mmm {CIAAWconsensus} | R Documentation |
This function provides meta-analysis of multivariate correlated data using the marginal method of moments with working independence assumption as described by Chen et al (2016). As such, the meta-analysis does not require correlations between the outcomes within each dataset.
mmm(y, uy, knha = TRUE, verbose = TRUE)
y |
A matrix of results from each of the |
uy |
A matrix with uncertainties of the results given in |
knha |
(Logical) Allows for the adjustment of consensus uncertainties using the Birge ratio (Knapp-Hartung adjustment) |
verbose |
(Logical) Requests annotated summary output of the results |
The marginal method of moments delivers the inference for correlated effect sizes using multiple univariate meta-analyses.
studies |
The number of independent studies |
beta |
The consensus estimates for all outcomes |
beta.u |
Standard uncertainties of the consensus estimates |
beta.U95 |
Expanded uncertainties of the consensus estimates corresponding to 95% confidence |
beta.cov |
Covariance matrix of the consensus estimates |
beta.cor |
Correlation matrix of the consensus estimates |
H |
Birge ratios (Knapp-Hartung adjustment) which were applied to adjust the standard uncertainties of each consensus outcome |
I2 |
Relative total variability due to heterogeneity (in percent) for each outcome |
Juris Meija <juris.meija@nrc-cnrc.gc.ca> and Antonio Possolo
Y. Chen, Y. Cai, C. Hong, and D. Jackson (2016) Inference for correlated effect sizes using multiple univariate meta-analyses. Statistics in Medicine, 35, 1405-1422
J. Meija and A. Possolo (2017) Data reduction framework for standard atomic weights and isotopic compositions of the elements. Metrologia, 54, 229-238
## Consensus isotope amount ratios for platinum df=normalize.ratios(platinum.data, "platinum", "195Pt") mmm(df$R, df$u.R)