[R-sig-ME] blme optimizer warnings
Viechtbauer, Wolfgang (SP)
wo||g@ng@v|echtb@uer @end|ng |rom m@@@tr|chtun|ver@|ty@n|
Thu May 14 09:22:08 CEST 2020
A while ago, I looked at this article and replicated the results with metafor.
meta$Variance <- 1/meta$Precision
res <- rma.mv(g, Variance, random = list(~ 1 | Study/Outcome, ~ 1 | Subscale), data=meta)
The variances given in the article are quite heavily rounded and one value is even given as 0. With meta$Variance <- 1/meta$Precision, we can recreate the variances to avoid this issue.
>From: R-sig-mixed-models [mailto:r-sig-mixed-models-bounces using r-project.org]
>On Behalf Of Sijia Huang
>Sent: Thursday, 14 May, 2020 4:58
>To: Vincent Dorie
>Subject: Re: [R-sig-ME] blme optimizer warnings
>Thank you so much, Vincent!
>On Wed, May 13, 2020 at 7:54 PM Vincent Dorie <vdorie using gmail.com> wrote:
>> A couple of guesses here in addition to what Ben mentioned, but you
>> likely don't want a prior on the covariance of the random effects and
>> the weights should be on the scale of inverse variances. The following
>> replicates the numbers for the CCREM column from table 1:
>> blmer(g ~ 1 + (1 | Study) + (1 | Subscale) + (1 | Outcome:Study:Subscale),
>> data = meta, weights = Precision, control =
>> lmerControl(optimizer = "bobyqa"),
>> resid.prior = point(1), cov.prior = NULL)
>> On Wed, May 13, 2020 at 10:04 PM Sijia Huang <huangsjcc using gmail.com> wrote:
>> > Here it is. Thanks!
>> > A demonstration and evaluation of the use of cross-classified
>> > random-effects models for meta-analysis
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