clinicalsignificance (version 2.0.0)

plot.cs_percentage: Plot an Object of Class cs_percentage

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 cs_percentage
plot(
  x,
  x_lab = "Pre",
  y_lab = "Post",
  color_lab = "Group",
  lower_limit,
  upper_limit,
  show,
  point_alpha = 1,
  pct_fill = "grey10",
  pct_alpha = 0.1,
  overplotting = 0.02,
  ...
)

Value

A ggplot2 plot

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

• 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.

pct_fill

String, a color (name or HEX code) for the percentage range fill

pct_alpha

Numeric, controls the transparency of the percentage fill. 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

Examples

cs_results <- antidepressants |>
  cs_percentage(
    patient,
    measurement,
    pre = "Before",
    mom_di,
    pct_improvement = 0.4
  )


# 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 category
plot(cs_results, show = improved)


# Show groups as specified in the data
cs_results_grouped <- antidepressants |>
  cs_percentage(
    patient,
    measurement,
    pre = "Before",
    mom_di,
    pct_improvement = 0.4,
    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" (PCC) region
plot(cs_results, pct_fill = "firebrick", pct_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)