plot.cs_distribution function

Plot an Object of Class cs_distribution

This function creates a generic clinical significance plot by plotting the patients' pre intervention value on the x-axis and the post intervention score on the y-axis.

## S3 method for class 'cs_distribution'
plot(
  x,
  x_lab = NULL,
  y_lab = NULL,
  color_lab = "Group",
  lower_limit,
  upper_limit,
  show,
  point_alpha = 1,
  trajectory_alpha = 1,
  rci_fill = "grey10",
  rci_alpha = 0.1,
  overplotting = 0.02,
  ...
)

Arguments

• x: An object of class cs_distribution

• x_lab: String, x axis label. Default is "Pre".

• y_lab: String, x axis label. Default is "Post".

• color_lab: String, color label (if colors are displayed). Default is "Group"

• lower_limit: Numeric, lower plotting limit. Defaults to 2% smaller than minimum instrument score

• upper_limit: Numeric, upper plotting limit. Defaults to 2% larger than maximum instrument score

• show: Unquoted category name. You have several options to color different features. Available are

  • category (shows all categories at once)
  • improved (shows improved participants)
  • unchanged (shows unchanged participants)
  • deteriorated (shows deteriorated participants)

• point_alpha: Numeric, transparency adjustment for points. A value between 0 and 1 where 1 corresponds to not transparent at all and 0 to fully transparent.

• trajectory_alpha: Numeric, transparency adjustment for trajectories. A value between 0 and 1 where 1 corresponds to not transparent at all and 0 to fully transparent.

• rci_fill: String, a color (name or HEX code) for RCI fill

• rci_alpha: Numeric, controls the transparency of the RCI. This can be any value between 0 and 1, defaults to 0.1

• overplotting: Numeric, control amount of overplotting. Defaults to 0.02 (i.e., 2% of range between lower and upper limit).

• ...: Additional arguments

Returns

A ggplot2 plot

Examples

cs_results <- antidepressants |>
  cs_distribution(
    patient,
    measurement,
    pre = "Before",
    mom_di,
    reliability = 0.80
  )

# Plot the results "as is"
plot(cs_results)

# Change the axis labels
plot(cs_results, x_lab = "Before Intervention", y_lab = "After Intervention")

# Show the individual categories
plot(cs_results, show = category)

# Show a specific
plot(cs_results, show = improved)

# Show groups as specified in the data
cs_results_grouped <- antidepressants |>
  cs_distribution(
    patient,
    measurement,
    pre = "Before",
    mom_di,
    reliability = 0.80,
    group = condition
  )

plot(cs_results_grouped)

# To avoid overplotting, generic ggplot2 code can be used to facet the plot
library(ggplot2)
plot(cs_results_grouped) +
  facet_wrap(~ group)

# Adjust the transparency of individual data points
plot(cs_results, point_alpha = 0.3)

# Adjust the fill and transparency of the "unchanged" (RCI) region
plot(cs_results, rci_fill = "firebrick", rci_alpha = 0.2)

# Control the overplotting
plot(cs_results, overplotting = 0.1)

# Or adjust the axis limits by hand
plot(cs_results, lower_limit = 0, upper_limit = 80)