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.

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`y ` |
A |

`v ` |
A |

`name.y ` |
A |

`name.study ` |
A |

`y.all ` |
A |

`y.all.se ` |
A |

`hline ` |
A |

`up.bound ` |
A |

`low.bound ` |
A |

`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.

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######################################################
# 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
``` |

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