smdMTS | R Documentation |
It computes the standardized mean differences and their asymptotic sampling covariance matrix for k multiple treatment studies. The first group is assumed as the control group.
smdMTS(m, v, n, homogeneity=c("variance", "none"), bias.adjust=TRUE,
all.comparisons=FALSE, list.output=TRUE, lavaan.output=FALSE)
m |
A vector of k sample means. |
v |
A vector of k sample variances. |
n |
A vector of k sample sizes. |
homogeneity |
If it is |
bias.adjust |
If it is |
all.comparisons |
If it is |
list.output |
If it is |
lavaan.output |
If it is |
Gleser and Olkin (2009) introduce formulas to calculate the
standardized mean differences and their sampling covariance matrix for
multiple treatment studies under the assumption of homogeneity of the
covariance matrix. This function uses a structural equation modeling (SEM)
approach introduced in Chapter 3 of Cheung (2015) to calculate the
same estimates. The SEM approach is more flexible in three ways: (1)
it allows homogeneity of variances or not; (2) it allows users to test
the assumption of homogeneity of variances by checking the fitted
lavaan-class
object; and (3) it may calculate all
pairwise comparisons.
Mike W.-L. Cheung <mikewlcheung@nus.edu.sg>
Cheung, M. W.-L. (2015). Meta-analysis: A structural equation modeling approach. Chichester, West Sussex: John Wiley & Sons, Inc.
Cheung, M. W.-L. (2018). Computing multivariate effect sizes and their sampling covariance matrices with structural equation modeling: Theory, examples, and computer simulations. Frontiers in Psychology, 9(1387). https://doi.org/10.3389/fpsyg.2018.01387
Gleser, L. J., & Olkin, I. (2009). Stochastically dependent effect sizes. In H. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), The handbook of research synthesis and meta-analysis. (2nd ed., pp. 357-376). New York: Russell Sage Foundation.
Gleser94
, smdMES
, calEffSizes
## Sample means for groups 1 to 3
m <- c(5,7,9)
## Sample variances
v <- c(10,11,12)
## Sample sizes
n <- c(50,52,53)
## Assuming homogeneity of variances
smdMTS(m, v, n, homogeneity = "var", bias.adjust=TRUE, all.comparisons=FALSE,
lavaan.output=FALSE)
## Not assuming homogeneity of variances and comparing all pairwise groups
## Please note that the SD of the first group is used as the standardizer
smdMTS(m, v, n, homogeneity = "none", bias.adjust=TRUE, all.comparisons=TRUE,
lavaan.output=FALSE)
## Output the fitted lavaan model
## It provides a likelihood ratio test to test the null hypothesis of
## homogeneity of variances.
fit <- smdMTS(m, v, n, homogeneity = "var", bias.adjust=FALSE, all.comparisons=FALSE,
lavaan.output=TRUE)
lavaan::summary(fit)
lavaan::parameterestimates(fit)
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