md2zfactor: Compute z-Factor based on MD.

In MKinfer: Inferential Statistics

Compute z-Factor based on MD.

Description

The function calculates the z-factor for a given mean difference (MD) and specified standard deviations of the two groups.

Usage

md2zfactor(delta, sd1 = 1, sd2 = 1)

Arguments

delta	(absolute) mean difference of the two groups.
sd1	standard deviation of first group.
sd2	standard deviation of second group.

Details

This function can be used to demonstrate how (standardized) effect sizes translate into z-factor, based on two normal distributions.

Value

Numeric vector with sensitivity and specificity.

Author(s)

Matthias Kohl [e-mail]

References

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

## (standardized) mean difference to sensitivity/specificity
## equal variances
library(ggplot2)
delta <- seq(from = 2, to = 18, by = 0.05)
res <- sapply(delta, md2zfactor)
DF <- data.frame(SMD = delta, zfactor = res)
ggplot(DF, aes(x = SMD, y = zfactor)) +
  geom_line() + xlab("(standardized) mean difference") +
  ylab("z-factor") + ggtitle("SD1 = SD2 = 1") + 
  geom_hline(yintercept = 1, linetype = "dotted")

## unequal variances
delta <- seq(from = 2.5, to = 20, by = 0.05)
res <- sapply(delta, md2zfactor, sd1 = 1, sd2 = 2)
DF <- data.frame(MD = delta, zfactor = res)
ggplot(DF, aes(x = MD, y = zfactor)) +
  geom_line() + xlab("mean difference") +
  ylab("z-factor") + ggtitle("SD1 = 1, SD2 = 2") +
  geom_hline(yintercept = 1, linetype = "dotted")

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