Description Usage Format Source See Also Examples

It includes two datasets in multiple-treatment studies and multiple-endpoint studies reported by Gleser and Olkin (1994).

1 | ```
data("Gleser94")
``` |

A list of two data frames.

`MTS`

A data frame of multiple-treatment studies.

`MES`

A data frame of multiple-endpoint studies.

Gleser, L. J., & Olkin, I. (1994). Stochastically dependent effect sizes. In H. Cooper & L. V. Hedges (Eds.), The handbook of research synthesis. (pp. 339-355). 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 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | ```
## Not run:
data(Gleser94)
#### Multiple-treatment studies
Gleser94$MTS
## Assuming homogeneity of variances
my.MTS <- t(apply(Gleser94$MTS, MARGIN=1,
function(x)
smdMTS(m=x[c("Mean.C", "Mean.E1", "Mean.E2", "Mean.E3", "Mean.E4", "Mean.E5")],
v=x[c("SD.C", "SD.E1", "SD.E2", "SD.E3", "SD.E4", "SD.E5")]^2,
n=x[c("N.C", "N.E1", "N.E2", "N.E3", "N.E4", "N.E5")],
homogeneity="variance", list.output=FALSE)))
## Fixed-effects multivariate meta-analysis
fit.MTS <- meta(y=my.MTS[, 1:5],
v=my.MTS[, 6:20],
RE.constraints = diag(0, ncol=5, nrow=5),
model.name="MTS")
summary(fit.MTS)
#### Multiple-endpoint studies
Gleser94$MES
## Calculate the sampling variances and covariance and amend into the data set
Gleser94$MES$Uncoached.V11 <- with(Gleser94$MES, SD.Uncoached.Math^2)
Gleser94$MES$Uncoached.V21 <- with(Gleser94$MES,
SD.Uncoached.Math*Cor.Math.Verbal*SD.Uncoached.Verbal)
Gleser94$MES$Uncoached.V22 <- with(Gleser94$MES, SD.Uncoached.Verbal^2)
Gleser94$MES$Coached.V11 <- with(Gleser94$MES, SD.Coached.Math^2)
Gleser94$MES$Coached.V21 <- with(Gleser94$MES,
SD.Coached.Math*Cor.Math.Verbal*SD.Coached.Verbal)
Gleser94$MES$Coached.V22 <- with(Gleser94$MES, SD.Coached.Verbal^2)
## Assuming homogeneity of covariance matrices
my.MES <- t(apply(Gleser94$MES, MARGIN=1,
function(x)
smdMES(m1=x[c("Mean.Uncoached.Math", "Mean.Uncoached.Verbal")],
m2=x[c("Mean.Coached.Math", "Mean.Coached.Verbal")],
V1=vec2symMat(x[c("Uncoached.V11", "Uncoached.V21", "Uncoached.V22")]),
V2=vec2symMat(x[c("Coached.V11", "Coached.V21", "Coached.V22")]),
n1=x["N.Uncoached"],
n2=x["N.Coached"],
homogeneity="covariance", list.output=FALSE)))
## Fixed-effects multivariate meta-analysis
fit.MES <- meta(y=my.MES[, 1:2],
v=my.MES[, 3:5],
RE.constraints = diag(0, ncol=2, nrow=2),
model.name="MES")
summary(fit.MES)
## End(Not run)
``` |

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