`R/maG.R`

`microaggrGower.Rd`

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)

```
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
)
```

- 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.

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

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.

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

```
data(testdata,package="sdcMicro")
testdata <- testdata[1:200,]
# \donttest{
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)
# }
```