Create an Importance-Performance Matrix Analysis scatter plot — kda_ipma

A short description...

Usage

kda_ipma_scatterPlot(
  model,
  ipma_obj,
  show_labels = TRUE,
  quadrant_colors = c(`Concentrate here` = yougov_colors[["Red 1"]],
    `Keep up the good work` = yougov_colors[["Purple 1"]], `Possible overkill` =
    yougov_colors[["Teal 1"]], `Low priority` = yougov_colors[["Blue 1"]]),
  geom_point_size = 6
)

Arguments

model: A fitted model object.
ipma_obj: An IPMA results object, i.e., the output from kda_ipma().
show_labels: Optional. A logical indicating whether to display predictor labels on the plot. Defaults to TRUE.
quadrant_colors: Optional. A named character vector of colors for the four quadrants. Defaults to a set of predefined colors.
geom_point_size: Optional. A numeric value specifying the size of the points on the scatter plot. Defaults to 8.

Value

A list containing:

d: A data frame with the plot data
p: A ggplot2 plot object

Examples

# Fit a model
m <- lm(F600 ~ ., data = bkw_processed)
# Fit importance and performance objects
importance_obj <- kda_importance_jrw(m)
performance_obj <- kda_performance(m)
ipma_obj <- kda_ipma(importance_obj, performance_obj)
# Create IPMA scatter plot
ipma_plot <- kda_ipma_scatterPlot(m, ipma_obj)
# Access IPMA plot
print(ipma_plot$p)