plot_interaction: Plot the interaction from a 2x2 design

In rcalinjageman/esci: Estimation Statistics with Confidence Intervals

Plot the interaction from a 2x2 design

Description

plot_interaction helps visualize the interaction from a 2x2 design. It plots the 2 simple effects for the first factor and can also help visualize the CIs on those simple effects. It is the comparison between those simple effects that represents an interaction (the difference in the difference). You can pass esci-estimate objects generated estimate_mdiff_2x2_between() or estimate_mdiff_2x2_mixed(). This function returns a ggplot2 object.

Usage

plot_interaction(
  estimate,
  effect_size = c("mean", "median"),
  show_CI = FALSE,
  ggtheme = NULL,
  line_count = 100,
  line_alpha = 0.02
)

Arguments

estimate	A esci_estimate object with raw data an es_mdiff_2x2_ function
effect_size	Optional; one of 'mean' or 'median' to determine the measure of central tendency plotted. Note that median is only available if the estimate was generated from raw data. Defaults to 'mean'
show_CI	Optional logical; set to TRUE to visualize the confidence intervals on each simple effect; defaults to FALSE
ggtheme	Optional ggplot2 theme object to specify the visual style of the plot. Defaults to ggplot2::theme_classic()
line_count	Optional integer > 0 to specify the number of lines used to visualize the simple-effect confidence intervals; defaults to 100
line_alpha	Optional numeric between 0 and 1 to specify the alpha (transparency) of the confidence interval lines; defaults to 0.02

Details

This function was developed primarily for student use within jamovi when learning along with the text book Introduction to the New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024).

Expect breaking changes as this function is improved for general use. Work still do be done includes:

• Revise to avoid deprecated ggplot features

• Revise for consistent ability to control aesthetics and consistent layer names

Value

Returns a ggplot object

Examples

data("data_videogameaggression")

estimates_from_raw <- esci::estimate_mdiff_2x2_between(
  esci::data_videogameaggression,
  Agression,
  Violence,
  Difficulty
)

# To visualize the estimated mean difference for the interaction
myplot_from_raw <- esci::plot_mdiff(
  estimates_from_raw$interaction,
  effect_size = "median"
)

# To conduct a hypothesis test on the mean difference
res_htest_from_raw <- esci::test_mdiff(
  estimates_from_raw$interaction,
  effect_size = "median"
)


# From summary data
means <- c(1.5, 1.14, 1.38, 2.22)
sds <- c(1.38, .96,1.5, 1.68)
ns <- c(26, 26, 25, 26)
grouping_variable_A_levels <- c("Evening", "Morning")
grouping_variable_B_levels <- c("Sleep", "No Sleep")

estimates_from_summary <- esci::estimate_mdiff_2x2_between(
  means = means,
  sds = sds,
  ns = ns,
  grouping_variable_A_levels = grouping_variable_A_levels,
  grouping_variable_B_levels = grouping_variable_B_levels,
  grouping_variable_A_name = "Testing Time",
  grouping_variable_B_name = "Rest",
  outcome_variable_name = "False Memory Score",
  assume_equal_variance = TRUE
)

# To visualize the estimated mean difference for the interaction
plot_mdiff_interaction <- esci::plot_mdiff(
  estimates_from_summary$interaction,
  effect_size = "mean"
)

# To visualize the interaction as a line plot
plot_interaction_line <- esci::plot_interaction(estimates_from_summary)

# Same but with fan effect representing each simple-effect CI
plot_interaction_line_CI <- esci::plot_interaction(
  estimates_from_summary,
  show_CI = TRUE
)

# To conduct a hypothesis test on the mean difference
res_htest_from_raw <- esci::test_mdiff(
  estimates_from_summary$interaction,
  effect_size = "mean"
)

rcalinjageman/esci documentation built on March 29, 2024, 7:30 p.m.