MKinfer (version 1.3)

md2zfactor: 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)

Value

Numeric vector with sensitivity and specificity.

Arguments

delta

(absolute) mean difference of the two groups.

sd1

standard deviation of first group.

sd2

standard deviation of second group.

Author

Matthias Kohl

Details

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

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")