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"
)
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?
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