Description Usage Arguments Details Author(s) References Examples
The function plot.CI
generates confidence interval figures for effect sizes from each study and the estimated effect sizes across studies.
1 2 3 4 5 6 7 |
y |
A N x p matrix or data frame that stores effect sizes from N primary studies. Usually the output value |
v |
A N-dimensional list of p x p matrices that stores within-study (co)variance matrices of the estimated effect sizes for each one of the N studies. Usually the output value |
name.y |
A p-dimensional vector that stores names for the effect sizes in |
name.study |
A N-dimensional vector that stores names for the primary in |
y.all |
A p-dimensional vector that stores the estimated effect sizes across studies. |
y.all.se |
A p-dimensional vector that stores the standard errors for the estimated effect sizes in |
hline |
A p-dimensional vector that specifies the position of the dash line in the figures to compare the coefficients across studies. If its length is one instead of p, this number will be adopted for all the p effect sizes. |
up.bound |
A p-dimensional vector that specifies the upper bound in the figures. If its length is one instead of p, this number will be adopted for all the p effect sizes. |
low.bound |
A p-dimensional vector that specifies the lower bound in the figures. If its length is one instead of p, this number will be adopted for all the p effect sizes. |
return.data |
Should the data for the confidence interval plots be returned? |
The difference between a forest plot and a confidence interval plot is that a forest plot requires a symbol on each confidence interval that is proportional to the weight for each study. Because the weighting mechanism in multivariate meta-analysis is too complex to be visualized, such a propositional symbol is omitted for multivariate meta-analysis.
Min Lu
Ahn, S., Lu, M., Lefevor, G.T., Fedewa, A. & Celimli, S. (2016). Application of meta-analysis in sport and exercise science. In N. Ntoumanis, & N. Myers (Eds.), An Introduction to Intermediate and Advanced Statistical Analyses for Sport and Exercise Scientists (pp.233-253). Hoboken, NJ: John Wiley and Sons, Ltd.
Cooper, H., Hedges, L.V., & Valentine, J.C. (Eds.) (2009). The handbook of research synthesis and meta-analysis. New York: Russell Sage Foundation.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | ######################################################
# Example: Craft2003 data
######################################################
data(Craft2003)
computvcov <- r.vcov(n = Craft2003$N,
corflat = subset(Craft2003, select = C1:C6),
method = "average")
y <- computvcov$ef
Slist <- computvcov$list.vcov
MMA_FE <- summary(metafixed(y = y, Slist = Slist))
obj <- MMA_FE
# pdf("CI.pdf", width = 4, height = 7)
plotCI(y = computvcov$ef, v = computvcov$list.vcov,
name.y = NULL, name.study = Craft2003$ID,
y.all = obj$coefficients[,1],
y.all.se = obj$coefficients[,2])
# dev.off()
######################################################
# Substitute obj for Random-effect model
######################################################
# library(mvmeta)
# S <- computvcov$matrix.vcov
# MMA_RE <- summary(mvmeta(cbind(C1, C2, C3, C4, C5, C6),
# S = S, data = y, method = "reml"))
# obj <- MMA_RE
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.