Perform lumping on a nominal variable

Perform lumping on a nominal variable

Usage

lump_nominal(
  data,
  threshold,
  adj_matrix = NULL,
  verbose = FALSE,
  alternative_metric = c("mutual information", "bin count", "surplus"),
  level_namer = default_level_namer
)

Arguments

data

Factor or character vector of the categorical data.

threshold

The minimum number of samples each lumped level should contain.

adj_matrix

Adjancency matrix of the preference graph. Default: a complete graph, allowing all lumpings.

verbose

Logical value dictating if values should be printed. Default: FALSE.

alternative_metric

The metric that should be optimised for, if it is different from the default, the mutual information. For an explanation of the metrics see vignette("metrics").

level_namer

Function that takes a character vector of the original levels in a lump and returns the name of the new lumped level. Default: concatenating the original levels with a "+" in between.

Value

A factor vector with the lumped levels.

See also

maximum_mutual_information_nominal() for the underlying algorithm that this function wraps.

lump_hierarchical() for a version of this function that can take advantage of hierarchical structure in the data to speed up the execution time.

lump_nominal_heuristic() to approximate this function when the runtime becomes infeasible.

Author

Daan Koning

Examples

m <- 5
n <- 50
q <- 10
data <- sample(LETTERS[1:m], n, replace = TRUE)
lump_nominal(data, q)
#>  [1] E   A+D E   A+D A+D E   E   E   B   C   C   E   E   A+D E   B   A+D B   C  
#> [20] A+D C   E   A+D B   E   B   A+D A+D A+D B   C   A+D A+D B   E   A+D A+D C  
#> [39] A+D A+D C   B   C   B   B   C   A+D B   A+D C  
#> Levels: E C B A+D