colleyRstats (version 0.0.1)

generateMoboPlot2: Generate a Multi-objective Optimization Plot

Description

This function generates a multi-objective optimization plot using ggplot2. The plot visualizes the relationship between the x and y variables, grouping and coloring by a fill variable, with the option to customize legend position, labels, and annotation of sampling and optimization phases. Appropriate if you use https://github.com/Pascal-Jansen/Bayesian-Optimization-for-Unity in version 1.1.0 or higher.

Usage

generateMoboPlot2(
  data,
  x = "Iteration",
  y,
  phaseCol = "Phase",
  fillColourGroup = "",
  ytext,
  legendPos = c(0.65, 0.85),
  labelPosFormulaY = "top",
  verticalLinePosY = 0.75
)

Value

A ggplot object representing the multi-objective optimization plot, ready to be rendered.

Arguments

data

A data frame containing the data to be plotted.

x

A string representing the column name in data to be used for the x-axis. Can be either numeric or factor. Default is "Iteration".

y

A string representing the column name in data to be used for the y-axis. This should be a numeric variable.

phaseCol

the name of the column for the color of the phase (sampling or optimization)

fillColourGroup

A string representing the column name in data that defines the fill color grouping for the plot. Default is "ConditionID".

ytext

A custom label for the y-axis. If not provided, the y-axis label will be the title-cased version of y.

legendPos

A numeric vector of length 2 specifying the position of the legend inside the plot. Default is c(0.65, 0.85).

labelPosFormulaY

A string specifying the vertical position of the polynomial equation label in the plot. Acceptable values are "top", "center", or "bottom". Default is "top".

verticalLinePosY

A numeric value of the y-coordinate where the "sampling" and "optimization" line should be drawn.

Examples

library(ggplot2)
library(ggpmisc)

# Example with numeric x-axis
df <- data.frame(
  x = 1:20,
  y = rnorm(20),
  ConditionID = rep(c("A", "B"), 10),
  Phase = rep(c("Sampling", "Optimization"), 10)
)
generateMoboPlot2(data = df, x = "x", y = "y")