pram: Post Randomization

Description

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.

Usage

pram(obj, variables = NULL, strata_variables = NULL, pd = 0.8, alpha = 0.5)

Arguments

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.

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:

the matrix must be a square matrix
the rownames and colnames of the matrix must match the levels (in the same order) of the factor-variable that should be postrandomized.
the rowSums and colSums of the matrix need to equal 1

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.

Value

a modified '>sdcMicroObj object or a new object containing original and post-randomized variables (with suffix "_pram").

References

http://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. 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. 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. 10.1007/978-3-319-50272-4

Examples

Run this code

# NOT RUN {
data(testdata)

# 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
# }