DMAR (version 1.0.0)

plot_smd: Visualize a Standardized Mean Difference With Overlapping Distributions

Description

Creates a publication-quality plot showing two normal distributions separated by the standardized mean difference (d). The plot includes a confidence interval for the population effect size and sample size annotations, both shown by default.

Usage

plot_smd(
  smd = NULL,
  n_1 = NULL,
  n_2 = NULL,
  group_1 = NULL,
  group_2 = NULL,
  conf_level = 0.95,
  show_ci = TRUE,
  show_n = TRUE,
  title = NULL,
  group_labels = c("Group 1", "Group 2"),
  palette = "okabe_ito",
  colors = NULL
)

Value

A ggplot2 object that can be further customized with standard

ggplot2 layers, scales, and themes.

Arguments

smd

The standardized mean difference (Cohen's d).

n_1

Sample size for Group 1.

n_2

Sample size for Group 2.

group_1

Raw data for Group 1. When provided, smd, n_1, and n_2 are computed from the data.

group_2

Raw data for Group 2.

conf_level

Confidence level for the confidence interval (default 0.95).

show_ci

Logical. If TRUE (the default), a confidence interval for the population standardized mean difference is displayed beneath the distributions. Requires both n_1 and n_2.

show_n

Logical. If TRUE (the default), per-group sample sizes are annotated on the plot.

title

Optional character string for the plot title. Defaults to "Standardized Mean Difference".

group_labels

Character vector of length 2 giving labels for the two groups. Defaults to c("Group 1", "Group 2").

palette

Character string naming the color palette used when colors is NULL. Defaults to "okabe_ito", base R's colorblind-safe Okabe-Ito palette; "tableau" is also available.

colors

Optional character vector of length 2 giving fill colors for the two groups. When NULL (the default), the first two colors of palette are used.

Author

Ken Kelley [email protected]

Details

Two unit-variance normal distributions are drawn, centered at 0 (Group 2 / reference) and d (Group 1 / focal). The semi-transparent fills make the overlap visible, giving a direct visual impression of how much the distributions differ.

When show_ci = TRUE and both n_1 and n_2 are available, the function calls ci_smd to compute the noncentral t based confidence interval and displays it as a horizontal bar beneath the curves. A filled dot marks the point estimate and vertical caps mark the confidence bounds.

References

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.

Kelley, K. (2007). Confidence intervals for standardized effect sizes: Theory, application, and implementation. Journal of Statistical Software, 20(8), 1--24. tools:::Rd_expr_doi("10.18637/jss.v020.i08")

See Also

smd, ci_smd, plot_ci, plot_R2

Other plotting: plot_R2(), plot_cfa_k(), plot_ci(), plot_equivalence(), plot_forest(), plot_irt_information(), plot_mediation_mbco(), plot_randomization_test(), plot_regions_of_significance(), plot_trajectories(), plot_trajectories_fitted(), power_equivalence_md_plot()

Other confidence intervals for effect sizes: ci_R2(), ci_c(), ci_c_ancova(), ci_c_ancova_bp(), ci_correlation, ci_cv(), ci_eta_squared(), ci_eta_squared_generalized(), ci_eta_squared_partial(), ci_mahalanobis(), ci_omega_squared(), ci_pvaf(), ci_rc(), ci_reg_coef(), ci_rmsea(), ci_sc(), ci_sc_ancova(), ci_sm(), ci_smd(), ci_smd_c(), ci_snr(), ci_src(), ci_srsnr(), contrast_adjusted()

Examples

# From a known standardized mean difference and the two sample sizes:
# the two curves, the interval beneath them, and the sample sizes in
# the corner are the default display.
plot_smd(smd = 0.50, n_1 = 50, n_2 = 50)

# From raw data, where the standardized mean difference and both
# sample sizes are taken from the data. The d annotation now reports
# the sample value rather than a supplied one.
set.seed(113)
g1 <- rnorm(40, mean = 0.6, sd = 1)
g2 <- rnorm(40, mean = 0.0, sd = 1)
plot_smd(group_1 = g1, group_2 = g2)

# Without the confidence interval or the sample size annotations, the
# figure is the two curves alone.
plot_smd(smd = 0.80, show_ci = FALSE, show_n = FALSE)

# Group labels and a title of the reader's own.
plot_smd(smd = 0.45, n_1 = 75, n_2 = 75,
         group_labels = c("Treatment", "Control"),
         title = "Treatment Effect on Reading Scores")