[R-meta] Error: Ratio of largest to smallest sampling variance extremely large

[florencia miguel]

Thanks James and Wolfgang.

James, I´ve checked data and I have some very small sampling variances.
BUT, these are not errors, it is the structure of data we are working with.

Wolfgang, I installed that version of metafor, i did get warnings when
running models, is that ok?

Best
Florencia

El jue., 14 de feb. de 2019 a la(s) 19:29, Viechtbauer, Wolfgang (SP) (
wolfgang.viechtbauer using maastrichtuniversity.nl) escribió:

> Hi Florencia,
>
> If you install the 'devel' version of metafor (
> https://wviechtb.github.io/metafor/#installation), then you should get a
> warning but no longer an error. However, the warning is there for a reason;
> the results might not be trustworthy.
>
> Best,
> Wolfgang
>
> -----Original Message-----
> From: R-sig-meta-analysis [mailto:
> r-sig-meta-analysis-bounces using r-project.org] On Behalf Of James Pustejovsky
> Sent: Thursday, 14 February, 2019 22:46
> To: florencia miguel
> Cc: R meta
> Subject: Re: [R-meta] Error: Ratio of largest to smallest sampling
> variance extremely large
>
> Florencia,
>
> I think the issue might have more to do with your data than with the
> estimation procedures. Could you try running one or all of the following
> lines:
>
> summary(mdata.all$var.es)
> plot(density(mdata.all$var.es)
> bottom_n(mdata.all, 5, var.es)
>
> This will provide a five-number summary of the sampling variances of your
> effect size estimates. If you have some that are very very small, this will
> cause the error you seem to have encountered. It might be worth checking
> the summary statistics for the effect sizes with very small variances, to
> see if there are data entry errors, or reporting errors in the primary
> sources.
>
> James
>
> On Thu, Feb 14, 2019 at 1:57 PM florencia miguel <mflormiguel using gmail.com>
> wrote:
>
> > Dear all, I am running a meta analysis with the main aim of comparing
> three
> > different kind of interventions and four kind of outcomes. I want to
> > perform different models for interventions and outcomes. I could run
> random
> > effects models using the package meta but, as I need to include
> moderators
> > in the models I tryed the metafor package.
> >
> > The problem is that I obtained this error when running rma.uni and
> > rma.mv functions:
> > "Error in rma.mv(yi = lrr, V = var.es, mods = ~aridity.index, method =
> > "REML", : Ratio of largest to smallest sampling variance extremely large.
> > Cannot obtain stable results."
> >
> > I am using Log response ratio as effect sizes. I know that data are very
> > heterogeneous (some rows with high variances values and other with low
> > variances) because I am comparing different kind of measures. So, I
> > performed models by subgroups (subsetting by interventions), and I
> obtained
> > the same type of error.
> >
> > Here are some codes:
> >
> > mod1 <- rma(lrr, var.es, mods= aridity.index, data=mdata.all,
> > subset=intervention=="vegetation")
> >
> > mod.2<-rma.mv(yi=lrr, V=var.es, mods= aridity.index, method = "REML",
> > test="t", random = ~ 1 | ID, data=mdata.all, sparse=TRUE)
> >
> > mod.3 <- rma(lrr, var.es, mods=  ~intervention, data = mdata.all,
> subset =
> > paradigm == "active")
> >
> > ##filtering by interventions
> > mdata.veg <- mydata %>%
> >   filter(intervention=="vegetation") %>%
> >   filter(!is.na(lrr)) %>%
> >   filter(!is.na(var.es))
> >
> > mod<-rma(lrr,var.es, mods= aridity.index, digits=4,data=mdata.veg)
> >
> > I dont know why i getting the same error after subsetting or filtering by
> > groups.
> >
> > Thank you in advance!
> > Florencia
>

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