mdplot: Visualize MD.

In MKinfer: Inferential Statistics

Visualize MD.

Description

The function visualizes the mean difference (MD) of two groups, assuming two normal distributions. In addition, the sensitivity and specificity based on the optimal cutoff (Youden's J statistic) can be added.

Usage

mdplot(delta, sd1 = 1, sd2 = 1, constant = 3, n = 501, alpha = 0.25, 
        lwd = 1, col1 = "darkblue", col2 = "darkred", xlim, 
        addSens = TRUE, digits = 3)

Arguments

delta	(absolute) mean difference of the two groups.
sd1	standard deviation of first group.
sd2	standard deviation of second group.
constant	used to define the plot range (constant*sd).
n	integer; the number of values at which the two normal densities are evaluated.
alpha	opacity used for geom_area.
lwd	linewidth of the plotted lines.
col1	color of density of first group.
col2	color of density of second group.
xlim	limits of x-axis. If missing, calculated based on constant and the specified SDs.
addSens	logical; should sensitivity and specificity be added to the plot.
digits	number of digits used for sensitivity and specificity.

Details

The function can be used to demonstrate (standardized) effect sizes and how these effect sizes transform into sensitivities and specificities.

The two standard diviations are assumed to be equal if their difference is smaller than 1e-08.

Value

ggplot object

Author(s)

Matthias Kohl [e-mail]

References

B\"ohning D, B\"ohning W, Holling H (2008). Revisiting Youden's index as a useful measure of the misclassification error in meta-analysis of diagnostic studies. Stat Methods Med Res. 17(6), 543-54.

J.H. Zhang, T.D.Y. Chung and K.R. Oldenburg (1999). A simple statistical parameter for use in evaluation and validation of high throughput screening assays. Journal of Biomolecular Screening 4 (2): 67-73.

Examples

## small effect size
mdplot(delta = 0.2)
## medium effect size
mdplot(delta = 0.5)
## large effect size
mdplot(delta = 0.8)
## z-factor = 0  (z-factor = 1 - 3*2*sd/delta); see Zhang et al. (1999)
mdplot(delta = 6)
## z-factor = 0.5  (z-factor = 1 - 3*2*sd/delta); see Zhang et al. (1999)
mdplot(delta = 12)

## unequal variances
mdplot(delta = 0.8, sd1 = 1, sd2 = 2)
mdplot(delta = 0.8, sd1 = 2, sd2 = 1)

MKinfer documentation built on Dec. 15, 2025, 9:06 a.m.