Post Randomization
To be used on categorical data stored as factors. The algorithm randomly changes the values of variables in selected records (usually the risky ones) according to an invariant probability transition matrix or a custom-defined transition matrix. methods
pram(obj, variables = NULL, strata_variables = NULL, pd = 0.8, alpha = 0.5)
obj: Input data. Allowed input data are objects of class data.frame, factor or sdcMicroObj .
variables: Names of variables in obj on which post-randomization should be applied. If obj is a factor, this argument is ignored. Please note that pram can only be applied to factor-variables.
strata_variables: names of variables for stratification (will be set automatically for an object of class sdcMicroObj . One can also specify an integer vector or factor that specifies that desired groups. This vector must match the dimension of the input data set, however. For a possible use case, have a look at the examples.
pd: minimum diagonal entries for the generated transition matrix P. Either a vector of length 1 (which is recycled) or a vector of the same length as the number of variables that should be postrandomized. It is also possible to set pd
to a numeric matrix. This matrix will be used directly as the transition matrix. The matrix must be constructed as follows:
It is also possible to combine the different ways. For details have a look at the examples.
alpha: amount of perturbation for the invariant Pram method. This is a numeric vector of length 1 (that will be recycled if necessary) or a vector of the same length as the number of variables. If one specified as transition matrix directly, alpha is ignored.
a modified sdcMicroObj object or a new object containing original and post-randomized variables (with suffix "_pram").
Deprecated method 'pram_strata' is no longer available in sdcMicro > 4.5.0
data(testdata) ## donttest is necessary because of ## Examples with CPU time > 2.5 times elapsed time ## caused by using C++ code and/or data.table ## using a factor variable as input res <- pram(as.factor(testdata$roof)) print(res) summary(res) ## using a data.frame as input ## pram can only be applied to factors ## -- > we have to recode to factors beforehand testdata$roof <- factor(testdata$roof) testdata$walls <- factor(testdata$walls) testdata$water <- factor(testdata$water) ## pram() is applied within subgroups defined by ## variables "urbrur" and "sex" res <- pram( obj = testdata, variables = "roof", strata_variables = c("urbrur", "sex")) print(res) summary(res) ## default parameters (pd = 0.8 and alpha = 0.5) for the generation ## of the invariant transition matrix will be used for all variables res1 <- pram( obj = testdata, variables = c("roof", "walls", "water")) print(res1) ## specific parameter settings for each variable res2 <- pram( obj = testdata, variables = c("roof", "walls", "water"), pd = c(0.95, 0.8, 0.9), alpha = 0.5) print(res2) ## detailed information on pram-parameters (such as the transition matrix 'Rs') ## is stored in the output, eg. for variable 'roof' #attr(res2, "pram_params")$roof ## we can also specify a custom transition-matrix directly mat <- diag(length(levels(testdata$roof))) rownames(mat) <- colnames(mat) <- levels(testdata$roof) res3 <- pram( obj = testdata, variables = "roof", pd = mat) print(res3) # of course, nothing has changed! ## it is possible use a transition matrix for a variable and use the 'traditional' way ## of specifying a number for the minimal diagonal entries of the transision matrix ## for other variables. In this case we must supply `pd` as list. res4 <- pram( obj = testdata, variables = c("roof", "walls"), pd = list(mat, 0.5), alpha = c(NA, 0.5)) print(res4) summary(res4) attr(res4, "pram_params") ## application to objects of class sdcMicro with default parameters data(testdata2) testdata2$urbrur <- factor(testdata2$urbrur) sdc <- createSdcObj( dat = testdata2, keyVars = c("roof", "walls", "water", "electcon", "relat", "sex"), numVars = c("expend", "income", "savings"), w = "sampling_weight") sdc <- pram( obj = sdc, variables = "urbrur") print(sdc, type = "pram") ## this is equal to the previous application. If argument 'variables' is NULL, ## all variables from slot 'pramVars' will be used if possible. sdc <- createSdcObj( dat = testdata2, keyVars = c("roof", "walls", "water", "electcon", "relat", "sex"), numVars = c("expend", "income", "savings"), w = "sampling_weight", pramVars = "urbrur") sdc <- pram(sdc) print(sdc, type="pram") ## we can specify transition matrices for sdcMicroObj-objects too #testdata2$roof <- factor(testdata2$roof) sdc <- createSdcObj( dat = testdata2, keyVars = c("roof", "walls", "water", "electcon", "relat", "sex"), numVars = c("expend", "income", "savings"), w = "sampling_weight") mat <- diag(length(levels(testdata2$roof))) rownames(mat) <- colnames(mat) <- levels(testdata2$roof) mat[1,] <- c(0.9, 0, 0, 0.05, 0.05) sdc <- pram( obj = sdc, variables = "roof", pd = mat) print(sdc, type = "pram") ## we can also have a look at the transitions get.sdcMicroObj(sdc, "pram")$transitions
https://www.gnu.org/software/glpk/
Kowarik, A. and Templ, M. and Meindl, B. and Fonteneau, F. and Prantner, B.: Testing of IHSN Cpp Code and Inclusion of New Methods into sdcMicro, in: Lecture Notes in Computer Science, J. Domingo-Ferrer, I. Tinnirello (editors.); Springer, Berlin, 2012, ISBN: 978-3-642-33626-3, pp. 63-77. tools:::Rd_expr_doi("10.1007/978-3-642-33627-0_6")
Templ, M. and Kowarik, A. and Meindl, B.: Statistical Disclosure Control for Micro-Data Using the R Package sdcMicro. in: Journal of Statistical Software, 67 (4), 1--36, 2015. tools:::Rd_expr_doi("10.18637/jss.v067.i04")
Templ, M.: Statistical Disclosure Control for Microdata: Methods and Applications in R.
in: Springer International Publishing, 287 pages, 2017. ISBN 978-3-319-50272-4. tools:::Rd_expr_doi("10.1007/978-3-319-50272-4")
Alexander Kowarik, Matthias Templ, Bernhard Meindl