GaussSuppression (version 0.9.2)

PrimaryFromRiskyDefault: Additional primary cells based on risky primary cells

Description

The algorithm uses parent-child relationships found from the model matrix (x)

Usage

PrimaryFromRiskyDefault(x, y, risky, candidates, allDims = FALSE)

Value

Additional primary cells as indices to columns in x.

Arguments

x

The model matrix

y

A vector of numeric values with a length equal to nrow(x)

risky

Indices to columns in x corresponding to primary cells classified as risky (interval limits not reached)

candidates

Indices to columns in x that are candidates for becoming additional primary cells. Higher order cells must be included so that parent-child relationships are seen.

allDims

When TRUE, a primary cell is added for each dimension. can be specified as a vector of length length(risky)

Details

For a single risky cell, the algorithm can be formulated as:

• Consider this cell as a child and identify all parents that are present in candidates.

• Remove parents who are also parents of other parents (i.e., eliminate higher-level parents).

• Identify the children of these remaining parents that are included in candidates.

• Select the child that has the smallest value in the numeric variable (y).

For several risky cells, coordination takes place. See the comment below the examples.

Examples

# Example inspired by suppression with maxN = 5
d1 <- SSBtoolsData("d1")
mm <- SSBtools::ModelMatrix(d1, dimVar = 1:2, crossTable = TRUE)
x <- mm$modelMatrix
y <- t(x) %*% d1$freq

risky <- c(13, 15, 40, 45)
candidates <- c(1:12, 14, 16, 17, 19, 21, 21, 24, 26:37, 39, 42, 44)

info <- rep("", length(y))
info[risky ] <- "risky"
info[candidates] <- "c"
cbind(mm$crossTable, y=as.vector(y), info)

PrimaryFromRiskyDefault(x = x, y = y, risky = risky, candidates = candidates)
PrimaryFromRiskyDefault(x = x, y = y, risky = 40, candidates = candidates)

# The last solution (39) is not included in the first (28, 35). 
# This is because 39 is not needed when 35 is already included.