homoStat: Test the Homogeneity of Effect Sizes

Description

It tests the homogeneity of univariate and multivariate effect sizes.

Usage

homoStat(y, v)

Arguments

A vector of effect size for univariate meta-analysis or a \(k\) x \(p\) matrix of effect sizes for multivariate meta-analysis where \(k\) is the number of studies and \(p\) is the number of effect sizes.

A vector of the sampling variance of the effect size for univariate meta-analysis or a \(k\) x \(p*\) matrix of the sampling covariance matrix of the effect sizes for multivariate meta-analysis where \(p* = p(p+1)/2 \). It is arranged by column major as used by vech. It is assumed that there is no missing value in v if y is complete. If there are missing values in v due to the missingness on y, the missing values in v will be removed automatically.

Value

A list of

Q statistic on the null hypothesis of homogeneity of effect sizes. It has an approximate chi-square distribution under the null hypothesis.

Q.df

Degrees of freedom of the Q statistic

pval

p-value on the test of homogeneity of effect sizes

References

Becker, B. J. (1992). Using results from replicated studies to estimate linear models. Journal of Educational Statistics, 17, 341-362.

Cheung, M. W.-L. (2010). Fixed-effects meta-analyses as multiple-group structural equation models. Structural Equation Modeling, 17, 481-509.

Cochran, W. G. (1954). The combination of estimates from different experiments. Biometrics, 10, 101-129.

Examples

Run this code

# NOT RUN {
with( Hox02, homoStat(yi, vi) )

with( HedgesOlkin85, homoStat(y=cbind(d_att, d_ach),
      v=cbind(var_att, cov_att_ach, var_ach)) )
# }