colleyRstats (version 0.0.1)

generateMoboPlot: 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.

Usage

generateMoboPlot(
  data,
  x,
  y,
  fillColourGroup = "ConditionID",
  ytext,
  legendPos = c(0.65, 0.85),
  numberSamplingSteps = 5,
  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.

y

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

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).

numberSamplingSteps

An integer specifying the number of initial sampling steps before the optimization phase begins. Default is 5.

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 "optimizatin" 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)
)
generateMoboPlot(df, x = "x", y = "y")

# Example with factor x-axis
df <- data.frame(
  x = factor(rep(1:5, each = 4)),
  y = rnorm(20),
  ConditionID = rep(c("A", "B"), 10)
)
generateMoboPlot(df, x = "x", y = "y", numberSamplingSteps = 3)