meanDiff
meanDiff
The meanDiff function compares the means between two groups. 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(x, y=NULL, paired = FALSE, r.prepost = NULL, var.equal = "test", conf.level = .95, plot = FALSE, digits = 2, envir = parent.frame())
Arguments
 x
 Dichotomous factor: variable 1; can also be a formula of the form y ~ x, where x must be a factor with two levels (i.e. dichotomous).
 y
 Numeric vector: variable 2; can be empty if x is a formula.
 paired
 Boolean; are x & y independent or dependent? Note that if x & y are dependent, they need to have the same length.
 r.prepost
 Correlation between the pre and posttest in the case of a paired samples ttest. This is required to compute Cohen's d using the formula on page 29 of Borenstein et al. (2009). If NULL, the correlation is simply computed from the provided scores (but of course it will then be lower if these is an effect  this will lead to an underestimate of the withingroups variance, and therefore, of the standard error of Cohen's d, and therefore, to confidence intervals that are too narrow (too liberal). Also, of course, when using this data to compute the withingroups correlation, random variations will also impact that correlation, which means that confidence intervals may in practice deviate from the null hypothesis significance testing pvalue in either direction (i.e. the pvalue may indicate a significant association while the confidence interval contains 0, or the other way around). Therefore, if the testretest correlation of the relevant measure is known, please provide this here to enable computation of accurate confidence intervals.
 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.
 plot
 Whether to print a dlvPlot.
 digits
 With what precision you want the results to print.
 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 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 < PlantGrowth[1:20,];
### Remove third level from group factor
dat$group < factor(dat$group);
### Compute mean difference and show it
meanDiff(dat$weight ~ dat$group);
### Look at second treatment
dat < rbind(PlantGrowth[1:10,], PlantGrowth[21:30,]);
### Remove third level from group factor
dat$group < factor(dat$group);
### Compute mean difference and show it
meanDiff(x=dat$group, y=dat$weight);