Function to define a plot, either showing the main or interaction effect in bold.

Function to define a plot, either showing the main or interaction effect in bold.

Usage

generateEffectPlot(
  data,
  x,
  y,
  fillColourGroup,
  ytext = "testylab",
  xtext = "testxlab",
  legendPos = c(0.1, 0.23),
  legendHeading = NULL,
  shownEffect = "main",
  effectLegend = FALSE,
  effectDescription = NULL,
  xLabelsOverwrite = NULL,
  useLatexMarkup = FALSE,
  numberColors = lifecycle::deprecated(),
  subject = NULL
)

plot_effect(
  data,
  x,
  y,
  fillColourGroup,
  ytext = "testylab",
  xtext = "testxlab",
  legendPos = c(0.1, 0.23),
  legendHeading = NULL,
  shownEffect = "main",
  effectLegend = FALSE,
  effectDescription = NULL,
  xLabelsOverwrite = NULL,
  useLatexMarkup = FALSE,
  numberColors = lifecycle::deprecated(),
  subject = NULL
)

Arguments

data

the data frame

x

factor shown on the x-axis

y

dependent variable

fillColourGroup

group to color

ytext

label for y-axis

xtext

label for x-axis

legendPos

position for legend

legendHeading

custom heading for legend

shownEffect

either "main" or "interaction"

effectLegend

TRUE: show legend for effect (Default: FALSE)

effectDescription

custom label for effect

xLabelsOverwrite

custom labels for x-axis

useLatexMarkup

use latex font and markup

numberColors

Never had any effect: the colours come from see::scale_colour_see(), which uses one colour per group. Passing it warns.

subject

optional participant-ID column (a string). Give it when x and/or fillColourGroup vary within participants, to get within-subject error bars (see Details). Default NULL.

Value

a plot

Details

Points and lines. The small points and the dashed (main) or bold (interaction) per-group lines are the cell means. The bold main-effect line and its large points are the unweighted marginal means of x: for each level of x, the mean of the cell means across fillColourGroup. That is the quantity a main effect in a factorial ANOVA is about; the mean of the raw observations would weight each cell by its size and, in an unbalanced design, follow whichever group happens to be larger.

Error bars are 95\

• subject = NULL (default): nonparametric bootstrap intervals (ggplot2::mean_cl_boot(), requires 'Hmisc'). These treat every observation as independent, i.e. they are between-subject intervals. That is right for between-subject factors, but for a factor that varies within participants they include the between-participant spread a within-subject comparison does not depend on, and look misleadingly wide.

• subject given: within-subject intervals after Cousineau (2005), with the Morey (2008) correction. Each participant's scores are centred on their own mean, the variance of the normalised scores is inflated by \(M/(M-1)\) (\(M\) = number of within-subject conditions), and a factor that does not vary within participants is respected by normalising within its groups. Participants without a value in every within-subject condition are dropped (with a message), and more than one row per participant and cell is an error.

Either way, the intervals describe single means, not differences between them; overlapping bars do not by themselves imply a non-significant difference.

Naming

plot_effect() is the spelling used throughout the documentation and the one to prefer in new code: the report_* / plot_* / check_* prefixes make the API discoverable through autocomplete.

generateEffectPlot() is the original name. Both names refer to the same function object, so they are entirely interchangeable; the original remains fully supported and is not scheduled for removal, and existing scripts keep working unchanged.

References

Cousineau, D. (2005). Confidence intervals in within-subject designs: A simpler solution to Loftus and Masson's method. Tutorials in Quantitative Methods for Psychology, 1(1), 42–45.

Morey, R. D. (2008). Confidence intervals from normalized data: A correction to Cousineau (2005). Tutorials in Quantitative Methods for Psychology, 4(2), 61–64.

Examples

# \donttest{
set.seed(123)
main_df <- data.frame(
  strategy    = factor(rep(c("A", "B"), each = 20)),
  Emotion     = factor(rep(c("Happy", "Sad"), times = 20)),
  trust_mean  = rnorm(40, mean = 5, sd = 1)
)

generateEffectPlot(
  data = main_df,
  x = "strategy",
  y = "trust_mean",
  fillColourGroup = "Emotion",
  ytext = "Trust",
  xtext = "Strategy",
  legendPos = c(0.1, 0.23)
)

# }