Given a categorical variable, this function creates a set of indicator variables for the various possible sets of levels.
binarizeCategoricalVariable(
x,
levelOrder = NULL,
ignore = NULL,
minCount = 3,
val1 = 0, val2 = 1,
includePairwise = TRUE,
includeLevelVsAll = FALSE,
dropFirstLevelVsAll = FALSE,
dropUninformative = TRUE,
namePrefix = "",
levelSep = NULL,
nameForAll = "all",
levelSep.pairwise = if (length(levelSep)==0) ".vs." else levelSep,
levelSep.vsAll = if (length(levelSep)==0)
(if (nameForAll=="") "" else ".vs.") else levelSep,
checkNames = FALSE,
includeLevelInformation = TRUE)A matrix containing the indicators variabels, one in each column. When includeLevelInformation is
TRUE, the attribute includedLevels is a table with one column per output column and two rows,
giving the two levels (unique values of x) represented by the column.
A vector with categorical values.
Optional specification of the levels (unique values) of x. Defaults to sorted unique values of
x, but can be used to only include a subset of the existing levels as well as to specify the order of
the levels in the output variables.
Optional specification of levels of x that are to be ignored. Note that the levels are ignored only when
deciding which variables to include in the output; the samples with these values of x will be included
in "all" in indicators of level vs. all others.
Levels of x for which there are fewer than minCount elements will be ignored.
Value for the lower level in binary comparisons.
Value for the higher level in binary comparisons.
Logical: should pairwise binary indicators be included? For each pair of levels, the indicator is val1
for the lower level (earlier in levelOrder), val2 for the higher level and NA otherwise.
Logical: should binary indicators for each level be included? The indicator is val2 where x
equals the level and val1 otherwise.
Logical: should the column representing first level vs. all be dropped? This makes the resulting matrix of indicators usable for regression models.
Logical: should uninformative (constant) columns be dropped?
Prefix to be used in column names of the output.
When naming columns that represent a level vs. all others, nameForAll will be used to represent all
others.
Separator for levels to be used in column names of the output. If NULL, pairwise and level vs. all indicators will
use different level separators set by levelSep.pairwise and levelSep.vsAll.
Separator for levels to be used in column names for pairwise indicators in the output.
Separator for levels to be used in column names for level vs. all indicators in the output.
Logical: should the names of the output be made into syntactically correct R language names?
Logical: should information about which levels are represented by which columns be included in the attributes of the output?
Peter Langfelder
The function creates two types of indicators. The first is one level (unique value) of x vs. all
others, i.e., for a given level, the indicator is val2 (usually 1) for all elements of x that
equal the level, and val1 (usually 0)
otherwise. Column names for these indicators are the concatenation of namePrefix, the level,
nameSep and nameForAll. The level vs. all indicators are created for all levels that have at
least minCounts samples, are present in levelOrder (if it is non-NULL) and are not included in
ignore.
The second type of indicator encodes binary comparisons. For each pair of levels (both with at least
minCount samples), the indicator is val2 (usually 1) for the higher level and val1
(usually 0) for the lower level. The level order is given by levelOrder (which defaults to the sorted
levels of x), assumed to be sorted in increasing order. All levels with at least minCount
samples that are included in levelOrder and not included in ignore are included.
Variations and wrappers for this function:
binarizeCategoricalColumns for binarizing several columns of a matrix or data frame
set.seed(2);
x = sample(c("A", "B", "C"), 15, replace = TRUE);
out = binarizeCategoricalVariable(x, includePairwise = TRUE, includeLevelVsAll = TRUE);
data.frame(x, out);
attr(out, "includedLevels")
# A different naming for level vs. all columns
binarizeCategoricalVariable(x, includeLevelVsAll = TRUE, nameForAll = "");
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