plot_scatter function

Generates a scatter plot of data for two continuous variables

plot_scatter returns a ggplot2 object of data from two continuous variables. Can indicate regression line and its confidence interval,prediction intervals regression residuals and more. This function requires as input an esci_estimate object generated by estimate_r()

plot_scatter(
  estimate,
  show_line = FALSE,
  show_line_CI = FALSE,
  show_PI = FALSE,
  show_residuals = FALSE,
  show_mean_lines = FALSE,
  show_r = FALSE,
  predict_from_x = NULL,
  plot_as_z = FALSE,
  ggtheme = ggplot2::theme_classic()
)

Arguments

• estimate: * an esci_estimate object generated by estimate_r()

• show_line: * Boolean; defaults to FALSE; set to TRUE to show the regression line

• show_line_CI: * Boolean; defaults to FALSE; set to TRUE to show the confidence interval on the regression line

• show_PI: * Boolean; defaults to FALSE; set to TRUE to show prediction intervals

• show_residuals: * Boolean; defaults to FALSE; set to TRUE to show residuals of prediction

• show_mean_lines: * Boolean; defaults to FALSE; set to TRUE to plot lines showing the mean of each variable

• show_r: * Boolean; defaults to FALSE; set to TRUE to print the r

   value and its CI on the plot
  

• predict_from_x: * Optional real number in the range of the x variable for the plot; Defaults to NULL; if passed, the graph shows the predicted Y' for this x value

• plot_as_z: * Boolean; defaults to FALSE; set to TRUE to convert x and y scores to z scores prior to plotting

• 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)
)