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

P. Roberto Bakker robertobakker at gmail.com
Wed Oct 18 23:13:16 CEST 2017

```It worked! Thanks a lot.
Roberto

2017-10-18 22:53 GMT+02:00 Ken Beath <ken at kjbeath.com.au>:

> Just naming teh passed argument is’t sufficient. What is needed is sei=sei
> for sei and vi=vi for vi, as for the other parameters.
>
> Ken
>
>
> > On 19 Oct 2017, at 7:44 am, 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 *
> >
> >       [[alternative HTML version deleted]]
> >
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> > R-sig-meta-analysis at r-project.org
> > https://stat.ethz.ch/mailman/listinfo/r-sig-meta-analysis
>
>

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