vcov.rma: Extract Various Types of Variance-Covariance Matrices from...

In metafor: Meta-Analysis Package for R

Extract Various Types of Variance-Covariance Matrices from 'rma' Objects

Description

Function to extract various types of variance-covariance matrices from objects of class "rma". By default, the variance-covariance matrix of the fixed effects is returned. \loadmathjax

Usage

## S3 method for class 'rma'
vcov(object, type="fixed", ...)

Arguments

object	an object of class "rma".
type	character string to specify the type of variance-covariance matrix to return: type="fixed" returns the variance-covariance matrix of the fixed effects (the default), type="obs" returns the marginal variance-covariance matrix of the observed effect sizes or outcomes, type="fitted" returns the variance-covariance matrix of the fitted values, type="resid" returns the variance-covariance matrix of the residuals.
...	other arguments.

Details

Note that type="obs" currently only works for object of class "rma.uni" and "rma.mv".

For objects of class "rma.uni", the marginal variance-covariance matrix of the observed effect sizes or outcomes is just a diagonal matrix with \mjeqn\hat\tau^2 + v_i\tau^2 + v_i along the diagonal, where \mjeqn\hat\tau^2\tau^2 is the estimated amount of (residual) heterogeneity (set to 0 in equal-effects models) and \mjseqnv_i is the sampling variance of the \mjeqni\textrmthith study.

For objects of class "rma.mv", the structure can be more complex and depends on the random effects included in the model.

Value

A matrix corresponding to the requested variance-covariance matrix.

Author(s)

Wolfgang Viechtbauer [e-mail] https://www.metafor-project.org

References

Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48. ⁠https://doi.org/10.18637/jss.v036.i03⁠

See Also

rma.uni, rma.mh, rma.peto, rma.glmm, and rma.mv for functions to fit models for which the various types of variance-covariance matrices can be extracted.

Examples

### calculate log risk ratios and corresponding sampling variances
dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg)

### fit mixed-effects model with absolute latitude and publication year as moderators
res <- rma(yi, vi, mods = ~ ablat + year, data=dat)

### var-cov matrix of the fixed effects (i.e., the model coefficients)
vcov(res)

### marginal var-cov matrix of the observed log risk ratios
round(vcov(res, type="obs"), 3)

### var-cov matrix of the fitted values
round(vcov(res, type="fitted"), 3)

### var-cov matrix of the residuals
round(vcov(res, type="resid"), 3)

metafor documentation built on May 29, 2024, 7:44 a.m.