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

Viechtbauer, Wolfgang (SP) wo||g@ng@v|echtb@uer @end|ng |rom m@@@tr|chtun|ver@|ty@n|
Thu Feb 14 23:23:12 CET 2019

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.


-----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


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

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


On Thu, Feb 14, 2019 at 1:57 PM florencia miguel <mflormiguel using gmail.com>

> 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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