Skip to contents

Add PARETO_EMOA Column to a Data Frame

Source: R/utils.R
add_pareto_emoa_column.Rd

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

Usage

add_pareto_emoa_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. FALSE (the default) treats every objective as one to be minimised, which is what emoa does natively. 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. Objectives flagged TRUE are negated internally, so you no longer 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_EMOA, which is TRUE for rows that are on the Pareto front and FALSE otherwise.

See also

add_pareto_moocore_column(), which answers the same question via moocore 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_emoa_column(data = main_df, objectives)
head(main_df)
#>         trust predictability perceivedSafety    Comfort PARETO_EMOA
#> 1 0.080750138     0.87460066       0.2898923 0.03123033        TRUE
#> 2 0.834333037     0.17494063       0.6783804 0.22556253        TRUE
#> 3 0.600760886     0.03424133       0.7353196 0.30083081        TRUE
#> 4 0.157208442     0.32038573       0.1959567 0.63646561        TRUE
#> 5 0.007399441     0.40232824       0.9805397 0.47902455        TRUE
#> 6 0.466393497     0.19566983       0.7415215 0.43217126        TRUE

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