[R-sig-eco] Regression when Y has an estimation variance

[Torsten Hauffe]

Bayesian mixed models implemented in MCMCglmm and brms have an argument to
specify the variances for meta analysis. I never used them for this task,
though. However, the author of brms is psychologist where meta analyses are
common.

Cheers!

On Mon, 14 Jan 2019 at 15:52, Dixon, Philip M [STAT] <pdixon using iastate.edu>
wrote:

> Roy,
>
> One relevant literature is that on meta-regression (a generalization of
> meta-analysis).  There is a very good handbook by Koricheva, Gurevitch and
> Mengerson.  Meta analysis mostly deals with Gaussian responses (or
> transformable to approximately Gaussian).  If there has been any work on
> non-gaussian responses, I expect it would be summarized in Koricheva.
>
> The metafor package is one implementation specifically for meta analysis.
> A resource on metafor and other R packages is Schwarzer and Carpenter,
> Meta-analysis with R.
>
> Other programs can also fit the models as mixed models with heterogenerous
> specified variances, lme() and lmer() do not let you do this.  lmer()
> doesn't allow heterogeneous variances; lme() does, but only of the form
> k_i*sigma^2 (i.e. variances relative to a common scaling factor, not
> absolutely specified variances).   For a meta analysis, you need to specify
> the absolute variance for each estimate.  If someone knows how to trick
> lme() to use exactly the error variances that have been specified, I would
> love to hear about it.
>
> Best,
> Philip Dixon
>
> _______________________________________________
> R-sig-ecology mailing list
> R-sig-ecology using r-project.org
> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
>

	[[alternative HTML version deleted]]