[R-meta] Weight argument in rma.uni models

[Tina Dudenhöffer]

Dear Wolfgang,

thanks for your quick response.  I, indeed, mean rma.mv()! 

Considering the default is the inverse variance weight, would you suggest I stick to my res5 version? 
> res5 <- rma.mv(EffectSize_NEW, Variance_New, method="REML",random = list(~ 1 | EffectSize_ID_NEW, ~ 1 | Study), data=Data)
> res5


I saw somewhere else the argument “weighted=TRUE” added - but when looking the documentation, I don’t see it. I suppose it’s not necessary or at least not in my case?


Thank you so much!

Tina



> On Oct 2, 2019, at 2:49 PM, Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
> 
> Dear Tina,
> 
> I assume you mean the rma.mv() function, not rma.uni().
> 
> If you don't specify W, then weights are applied automatically. See:
> 
> https://wviechtb.github.io/metafor/reference/rma.mv.html
> 
> and search for "Together with the variance-covariance matrix of the sampling errors". However, for the model you show, the model implied marginal variance-covariance matrix of the observed outcomes (M) is not diagonal, but also has off-diagonal elements. So, there is actually an entire weight matrix.
> 
> Also, in 'res5', M (and hence the weight matrix W = M^(-1)) will incorporate the estimates of the two variance components (for EffectSize_ID_NEW and Study), while 'res6' only uses 1/Variance_New as the weights and doesn't consider the two variance components.
> 
> Best,
> Wolfgang
> 
> -----Original Message-----
> From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org] On Behalf Of Tina Dudenhöffer
> Sent: Wednesday, 02 October, 2019 13:48
> To: r-sig-meta-analysis using r-project.org
> Subject: [R-meta] Weight argument in rma.uni models
> 
> Dear all,
> 
> I am working with the rma.uni function to fit a multivariate random effects model.
> 
> I see that the W argument is an optional argument to specify a user-defined weight matrix. I was curious if any weight is applied if I don’t specify this argument. 
> Specifically, I am wondering if the inverse variance weight is applied “automatically”, considering I am specifying the V argument in the model itself.
> 
> In terms of my model, I am wondering about the difference between these:
> 
> res5 <- rma.mv(EffectSize_NEW, Variance_New, method="REML",random = list(~ 1 | EffectSize_ID_NEW, ~ 1 | Study), data=Data)
> res5
> 
> res6 <- rma.mv(EffectSize_NEW, Variance_New, W=weight, method="REML",random = list(~ 1 | EffectSize_ID_NEW, ~ 1 | Study), data=Data)
> res6
> 
> Note: the argument weight in my dataset is simply: 1/Variance_New
> 
> Hope this is all clear.
> 
> Many thanks and kind regards,
> Tina