microaggrGower function

Microaggregation for numerical and categorical key variables based on a distance similar to the Gower Distance

The microaggregation is based on the distances computed similar to the Gower distance. The distance function makes distinction between the variable types factor,ordered,numerical and mixed (semi-continuous variables with a fixed probability mass at a constant value e.g. 0) methods

microaggrGower(
  obj,
  variables = NULL,
  aggr = 3,
  dist_var = NULL,
  by = NULL,
  mixed = NULL,
  mixed.constant = NULL,
  trace = FALSE,
  weights = NULL,
  numFun = mean,
  catFun = VIM::sampleCat,
  addRandom = FALSE
)

Arguments

• obj: sdcMicroObj-class-object or a data.frame
• variables: character vector with names of variables to be aggregated (Default for sdcMicroObj is all keyVariables and all numeric key variables)
• aggr: aggregation level (default=3)
• dist_var: character vector with variable names for distance computation
• by: character vector with variable names to split the dataset before performing microaggregation (Default for sdcMicroObj is strataVar)
• mixed: character vector with names of mixed variables
• mixed.constant: numeric vector with length equal to mixed, where the mixed variables have the probability mass
• trace: TRUE/FALSE for some console output
• weights: numerical vector with length equal the number of variables for distance computation
• numFun: function: to be used to aggregated numerical variables
• catFun: function: to be used to aggregated categorical variables
• addRandom: TRUE/FALSE if a random value should be added for the distance computation.

Returns

The function returns the updated sdcMicroObj or simply an altered data frame.

Details

The function sampleCat samples with probabilities corresponding to the occurrence of the level in the NNs. The function maxCat chooses the level with the most occurrences and random if the maximum is not unique.

Note

In each by group all distance are computed, therefore introducing more by-groups significantly decreases the computation time and memory consumption.

Examples

data(testdata,package="sdcMicro")
testdata <- testdata[1:200,]

for(i in c(1:7,9)) testdata[,i] <- as.factor(testdata[,i])
test <- microaggrGower(testdata,variables=c("relat","age","expend"),
  dist_var=c("age","sex","income","savings"),by=c("urbrur","roof"))

sdc <- createSdcObj(testdata,
  keyVars=c('urbrur','roof','walls','water','electcon','relat','sex'),
  numVars=c('expend','income','savings'), w='sampling_weight')

sdc <- microaggrGower(sdc)

See Also

sampleCat and maxCat

Author(s)

Alexander Kowarik