meanDiff.multi
meanDiff.multi
The meanDiff.multi function compares many means for many groups. It presents the results in a dataframe summarizing all relevant information, and produces plot showing the confidence intervals for the effect sizes for each predictor (i.e. dichotomous variable). Like meanDiff, it computes Cohen's d, the unbiased estimate of Cohen's d (Hedges' g), and performs a ttest. It also shows the achieved power, and, more usefully, the power to detect small, medium, and large effects.
 Keywords
 utilities
Usage
meanDiff.multi(dat, y, x=NULL, var.equal = "yes", conf.level = .95, digits = 2, orientation = "vertical", zeroLineColor = "grey", zeroLineSize = 1.2, envir = parent.frame())
Arguments
 dat
 The dataframe containing the variables involved in the mean tests.
 y
 Character vector containing the list of interval variables to include in the tests.
 x
 Character vector containing the list of the dichotomous variables to include in the tests. If x is empty, paired samples ttests will be conducted.
 var.equal
 String; only relevant if x & y are independent; can be "test" (default; test whether x & y have different variances), "no" (assume x & y have different variances; see the Warning below!), or "yes" (assume x & y have the same variance)
 conf.level
 Confidence of confidence intervals you want.
 digits
 With what precision you want the results to print.
 orientation
 Whether to plot the effect size confidence intervals vertically (like a forest plot, the default) or horizontally.
 zeroLineColor
 Color of the horizontal line at an effect size of 0 (set to 'white' to not display the line; also adjust the size to 0 then).
 zeroLineSize
 Size of the horizontal line at an effect size of 0 (set to 0 to not display the line; also adjust the color to 'white' then).
 envir
 The environment where to search for the variables (useful when calling meanDiff from a function where the vectors are defined in that functions environment).
Details
This function uses the meanDiff function, which uses the formulae from Borenstein, Hedges, Higgins & Rothstein (2009) (pages 2532).
Value

An object is returned with the following elements:
Warning
Note that when different variances are assumed for the ttest (i.e. the nullhypothesis test), the values of Cohen's d are still based on the assumption that the variance is equal. In this case, the confidence interval might, for example, not contain zero even though the NHST has a nonsignificant pvalue (the reverse can probably happen, too).
References
Borenstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2011). Introduction to metaanalysis. John Wiley & Sons.
Examples
### Create simple dataset
dat < data.frame(x1 = factor(rep(c(0,1), 20)),
x2 = factor(c(rep(0, 20), rep(1, 20))),
y=rep(c(4,5), 20) + rnorm(40));
### Compute mean difference and show it
meanDiff.multi(dat, x=c('x1', 'x2'), y='y', var.equal="yes");