plot.cs_combined: Plot an Object of Class cs_combined

In clinicalsignificance: A Toolbox for Clinical Significance Analyses in Intervention Studies

Plot an Object of Class cs_combined

Description

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.

Usage

## S3 method for class 'cs_combined'
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) recovered (shows recovered participants) improved (shows improved participants) unchanged (shows unchanged participants) deteriorated (shows deteriorated participants) harmed (shows harmed 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

Value

A ggplot2 plot

Examples

cs_results <- antidepressants |>
  cs_combined(
    patient,
    measurement,
    pre = "Before",
    mom_di,
    reliability = 0.80,
    m_functional = 15,
    sd_functional = 8,
    cutoff_type = "c"
  )


# 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 = recovered)


# Show groups as specified in the data
cs_results_grouped <- antidepressants |>
  cs_combined(
    patient,
    measurement,
    pre = "Before",
    mom_di,
    reliability = 0.80,
    m_functional = 15,
    sd_functional = 8,
    cutoff_type = "c",
    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)

clinicalsignificance documentation built on April 4, 2025, 12:19 a.m.