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anomo

Zuchao Shen

This package implements the Monte Carlo confidence interval (MCCI) method to test (a) the significance of moderation effects and (b) the significance of equivalence test. The main function is the MCCI function.

1. Key Arguments in the MCCI Function

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(-0.0768,0.4862)."
## [1] "The 90% MCCI for Equivalence Test is c(-0.0299,0.4373)."

# 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(0.022,0.5725)."
## [1] "The 90% MCCI for Equivalence Test is c(0.0703,0.5292)."

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.