[R-meta] vi versus sei in rma() give different results

[A C Del Re]

You just need to specify in the arguments whether it's sei or vi, eg,

# vi
res <- rma(measure="SMD", yi, vi=vi, data=re, method="FE", digits=2)

#sei
res <- rma(measure="SMD", yi, sei=sei, data=re, method="FE", digits=2)


On Wed, Oct 18, 2017 at 1:44 PM, P. Roberto Bakker <robertobakker at gmail.com>
wrote:

> Hi,
>
> I understood from the metafor documentation you can either use vi
> (variance) or sei (standard error (=sqrt of variance) in rma()..
> However, with vi and I get other results than with sei. I would expect the
> same results should be the same.
> Can somebody help me understand this, or am I doing something wrong?
>
> Tnx in advance,
> Roberto
> PS in the meta-analysis literature I read that se = sqrt of variance. But I
> learned from statistics that SD = sqrt of variance. Can somebody explain me
> this?
>
>
> > re
> # A tibble: 10 × 6
>             yi       sei         vi `inverse varience` `weighed hedge`
>   X__1
>          <dbl>     <dbl>      <dbl>              <dbl>           <dbl>
>  <dbl>
> 1   1.91498797 0.4231468 0.17905321           5.584932      10.6950776
> 1.91498797
> 2  -0.25848358 0.2615776 0.06842283          14.615005      -3.7777390
> -0.25848358
> 3   0.06543005 0.3204569 0.10269263           9.737797       0.6371445
> 0.06543005
> 4   0.77363080 0.3290877 0.10829873           9.233718       7.1434890
> 0.77363080
> 5  -0.79554100 0.3341517 0.11165733           8.955973      -7.1248437
> -0.79554100
> 6   0.29374938 0.2387312 0.05699258          17.546145       5.1541691
> 0.29374938
> 7   0.94409401 0.2527629 0.06388910          15.652122      14.7770750
> 0.94409401
> 8  -0.56731857 0.2423818 0.05874893          17.021588      -9.6566629
> -0.56731857
> 9   0.18910949 0.1766278 0.03119739          32.053967       6.0617093
> 0.18910949
> 10  0.38103574 0.1713235 0.02935176          34.069510      12.9817010
> 0.38103574
> >
>
> WITH SEI
> *> reis <- rma(measure="SMD", yi, sei, data=re, method="FE", digits=2)*
> *> reis*
>
> *Fixed-Effects Model (k = 10)*
>
> *Test for Heterogeneity: *
> *Q(df = 9) = 16.54, p-val = 0.06*
>
> *Model Results:*
>
> *estimate    se  zval  pval  ci.lb <http://ci.lb>  ci.ub   *
> *    0.25  0.16  1.54  0.12  -0.07   0.56   *
>
> *---*
> *Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 *
>
>
> *WITH VI*
>
>
>
>
>
> *> res <- rma(measure="SMD", yi, vi, data=re, method="FE", digits=2)>
> resFixed-Effects Model (k = 10)Test for Heterogeneity: Q(df = 9) = 51.46,
> p-val < .01Model Results:estimate    se  zval  pval  ci.lb <http://ci.lb>
> ci.ub        0.22  0.08  2.88  <.01   0.07   0.38  **---Signif. codes:  0
> ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 *
>
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>
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-- 
AC Del Re, PhD
acdelre.weebly.com

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