plot_correlation function

Plot an estimated Pearson's r value

plot_correlation creates a ggplot2 plot suitable for visualizing an estimate correlation between two continuous variables (Pearson's r). This function can be passed an esci_estimate object generated by estimate_r()

plot_correlation(
  estimate,
  error_layout = c("halfeye", "eye", "gradient", "none"),
  error_scale = 0.3,
  error_normalize = c("groups", "all", "panels"),
  rope = c(NA, NA),
  ggtheme = NULL
)

Arguments

• estimate: * An esci_estimate object generated by estimate_r()
• error_layout: * Optional; One of 'halfeye', 'eye', 'gradient' or 'none' for how expected sampling error of the measure of central tendency should be displayed. Caution - the displayed error distributions do not seem correct yet
• error_scale: * Optional real number > 0 specifying width of the expected sampling error visualization; default is 0.3
• error_normalize: * Optional; One of 'groups' (default), 'all', or 'panels' specifying how width of expected sampling error distributions should be calculated.
• rope: * Optional two-item vector specifying a region of practical equivalence (ROPE) to be highlighted on the plot. For a point null hypothesis, pass the same value (e.g. c(0, 0) to test a point null of exactly 0); for an interval null pass ascending values (e.g. c(-1, 1))
• ggtheme: * Optional ggplot2 theme object to control overall styling; defaults to ggplot2::theme_classic()

Returns

Returns a ggplot object

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

Examples

# From raw data
data("data_thomason_1")

estimate_from_raw <- esci::estimate_r(
  esci::data_thomason_1,
  Pretest,
  Posttest
)

# To visualize the value of r
myplot_correlation <- esci::plot_correlation(estimate_from_raw)

# To visualize the data (scatterplot) and use regression to obtain Y' from X
myplot_scatter_from_raw <- esci::plot_scatter(estimate_from_raw, predict_from_x = 10)

# To evaluate a hypothesis (interval null from -0.1 to 0.1):
res_htest_from_raw <- esci::test_correlation(
  estimate_from_raw,
  rope = c(-0.1, 0.1)
)

# From summary data
estimate_from_summary <- esci::estimate_r(r = 0.536, n = 50)

# To visualize the value of r
myplot_correlation_from_summary <- esci::plot_correlation(estimate_from_summary)

# To evaluate a hypothesis (interval null from -0.1 to 0.1):
res_htest_from_summary <- esci::test_correlation(
  estimate_from_summary,
  rope = c(-0.1, 0.1)
)