Class to save all information about the SDC process
createSdcObj(
dat,
keyVars,
numVars = NULL,
pramVars = NULL,
ghostVars = NULL,
weightVar = NULL,
hhId = NULL,
strataVar = NULL,
sensibleVar = NULL,
excludeVars = NULL,
options = NULL,
seed = NULL,
randomizeRecords = FALSE,
alpha = 1
)
undolast(object)
strataVar(object) <- value
# S4 method for class 'sdcMicroObj,characterOrNULL'
strataVar(object) <- valueThe microdata set. A numeric matrix or data frame containing the data.
Indices or names of categorical key variables. They must, of course, match with the columns of ‘dat’.
Index or names of continuous key variables.
Indices or names of categorical variables considered to be pramed.
if specified a list which each element being a list of exactly two elements.
The first element must be a character vector specifying exactly one variable name that was
also specified as a categorical key variable (keyVars), while the second element is
a character vector of valid variable names (that must not be listed as keyVars).
If localSuppression or kAnon was applied, the resulting
suppression pattern for each key-variable is transferred to the depending variables.
Indices or name determining the vector of sampling weights.
Index or name of the cluster ID (if available).
Indices or names of stratification variables.
Indices or names of sensible variables (for l-diversity)
which variables of dat should not be included in
result-object? Users may specify a vector of variable-names available in dat
that were not specified in either keyVars, numVars, pramVars,
ghostVars, hhId, strataVar or sensibleVar.
additional options (if specified, a list must be used as input)
(numeric) number specifiying the seed which will be set to allow for
reproducablity. The number will be rounded and saved as element seed in slot options.
(logical) if TRUE, the order of observations in the input microdata set
will be randomized.
numeric between 0 and 1 specifying the fraction on how much keys containing NAs should
contribute to the frequency calculation which is also crucial for risk-estimation.
a sdcMicroObj-class object
NULL or a character vector of length 1 specifying a valid variable name
a sdcMicroObj-class object
an object of class sdcMicroObj with modified slot @strataVar
Objects can be created by calls of the form
new("sdcMicroObj", ...).
Templ, M. and Meindl, B. and Kowarik, A.: Statistical Disclosure Control for Micro-Data Using the R Package sdcMicro, Journal of Statistical Software, 67 (4), 1–36, 2015. doi:10.18637/jss.v067.i04
## we can also specify ghost (linked) variables
## these variables are linked to some categorical key variables
## and have the sampe suppression pattern as the variable that they
## are linked to after \code{\link{localSuppression}} has been applied
data(testdata)
testdata$electcon2 <- testdata$electcon
testdata$electcon3 <- testdata$electcon
testdata$water2 <- testdata$water
keyVars <- c("urbrur","roof","walls","water","electcon","relat","sex")
numVars <- c("expend","income","savings")
w <- "sampling_weight"
## we want to make sure that some variables not used as key-variables
## have the same suppression pattern as variables that have been
## selected as key variables. Thus, we are using 'ghost'-variables.
ghostVars <- list()
## we want variables 'electcon2' and 'electcon3' to be linked
## to key-variable 'electcon'
ghostVars[[1]] <- list()
ghostVars[[1]][[1]] <- "electcon"
ghostVars[[1]][[2]] <- c("electcon2","electcon3")
# \donttest{
## donttest because Examples with CPU time > 2.5 times elapsed time
## we want variable 'water2' to be linked to key-variable 'water'
ghostVars[[2]] <- list()
ghostVars[[2]][[1]] <- "water"
ghostVars[[2]][[2]] <- "water2"
## create the sdcMicroObj
obj <- createSdcObj(testdata, keyVars=keyVars,
numVars=numVars, w=w, ghostVars=ghostVars)
## apply 3-anonymity to selected key variables
obj <- kAnon(obj, k=3); obj
#> The input dataset consists of 4580 rows and 18 variables.
#> --> Categorical key variables: urbrur, roof, walls, water, electcon, relat, sex
#> --> Numerical key variables: expend, income, savings
#> --> Weight variable: sampling_weight
#> --> Ghost variable(s) exist
#> Variable(s) electcon2, electcon3 are linked to key variable electcon
#> Variable(s) water2 are linked to key variable water
#> ----------------------------------------------------------------------
#>
#> Information on categorical key variables:
#>
#> Reported is the number, mean size and size of the smallest category >0 for recoded variables.
#> In parenthesis, the same statistics are shown for the unmodified data.
#> Note: NA (missings) are counted as seperate categories!
#>
#> Key Variable Number of categories Mean size
#> <char> <char> <char> <char> <char>
#> urbrur 2 (2) 2290.000 (2290.000)
#> roof 6 (5) 913.200 (916.000)
#> walls 4 (3) 1525.333 (1526.667)
#> water 9 (8) 567.500 (572.500)
#> electcon 4 (3) 1524.667 (1526.667)
#> relat 9 (9) 543.875 (508.889)
#> sex 2 (2) 2290.000 (2290.000)
#> Size of smallest (>0)
#> <char> <char>
#> 646 (646)
#> 15 (16)
#> 46 (50)
#> 25 (26)
#> 103 (107)
#> 2 (1)
#> 2284 (2284)
#> ----------------------------------------------------------------------
#>
#> Infos on 2/3-Anonymity:
#>
#> Number of observations violating
#> - 2-anonymity: 0 (0.000%) | in original data: 157 (3.428%)
#> - 3-anonymity: 0 (0.000%) | in original data: 281 (6.135%)
#> - 5-anonymity: 100 (2.183%) | in original data: 458 (10.000%)
#>
#> ----------------------------------------------------------------------
#>
#> Numerical key variables: expend, income, savings
#>
#> Disclosure risk (~100.00% in original data):
#> modified data: [0.00%; 100.00%]
#>
#> Current Information Loss in modified data (0.00% in original data):
#> IL1: 0.00
#> Difference of Eigenvalues: 0.000%
#> ----------------------------------------------------------------------
#>
#> Local suppression:
#> KeyVar | Suppressions (#) | Suppressions (%)
#> <char> <char> <int> <char> <char>
#> urbrur | 0 | 0.000
#> roof | 14 | 0.306
#> walls | 4 | 0.087
#> water | 40 | 0.873
#> electcon | 6 | 0.131
#> relat | 229 | 5.000
#> sex | 0 | 0.000
#> ----------------------------------------------------------------------
#>
## check, if the suppression patterns are identical
manipGhostVars <- get.sdcMicroObj(obj, "manipGhostVars")
manipKeyVars <- get.sdcMicroObj(obj, "manipKeyVars")
all(is.na(manipKeyVars$electcon) == is.na(manipGhostVars$electcon2))
#> [1] TRUE
all(is.na(manipKeyVars$electcon) == is.na(manipGhostVars$electcon3))
#> [1] TRUE
all(is.na(manipKeyVars$water) == is.na(manipGhostVars$water2))
#> [1] TRUE
## exclude some variables
obj <- createSdcObj(testdata, keyVars=c("urbrur","roof","walls"), numVars="savings",
weightVar=w, excludeVars=c("relat","electcon","hhcivil","ori_hid","expend"))
colnames(get.sdcMicroObj(obj, "origData"))
#> [1] "urbrur" "roof" "walls"
#> [4] "water" "sex" "age"
#> [7] "income" "savings" "sampling_weight"
#> [10] "household_weights" "electcon2" "electcon3"
#> [13] "water2"
# }