Approximate the lumping on a nominal variable

A drop-in approximation to lump_nominal() for when the exact solver becomes too slow. It applies a greedy heuristic that runs in polynomial time but does not guarantee the optimal lumping. See vignette("metrics") for the available heuristics.

Usage

lump_nominal_heuristic(
  data,
  threshold,
  adj_matrix = NULL,
  verbose = FALSE,
  heuristic = c("smart", "largest", "other"),
  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.

heuristic

Character string specifying the heuristic to use. For explanation, see maximum_mutual_information_nominal_heuristic().

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

lump_nominal() for a non-approximate version of this function.

Author

Daan Koning

Examples

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