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Add PARETO_MOOCORE Column to a Data Frame

Source: R/utils.R
add_pareto_moocore_column.Rd

This function calculates the Pareto front using moocore for a given set of objectives in a data frame and adds a new column, PARETO_MOOCORE, which indicates whether each row in the data frame belongs to the Pareto front.

Usage

add_pareto_moocore_column(data, objectives, maximise = FALSE)

Arguments

data

A data frame containing the data, including the objective columns.

objectives

A character vector specifying the names of the objective columns in data. These columns should be numeric and will be used to calculate the Pareto front.

maximise

Direction of optimisation, passed through to moocore::is_nondominated(). FALSE (the default) treats every objective as one to be minimised. Pass TRUE when larger is better for every objective – as it is for trust, acceptance, perceived safety and most other rating-scale outcomes – or a logical vector with one entry per objective for a mixed problem, e.g. c(TRUE, TRUE, FALSE) to maximise the first two and minimise the third. This removes the need to pass negated copies of your own columns.

Value

A data frame with the same columns as data, along with an additional column, PARETO_MOOCORE, which is TRUE for rows that are on the Pareto front and FALSE otherwise.

See also

add_pareto_emoa_column(), which answers the same question via emoa and accepts the same maximise argument.

Examples

# Define objective columns
objectives <- c("trust", "predictability", "perceivedSafety", "Comfort")

# Example data frame
main_df <- data.frame(
  trust = runif(10),
  predictability = runif(10),
  perceivedSafety = runif(10),
  Comfort = runif(10)
)

# Add the Pareto front column (minimising, the default)
main_df <- add_pareto_moocore_column(data = main_df, objectives)
head(main_df)
#>        trust predictability perceivedSafety    Comfort PARETO_MOOCORE
#> 1 0.68016292      0.4611865       0.8251994 0.44670247          FALSE
#> 2 0.49884561      0.3152418       0.2738182 0.37151118           TRUE
#> 3 0.64167935      0.1746759       0.5700450 0.02806097           TRUE
#> 4 0.66028435      0.5315735       0.3357191 0.46598719          FALSE
#> 5 0.09602416      0.4936370       0.5962628 0.39003139           TRUE
#> 6 0.76560016      0.7793086       0.1915180 0.02006522           TRUE

# All four objectives are ratings where higher is better
main_df <- add_pareto_moocore_column(main_df, objectives, maximise = TRUE)

# Mixed: maximise the ratings, minimise a workload score
main_df$workload <- runif(10)
main_df <- add_pareto_moocore_column(
  main_df,
  c(objectives, "workload"),
  maximise = c(TRUE, TRUE, TRUE, TRUE, FALSE)
)