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<p>That's neat! LRT and AIC favor the first model, while BIC is
somewhat ambigious (probably given complexity). In that case, I'll
probably stick with the first model. Thanks again for your help,
Wolfgang and James! <br>
</p>
<div class="moz-cite-prefix">Am 13.12.2018 um 17:41 schrieb
Viechtbauer, Wolfgang (SP):<br>
</div>
<blockquote type="cite"
cite="mid:53c693a7226549f582952ff868734eba@UM-MAIL3214.unimaas.nl">
<pre class="moz-quote-pre" wrap="">First: I forgot the 'struct="UN"' part, so:
res1 <- rma.mv(yi, dat$V, mods = ~ var1var2 - 1, random = list(~ var1var2 | id, ~ 1 | study/id), struct="UN", data=dat$dat)
One could consider various models here. Another one would indeed be:
res2 <- rma.mv(yi, dat$V, mods = ~ var1var2 - 1, random = list(~ var1var2 | study, ~ var1var2 | id), struct=c("UN","UN"), data=dat$dat)
('~ var1var2 | study/id' does not currently work, but if 'id' is coded in the way I show below, then this is the same thing).
As James mentioned, one could use LRTs to compare such models:
anova(res1, res2)
Best,
Wolfgang
</pre>
<blockquote type="cite">
<pre class="moz-quote-pre" wrap="">-----Original Message-----
From: Anna-Lena Schubert [<a class="moz-txt-link-freetext" href="mailto:anna-lena.schubert@psychologie.uni">mailto:anna-lena.schubert@psychologie.uni</a>-
heidelberg.de]
Sent: Thursday, 13 December, 2018 17:25
To: Viechtbauer, Wolfgang (SP); <a class="moz-txt-link-abbreviated" href="mailto:r-sig-meta-analysis@r-project.org">r-sig-meta-analysis@r-project.org</a>
Subject: Re: [R-meta] Quick question about multiple independent samples
within one study
Yes, that's exactly what my design looks like -- and all levels are
incompletely crossed because there are only few studies with additional
samples or additional measures. Thanks again for the prompt response and
the great help! My final question (so I don't have to post again if I run
into the same situation in the future) would be why the random effect for
var1var2 only contains "id" and not "study/id"?
Best,
Anna-Lena
Am 13.12.2018 um 17:06 schrieb Viechtbauer, Wolfgang (SP):
Ok, so if I understand (and remember correctly), then you will have two
rows for each level of 'id', one for the correlation cor(x1, y) and one
for the correlation cor(x2, y). And now you also have studies with
multiple (independent) samples. So, for example:
study id var1var2 yi
-------------------------
1 1 var1 .
1 1 var2 .
2 2 var1 .
2 2 var2 .
2 3 var1 .
2 3 var2 .
3 4 var1 .
3 4 var2 .
So, study 2 has 2 samples and hence 4 rows. That would actually be a
model with an additional level beyond what Konstantopoulos describes. So
you have studies, samples within studies, and then two estimates within
samples. Then I would go with:
res <- rma.mv(yi, dat$V, mods = ~ var1var2 - 1, random = list(~ var1var2
| id, ~ 1 | study/id), data=dat$dat)
Best,
Wolfgang
</pre>
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