# Class `"sdcMicroObj"` Class to save all information about the SDC process class ```r 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 ```r ## 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

sdcMicroObj-class function