
newdata <- synorig.compare(pretendsyn, orig)
codebook.syn(newdata$orig[,1:5])

Distributional comparison of synthesised and observed data

Description

Distributional comparison of synthesised data set with the original (observed) data set using propensity scores.

This function can be also used with synthetic data NOT created by syn(), but then additional parameters not.synthesised and cont.na might need to be provided.

Usage

## S3 method for class 'synds'
utility.gen(object, data, 
            method = "cart", maxorder = 1, k.syn = FALSE, tree.method = "rpart",
            max.params = 400, print.stats = c("pMSE", "S_pMSE"), resamp.method = NULL, 
            nperms = 50, cp = 1e-3, minbucket = 5, mincriterion = 0, vars = NULL, 
            aggregate = FALSE, maxit = 200, ngroups = NULL, print.flag = TRUE,
            print.every = 10, digits = 6, print.zscores = FALSE, zthresh = 1.6,
            print.ind.results = FALSE, print.variable.importance = FALSE, ...)

## S3 method for class 'data.frame'
utility.gen(object, data, not.synthesised = NULL, cont.na = NULL, 
            method = "cart", maxorder = 1, k.syn = FALSE, tree.method = "rpart",
            max.params = 400, print.stats = c("pMSE", "S_pMSE"), resamp.method = NULL, 
            nperms = 50, cp = 1e-3, minbucket = 5, mincriterion = 0, vars = NULL, 
            aggregate = FALSE, maxit = 200, ngroups = NULL, print.flag = TRUE,
            print.every = 10, digits = 6, print.zscores = FALSE, zthresh = 1.6,
            print.ind.results = FALSE, print.variable.importance = FALSE, ...)

## S3 method for class 'list'
utility.gen(object, data, not.synthesised = NULL, cont.na = NULL, 
            method = "cart", maxorder = 1, k.syn = FALSE, tree.method = "rpart",
            max.params = 400, print.stats = c("pMSE", "S_pMSE"), resamp.method = NULL, 
            nperms = 50, cp = 1e-3, minbucket = 5, mincriterion = 0, vars = NULL, 
            aggregate = FALSE, maxit = 200, ngroups = NULL, print.flag = TRUE,
            print.every = 10, digits = 6, print.zscores = FALSE, zthresh = 1.6,
            print.ind.results = FALSE, print.variable.importance = FALSE, ...)


## S3 method for class 'utility.gen'
print(x, digits = NULL, zthresh = NULL, 
               print.zscores = NULL, print.stats = NULL,
               print.ind.results = NULL, print.variable.importance = NULL, ...)

Arguments

Details

This function follows the method for evaluating the utility of masked data as given in Snoke et al. (2018) and originally proposed by Woo et al. (2009). The original and synthetic data are combined into one dataset and propensity scores, as detailed in Rosenbaum and Rubin (1983), are calculated to estimate the probability of membership in the synthetic data set. The utility measure is based on the mean squared difference between these probabilities and the probability expected if the data did not distinguish the synthetic data from the original.

If k.syn = FALSE the expected probability is just the proportion of synthetic data in the combined data set, 0.5 when the original and synthetic data have the same number of records. Setting k.syn = TRUE indicates that the numbers of observations in the synthetic data was synthesised and not fixed by the synthesiser. In this case the expected probability will be 0.5 in all cases and the model to discriminate between observed and synthetic will include an intercept term. This will usually only apply when the standalone version of this function utility.gen.sa() is used.

Propensity scores can be modeled by logistic regression method = "logit" or by two different implementations of classification and regression trees as method "cart". For logistic regression the predictors are all variables in the data and their interactions up to order maxorder. The default of 1 gives all main effects and first order interactions. For logistic regression the null distribution of the propensity score is derived and is used to calculate ratios and standardised values.

For method = "cart" the expectation and variance of the null distribution is calculated from a permutation test. Our recent work indicates that this method can sometimes give misleading results.

If missing values exist, indicator variables are added and included in the model as recommended by Rosenbaum and Rubin (1984). For categorical variables, NA is treated as a new category.

Value

An object of class utility.gen which is a list including the utility measures their expected null values for each synthetic set with the following components:

References

Woo, M-J., Reiter, J.P., Oganian, A. and Karr, A.F. (2009). Global measures of data utility for microdata masked for disclosure limitation. Journal of Privacy and Confidentiality, 1(1), 111-124.

Rosenbaum, P.R. and Rubin, D.B. (1984). Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association, 79(387), 516-524.

Snoke, J., Raab, G.M., Nowok, B., Dibben, C. and Slavkovic, A. (2018). General and specific utility measures for synthetic data. Journal of the Royal Statistical Society: Series A, 181, Part 3, 663-688.

See Also

utility.tab

Examples

## Not run: 
  ods <- SD2011[1:1000, c("age", "bmi", "depress", "alcabuse", "nofriend")]
  s1 <- syn(ods, m = 5, method = "parametric", 
            cont.na = list(nofriend = -8))
    
  ### synthetic data provided as a 'synds' object   
  u1 <- utility.gen(s1, ods)
  print(u1, print.zscores = TRUE, zthresh = 1, digits = 6)
  u2 <- utility.gen(s1, ods, ngroups = 3, print.flag = FALSE)
  print(u2, print.zscores = TRUE)
  u3 <- utility.gen(s1, ods, method = "cart", nperms = 20)
  print(u3, print.variable.importance = TRUE)
    
  ### synthetic data provided as 'list'
  utility.gen(s1$syn, ods, cont.na = list(nofriend = -8))  
  
## End(Not run)

Tabular utility

Description

Produces tables from observed and synthesised data and calculates utility measures to compare them with their expectation if the synthesising model is correct.

It can be also used with synthetic data NOT created by syn(), but then an additional parameter cont.na might need to be provided.

Usage

## S3 method for class 'synds'
utility.tab(object, data, vars = NULL, ngroups = 5,
            useNA = TRUE, max.table = 1e6,
            print.tables = length(vars) < 4,
            print.stats = c("pMSE", "S_pMSE", "df"),
            print.zdiff = FALSE, print.flag = TRUE,
            digits = 4, k.syn = FALSE,  ...)

## S3 method for class 'data.frame'
utility.tab(object, data, vars = NULL, cont.na = NULL,
            ngroups = 5, useNA = TRUE, max.table = 1e6,
            print.tables = length(vars) < 4,
            print.stats = c("pMSE", "S_pMSE", "df"),
            print.zdiff = FALSE, print.flag = TRUE,
            digits = 4, k.syn = FALSE, 
            compare.synorig  = TRUE, ...)

## S3 method for class 'list'
utility.tab(object, data, vars = NULL, cont.na = NULL,
            ngroups = 5, useNA = TRUE, max.table = 1e6,
            print.tables = length(vars) < 4,
            print.stats = c("pMSE", "S_pMSE", "df"),
            print.zdiff = FALSE, print.flag = TRUE,
            digits = 4, k.syn = FALSE, 
            compare.synorig = TRUE, ...)


## S3 method for class 'utility.tab'
print(x, print.tables = NULL,
      print.zdiff = NULL, print.stats = NULL,
      digits = NULL, ...)

Arguments

Details

Forms tables of observed and synthesised values for the variables specified in vars. Several utility measures are calculated from the cells of the tables, as described below. Details of all of these measures can be found in Raab et al. (2021). If the synthesising model is correct the measures VW, FT, G and JSD should have chi-square distributions with df degrees of freedom for large samples. Standardised versions of each measure are available (e.g. S_VW for VW, where S_VW = VW/df) that will have an expected value of 1 if the synthesising model is correct. Four other measures are calculated by considering the table as a prediction model. The propensity score mean-squared error pMSE, and from a comparison of propensity scores for the synthetic and original data the Kolmogorov-Smirnov statistic SPECKS and the Wilcoxon rank-sum statistic U and also the percentage of the observations correctly predicted in the combined tables over 50%(PO50) where the majority of observations in each grouping are in agreement with category (real or synthetic) of the observation. The first of these pMSE is identical except for a constant to VW. No expected values are computed for the last three of these measures, but they can be obtained by replication from utility.gen(). Three further measures are calculated from the tables. The mean absolute difference in distributions: firstly MabsDD, the avarage absolute difference in the proportions of original and synthetic data from all the cells in the table. Secondly a weighted version of this measure WMabsDD where the weights are proportional to the inverse of the variance of the absolute differences so that this measure can be standardised by its expected value, df. Finally the Bhattacharyya distances BhattD derived from the overlap of the histograms of the original and synthetic data sets.

Value

An object of class utility.tab which is a list with the following components:

References

Nowok, B., Raab, G.M and Dibben, C. (2016). synthpop: Bespoke creation of synthetic data in R. Journal of Statistical Software, 74(11), 1-26. doi:10.18637/jss.v074.i11.

Raab, G.M., Nowok, B. and Dibben, C. (2021). Assessing, visualizing and improving the utility of synthetic data. Available from https://arxiv.org/abs/2109.12717.

Read, T.R.C. and Cressie, N.A.C. (1988) Goodness–of–Fit Statistics for Discrete Multivariate Data, Springer–Verlag, New York.

Voas, D. and Williamson, P. (2001) Evaluating goodness-of-fit measures for synthetic microdata. Geographical and Environmental Modelling, 5(2), 177-200.

See Also

utility.gen

Examples

ods <- SD2011[1:1000, c("sex", "age", "marital", "nofriend")]

s1 <- syn(ods, m = 10, cont.na = list(nofriend = -8))
utility.tab(s1, ods, vars = c("marital", "sex"), print.stats = "all")

s2 <- syn(ods, m = 1, cont.na = list(nofriend = -8))
u2 <- utility.tab(s2, ods, vars = c("marital", "age", "sex"), ngroups = 3)
print(u2, print.tables = TRUE, print.zdiff = TRUE)

### synthetic data provided as 'data.frame'
utility.tab(s2$syn, ods, vars = c("marital", "nofriend"), ngroups = 3,
            print.tables = TRUE, cont.na = list(nofriend = -8), digits = 4)

Tables and plots of utility measures

Description

Calculates and plots tables of utility measures. The calculations of utility measures are done by the function utility.tab. Options are all one-way tables, all two-way tables or three-way tables for a specified third variable along with pairs of all other variables.

This function can be also used with synthetic data NOT created by syn(), but then an additional parameters not.synthesised and cont.na might need to be provided.

Usage

## S3 method for class 'synds'
utility.tables(object, data,
               tables = "twoway", maxtables = 5e4,
               vars = NULL, third.var = NULL,
               useNA = TRUE, ngroups = 5,
               tab.stats = c("pMSE", "S_pMSE", "df"), 
               plot.stat = "S_pMSE", plot = TRUE, max.table = 1e07,
               print.tabs = FALSE, digits.tabs = 4,
               max.scale = NULL, min.scale = 0, plot.title = NULL,
               nworst = 5, ntabstoprint = 0, k.syn = FALSE, 
               low = "grey92", high = "#E41A1C",
               n.breaks = NULL, breaks = NULL, print.flag = TRUE, ...)
               
## S3 method for class 'data.frame'
utility.tables(object, data, 
               cont.na = NULL, not.synthesised = NULL, 
               tables = "twoway", maxtables = 5e4,
               vars = NULL, third.var = NULL, 
               useNA = TRUE, ngroups = 5, 
               tab.stats = c("pMSE", "S_pMSE", "df"), 
               plot.stat = "S_pMSE", plot = TRUE, max.table = 1e07, 
               print.tabs = FALSE, digits.tabs = 4,
               max.scale = NULL, min.scale = 0, plot.title = NULL,  
               nworst = 5, ntabstoprint = 0, k.syn = FALSE,
               low = "grey92", high = "#E41A1C",
               n.breaks = NULL, breaks = NULL, 
               compare.synorig = TRUE, print.flag = TRUE,...)

## S3 method for class 'list'
utility.tables(object, data,
               cont.na = NULL, not.synthesised = NULL, 
               tables = "twoway", maxtables = 5e4, 
               vars = NULL, third.var = NULL, 
               useNA = TRUE, ngroups = 5, 
               tab.stats = c("pMSE", "S_pMSE", "df"), 
               plot.stat = "S_pMSE", plot = TRUE,  max.table = 1e07,
               print.tabs = FALSE, digits.tabs = 4,
               max.scale = NULL, min.scale = 0, plot.title = NULL,  
               nworst = 5, ntabstoprint = 0, k.syn = FALSE,
               low = "grey92", high = "#E41A1C",
               n.breaks = NULL, breaks = NULL, 
               compare.synorig = TRUE, print.flag = TRUE,...)

## S3 method for class 'utility.tables'
print(x, print.tabs = NULL, digits.tabs = NULL, 
      plot = NULL, plot.title = NULL, max.scale = NULL, min.scale = NULL, 
      nworst = NULL, ntabstoprint = NULL, ...)

Arguments

.

Details

Calculates tables of observed and synthesised values for the variables specified in vars with the function utility.tab and produces tables and plots of one-way, two-way or three-way utility measures formed from vars. Several options for utility measures can be selected for printing or plotting. Details are in help file for utility.tab.

The tables and variables with the worst utility scores are identified. Visualisations of the matrices of utility scores are plotted. For threeway tables a third variable can be defined to select all tables involving that variable for plotting. If it is not specified the variable with tables giving the worst utility is selected as the third variable.

Value

An object of class utility.tab which is a list with the following components:

References

Read, T.R.C. and Cressie, N.A.C. (1988) Goodness–of–Fit Statistics for Discrete Multivariate Data, Springer–Verlag, New York.

Voas, D. and Williamson, P. (2001) Evaluating goodness-of-fit measures for synthetic microdata. Geographical and Environmental Modelling, 5(2), 177-200.

See Also

utility.tab

Examples

ods <- SD2011[1:1000, c("sex", "age", "edu", "marital", "region", "income")]
s1 <- syn(ods)

### synthetic data provided as a 'synds' object  
(t1 <- utility.tables(s1, ods, tab.stats = "all", print.tabs = TRUE))
### synthetic data provided as a 'data.frame' object
(t1 <- utility.tables(s1$syn, ods, tab.stats = "all", print.tabs = TRUE))

t2 <- utility.tables(s1, ods, tables = "twoway")
print(t2, max.scale = 3)

(t3 <- utility.tables(s1, ods, tab.stats = "all", tables = "threeway", 
                      third.var = "sex", print.tabs = TRUE))

(t4 <- utility.tables(s1, ods, tab.stats = "all", tables = "threeway", 
                      third.var = "sex", useNA = FALSE, print.tabs = TRUE))

(t5 <- utility.tables(s1, ods,  tab.stats = "all", 
                      print.tabs = TRUE))

Exporting synthetic data sets to external files

Description

Exports synthetic data set(s) from synthesised data set (synds) object to external files of selected format. Currently supported file formats include: SPSS, Stata, SAS, csv, tab, rda, RData and txt. For SPSS, Stata and SAS it uses functions from the foreign package with some adjustments where necessary. Information about the synthesis is written into a separate text file.

NOTE: Currently numeric codes and labels can be preserved correctly only for SPSS files imported into R using read.obs function.

Usage

write.syn(object, filename, 
filetype = c("csv", "tab", "txt",
                    "SPSS", "Stata", "SAS", "rda", "RData"), 
convert.factors = "numeric", data.labels = NULL, save.complete = TRUE, 
extended.info = TRUE, ...)

Arguments

Value

File(s) with synthesised data set(s) and a text file with information about synthesis are produced. Optionally a complete synthesised data set object is saved into synobject_filename.RData file.

See Also

read.obs