protectTable {sdcTable} | R Documentation |
Wrapper-function to call several algorithms for secondary cell suppression of tabular data.
protectTable (fullData, method, ...)
fullData |
object from class fullData. |
method |
either HYPERCUBE, HITAS |
... |
additional input parameters. |
For correct input parameters for function protectTable() please have a look at Please have a look at the corresponding functions processTableHYPERCUBE() or rather processTableHITAS().
Manipulated data.
Bernhard Meindl
Repsilber, D. (1999). Das Quaderverfahren. In: Forum der Bundesstatistik, Band 31/1999. \ de Wolf, P.P (2002). HiTaS: A Heuristic Approach to Cell Suppression in Hierarchical Tables. In: Domingo-Ferrer, J. (Hrsg.): Inference Control in Statistical Databases. Vol. 2316. \ Fischetti, M., Salazar, J.J. (2000). Models and Algorithms for Optimizing Cell Suppression in Tabular Data with Linear Constraints. In: Journal of the American Statistical Association 95, 916-928.
## Not run: ### very simple example: protect a 2-dimensional table with only one marginal sum for each dimensional variable V1 <- c("01", "02", "03") V2 <- c("01", "02", "03","04", "05") minDat <- expand.grid(V1, V2) minDat$value <- rpois(nrow(minDat), 7) # creating the full data set needed for protectTable() fullDat <- createFullData (minDat, indexvars=1:2, l=list(c(1,1), c(1,1)), suppVals=TRUE, suppLimit=3, suppZeros=FALSE) # protecting the data using HYPERCUBE and HITAS approach protData1 <- protectTable(fullDat, method="HYPERCUBE") protData2 <- protectTable(fullDat, method="HITAS") summary(protData1); summary(protData2) ### example with existing data-set data(exampleFullData) result1 <- protectTable(exampleFullData, method="HYPERCUBE", allowZeros=FALSE, suppMethod="minSum") result2 <- protectTable(exampleFullData, method="HITAS", LPLPerc=15, UPLPerc=20) summary(result1) summary(result2) ## End(Not run)