sdcMicro (version 4.6.0)

sdcMicroObj-class: Class "sdcMicroObj"

Description

Class to save all information about the SDC process

modify sdcMicroObj-objects depending on argument type

undo last changes to sdcMicroObj-objects if possible note that this will only work if the user makes use of the prev slot or uses the sdcMicroObj functions

Usage

createSdcObj(dat, keyVars, numVars = NULL, pramVars = NULL, ghostVars = NULL, weightVar = NULL, hhId = NULL, strataVar = NULL, sensibleVar = NULL, options = NULL)
get.sdcMicroObj(object, type)
set.sdcMicroObj(object, type, input)
undolast(object)
"get.sdcMicroObj"(object, type)
"set.sdcMicroObj"(object, type, input)
"undolast"(object)

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)

options

additional options.

object

an object of class sdcMicroObj

type

a character vector of length 1 defining what to calculate|return|modify. Allowed types are:

input

a list depending on argument type.

Value

an object of class sdcMicroObjan object of class sdcMicroObj

Objects from the Class

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

Examples

showClass("sdcMicroObj")
## Not run: 
# data(testdata)
# sdc <- createSdcObj(testdata,
#   keyVars=c('urbrur','roof','walls','water','electcon','relat','sex'),
#   numVars=c('expend','income','savings'), w='sampling_weight')
# head(sdc@manipNumVars)
# ### Display Risks
# sdc@risk$global
# sdc <- dRisk(sdc)
# sdc@risk$numeric
# ### use addNoise without Parameters
# sdc <- addNoise(sdc,variables=c("expend","income"))
# head(sdc@manipNumVars)
# sdc@risk$numeric
# ### undolast
# sdc <- undolast(sdc)
# head(sdc@manipNumVars)
# sdc@risk$numeric
# ### redo addNoise with Parameter
# sdc <- addNoise(sdc, noise=0.2)
# head(sdc@manipNumVars)
# sdc@risk$numeric
# ### dataGen
# #sdc <- undolast(sdc)
# #head(sdc@risk$individual)
# #sdc@risk$global
# #sdc <- dataGen(sdc)
# #head(sdc@risk$individual)
# #sdc@risk$global
# ### LocalSuppression
# sdc <- undolast(sdc)
# head(sdc@risk$individual)
# sdc@risk$global
# sdc <- localSuppression(sdc)
# head(sdc@risk$individual)
# sdc@risk$global
# ### microaggregation
# sdc <- undolast(sdc)
# head(get.sdcMicroObj(sdc, type="manipNumVars"))
# sdc <- microaggregation(sdc)
# head(get.sdcMicroObj(sdc, type="manipNumVars"))
# ### pram
# sdc <- undolast(sdc)
# head(sdc@risk$individual)
# sdc@risk$global
# sdc <- pram(sdc,keyVar="water")
# head(sdc@risk$individual)
# sdc@risk$global
# ### rankSwap
# sdc <- undolast(sdc)
# head(sdc@risk$individual)
# sdc@risk$global
# head(get.sdcMicroObj(sdc, type="manipNumVars"))
# sdc <- rankSwap(sdc)
# head(get.sdcMicroObj(sdc, type="manipNumVars"))
# head(sdc@risk$individual)
# sdc@risk$global
# ### suda2
# sdc <- suda2(sdc)
# sdc@risk$suda2
# ### topBotCoding
# head(get.sdcMicroObj(sdc, type="manipNumVars"))
# sdc@risk$numeric
# sdc <- topBotCoding(sdc, value=60000000, replacement=62000000, column="income")
# head(get.sdcMicroObj(sdc, type="manipNumVars"))
# sdc@risk$numeric
# ### LocalRecProg
# data(testdata2)
# sdc <- createSdcObj(testdata2,
#   keyVars=c("urbrur", "roof", "walls", "water", "sex", "relat"))
# sdc@risk$global
# sdc <- LocalRecProg(sdc)
# sdc@risk$global
# ### LLmodGlobalRisk
# sdc <- undolast(sdc)
# sdc <- LLmodGlobalRisk(sdc, inclProb=0.001)
# sdc@risk$model
# ## End(Not run)
## we can also specify ghost (linked) variables
## these variables are linked to some categorical key variables
## and have the sampe suppression pattern as the variable that they
## are linked to after \code{\link{localSuppression}} has been applied
data(testdata)
testdata$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")

## 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))