# [R-meta] Interpreting variance components in rma.mv

Yuhang Hu yh342 @end|ng |rom n@u@edu
Mon Aug 22 06:28:02 CEST 2022

```Dear James,

Thank you. You noted that using estimates of average effect and total
variation (in sd unit) ignores the fact that these quantities are
themselves estimates and not fixed values.

But can't we use the lower and upper limits of these estimates' own CIs to
obtain the a range to supplement A in the following calculations?

A:
pnorm(0, average_effect, average_total_variation, lower.tail = FALSE)

B:
pnorm(0, lower_average_effect, lower_total_variation, lower.tail = FALSE)

pnorm(0, upper_average_effect, upper_total_variation, lower.tail = FALSE)

Best,
Yuhang

On Sun, Aug 21, 2022 at 12:08 PM James Pustejovsky <jepusto using gmail.com>
wrote:

> Hi Yuhang,
>
> But is it appropriate to assume that true effects' dispersion at time 0
>> and time 1 is exactly the same (equality of variances across time points)?
>>
>
> The model you've fit assumes that the variances are equal across time
> points. Whether this assumption is appropriate is an empirical question and
> something you'll need to gauge for yourself. You could probe it by, for
> example, fitting a model that allows the variance components to differ by
> time point:
> rma.mv(yi ~ 0 + cat_mod * time + covariates, random = ~ time |
> study/effect, struct = "UN")
> And then comparing the fit of this model to the fit of the model that
> assumes compound symmetry (i.e., your initial model).
>
> James
>

--
Yuhang Hu (She/Her/Hers)
Ph.D. Student in Applied Linguistics
Department of English
Northern Arizona University

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