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

[Yuhang Hu]

And James to add some clarity to my previous email, I can imagine 3
situations for calculating that probability using:

(1) Estimates of average effect and total variation in sd unit (Kind of
like point estimate for probability)

(2) Lower CI limit of average effect and upper CI limit of total variation
in sd unit (Kind of like lower limit for probability)

(3) Upper CI limit of average effect and lower CI limit of total variation
in sd unit (Kind of like upper limit for probability)

I wonder how legitimate this proposal might sound to you? Thanks!

Yuhang

On Sun, Aug 21, 2022 at 9:28 PM Yuhang Hu <yh342 using nau.edu> wrote:

> 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
>


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

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