1.1 MCCI computation

To compute the MCCI for moderation or equivalence test, we need the following information - d1: The estimated effect size for study 1. - se1: The standard error of the estimate for study 2. - d2: The estimated effect size for study 1. - se2: The standard error of the estimate for study 2.

1.2 Plot

The function also provide a plot of the MCCI by default. Arguments are available to adjust the appearance of the plot. See the function documentation for details.

1.3 Examples

 library(anomo)
myci <- mcci(d1 = .1, se1 = .1, d2 = .3, se2 = .1)

## [1] "The 95% MCCI for Moderation is c(-0.0822,0.4771)."
## [1] "The 90% MCCI for Equivalence Test is c(-0.0309,0.4297)."

# Note. Effect difference (the black square representing d2 - d1), 90% MCCI (the thick horizontal line) for the test of equivalence, and 95% MCCI (the thin horizontal line) for the test of moderation (or difference in effects).

# Adjust the plot
myci <- mcci(d1 = .1, se1 = .1, d2 = .4, se2 = .1,
             bound.eq = c(-0.1, 0.2), xlim = c(-.2, .7))

## [1] "The 95% MCCI for Moderation is c(0.0208,0.5792)."
## [1] "The 90% MCCI for Equivalence Test is c(0.0696,0.5307)."