smdMES | R Documentation |
It computes the standardized mean differences and their asymptotic sampling covariance matrix for two multiple end-point studies with p effect sizes.
smdMES(m1, m2, V1, V2, n1, n2,
homogeneity=c("covariance", "correlation", "none"),
bias.adjust=TRUE, list.output=TRUE, lavaan.output=FALSE)
m1 |
A vector of p sample means of the first group. |
m2 |
A vector of p sample means of the second group. |
V1 |
A p by p sample covariance matrix of the first group. |
V2 |
A p by p sample covariance matrix of the second group. |
n1 |
The sample size of the first group. |
n2 |
The sample size of the second group. |
homogeneity |
If it is |
bias.adjust |
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 end-point 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 two ways: (1)
it allows homogeneity of covariance or correlation matrices or not; and
(2) it allows users to test this assumption by checking the fitted
lavaan-class
object.
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
, smdMTS
, calEffSizes
## Sample means for the two constructs in Group 1
m1 <- c(2.5, 4.5)
## Sample means for the two constructs in Group 2
m2 <- c(3, 5)
## Sample covariance matrix in Group 1
V1 <- matrix(c(3,2,2,3), ncol=2)
## Sample covariance matrix in Group 2
V2 <- matrix(c(3.5,2.1,2.1,3.5), ncol=2)
## Sample size in Group 1
n1 <- 20
## Sample size in Group 2
n2 <- 25
## SMD with the assumption of homogeneity of covariance matrix
smdMES(m1, m2, V1, V2, n1, n2, homogeneity="cov", bias.adjust=TRUE,
lavaan.output=FALSE)
## SMD with the assumption of homogeneity of correlation matrix
smdMES(m1, m2, V1, V2, n1, n2, homogeneity="cor", bias.adjust=TRUE,
lavaan.output=FALSE)
## SMD without any assumption of homogeneity
smdMES(m1, m2, V1, V2, n1, n2, homogeneity="none", bias.adjust=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 <- smdMES(m1, m2, V1, V2, n1, n2, homogeneity="cov", bias.adjust=TRUE,
lavaan.output=TRUE)
lavaan::summary(fit)
lavaan::parameterestimates(fit)
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