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Relabels clusters to match another cluster assignment

Source: R/reconcile_clusterings.R
reconcile_clusterings_mapping.Rd

When forcing one-to-one, the user needs to decide what to prioritize:

  • "accuracy": optimize raw count of all observations with the same label across the two assignments

  • "precision": optimize the average percent of each alt cluster that matches the corresponding primary cluster

Usage

reconcile_clusterings_mapping(
  primary,
  alternative,
  one_to_one = TRUE,
  optimize = "accuracy"
)

Arguments

primary

A vector containing cluster labels, to be matched

alternative

Another vector containing cluster labels, to be changed

one_to_one

Boolean; should each alt cluster match only one primary cluster?

optimize

One of "accuracy" or "precision"; see description.

Value

A tibble with 3 columns; primary, alt, alt_recoded

Details

Retains the cluster labels of the primary assignment, and relabel the alternate assignment to match as closely as possible. The user must decide whether clusters are forced to be "one-to-one"; that is, are we allowed to assign multiple labels from the alternate assignment to the same primary label?

Cluster labels are arbitrary — two clusterings of the same data may agree on the groups but use different label names (e.g. "Dog" vs "Apple" for the same cluster). reconcile_clusterings_mapping() is useful when you want to compare two clusterings, for example:

  • Comparing cluster assignments across cross-validation folds.

  • Checking stability of a clustering algorithm across different random seeds.

  • Aligning predicted clusters on new data with the original training labels.

Examples

factor1 <- c("Apple", "Apple", "Carrot", "Carrot", "Banana", "Banana")
factor2 <- c("Dog", "Dog", "Cat", "Dog", "Fish", "Fish")
reconcile_clusterings_mapping(factor1, factor2)
#> # A tibble: 6 × 3
#>   primary alt   alt_recoded
#>   <chr>   <chr> <chr>      
#> 1 Apple   Dog   Carrot     
#> 2 Apple   Dog   Carrot     
#> 3 Carrot  Cat   Banana     
#> 4 Carrot  Dog   Carrot     
#> 5 Banana  Fish  Apple      
#> 6 Banana  Fish  Apple      

factor1 <- c("Apple", "Apple", "Carrot", "Carrot", "Banana", "Banana")
factor2 <- c("Dog", "Dog", "Cat", "Dog", "Fish", "Parrot")
reconcile_clusterings_mapping(factor1, factor2, one_to_one = FALSE)
#> # A tibble: 6 × 3
#>   primary alt    alt_recoded
#>   <chr>   <chr>  <chr>      
#> 1 Apple   Dog    Apple      
#> 2 Apple   Dog    Apple      
#> 3 Carrot  Cat    Carrot     
#> 4 Carrot  Dog    Apple      
#> 5 Banana  Fish   Banana     
#> 6 Banana  Parrot Banana