sdcMicroObj-class function

Class "sdcMicroObj"

Class "sdcMicroObj"

Class to save all information about the SDC process class

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 replacement method for signature 'sdcMicroObj,characterOrNULL'
strataVar(object) <- value

Arguments

  • dat: The microdata set. A numeric matrix or data frame containing the data.

  • keyVars: Indices or names of categorical key variables. They must, of course, match with the columns of dat .

  • numVars: Index or names of continuous key variables.

  • pramVars: Indices or names of categorical variables considered to be pramed.

  • ghostVars: 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.

  • weightVar: Indices or name determining the vector of sampling weights.

  • hhId: Index or name of the cluster ID (if available).

  • strataVar: Indices or names of stratification variables.

  • sensibleVar: Indices or names of sensible variables (for l-diversity)

  • excludeVars: 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.

  • options: additional options (if specified, a list must be used as input)

  • seed: (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.

  • randomizeRecords: (logical) if TRUE, the order of observations in the input microdata set will be randomized.

  • alpha: 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.

  • object: a sdcMicroObj-class object

  • value: NULL or a character vector of length 1 specifying a valid variable name

Returns

a sdcMicroObj-class object

an object of class sdcMicroObj with modified slot @strataVar

Objects from the Class

Objects can be created by calls of the form new("sdcMicroObj", ...).

Examples

## 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 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

## 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))
all(is.na(manipKeyVars$electcon) == is.na(manipGhostVars$electcon3))
all(is.na(manipKeyVars$water) == is.na(manipGhostVars$water2))

## 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"))

References

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. tools:::Rd_expr_doi("10.18637/jss.v067.i04")

Author(s)

Bernhard Meindl, Alexander Kowarik, Matthias Templ, Elias Rut

sdcMicro packageRead PDF manual