[R-meta] Question about function reporter()

Hello Wolfgang,

Thank you very much for the explanation  and the link. I will go through
it carefully.

Briefly, I was trying to use '~ 1 | alloc'  (with alloc being a column in
the table class numerical taking the sing of the correlation either -1 or
1)  to model whether the influence of the correlations' sing  hold for the
alternative hypothesis that when positive the correlations --which are
coming from different studies (or experiments in the studies)-- are the
result of correlating a magnitud A with B; whereas when negative the
correlations comes from correlating the same source A source with C.
Thanks a lot again.
Kind regards,
Gabriel



On Fri, Jun 16, 2023 at 2:34?PM Viechtbauer, Wolfgang (NP) <
wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:

There is no universal definition of what a random-effects model is, but
take a look at:

https://www.metafor-project.org/doku.php/analyses:crede2010

and especially the section "A Common Mistake when Fitting the Multilevel
Model". So, if your data have such a multilevel structure, then typically
we would add a random effect for articles and a random effect for the
observations within articles. Beyond this, you could do '~ 1 | alloc'
although it is still not clear to me what you think this is modelling.

Best,
Wolfgang

-----Original Message-----
From: Gabriel Cotlier [mailto:gabiklm01 at gmail.com]
Sent: Friday, 16 June, 2023 11:45
To: Viechtbauer, Wolfgang (NP)
Cc: R Special Interest Group for Meta-Analysis
Subject: Re: [R-meta] Question about function reporter()

Hello Wolfgang,

As far as I understand from your explanation, by setting random

variables to

observe the potential influence of whether the correlations ( yi ) are
positive or negative would not be a random model, is this correct?

But, what if I keep moderator for grouping by Type (categorical:

corresponding

to the method employed by the studies either "CS", "R", "E")  and would

include

in random another "Id" that represent whether the correlations come from

either

the same or a different study by means of the variable named "Article"
(numerical: with same value for same article ) as follows:

res3a <- rma.mv(yi = yi,
              V = vi,
              mods = ~Type - 1,
              random = list(~1 | alloc, ~1 | Article),
              data = dat)

Could now be considered in this case a random effect model?

Thanks a lot.
Kind regards,
Gabriel

On Fri, Jun 16, 2023 at 11:45?AM Viechtbauer, Wolfgang (NP)
<wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
As for your actual question: That's a bit too broad. Essentially, you

need to

familiarize yourself with what people typically report for standard RE

models (as

reporter() does), then familiarize yourself with what people report meta-
regression models (since you have a meta-regression model), and then

consider how

the random effects structure of your model differs from a standard RE

model and

how this might affect what to report. I am not aware of any papers that

will walk

you through all of that. People typically learn reporting practices from

what

other people have done, so reading lots of existing meta-analyses will

give you a

sense of how this is done.

This aside, I am not sure what you hope to accomplish by adding a random

effect

for whether the outcome is positive or negative. Also, your model does

not

capture any heterogeneity beyond this, so it isn't really a

random-effects model

(or something akin to it).

Best,
Wolfgang

-----Original Message-----
From: Gabriel Cotlier [mailto:gabiklm01 at gmail.com]
Sent: Friday, 16 June, 2023 9:08
To: Yefeng Yang
Cc: R Special Interest Group for Meta-Analysis; Viechtbauer, Wolfgang

(NP)

Subject: Re: [R-meta] Question about function reporter()

Hello,

Wolfgang : very helpful and nourishing knowledge and guidance about the
function rma.mv() potential and complexity, thanks a lot for the

explanation!

Yes indeed, maybe in the "longue dur?e" --or not so much-- future time

we will

be

more worried about bigger threats AI technology can pose to us ...

Anyway, It become an interesting participative discussion afterwards.

Maybe, I should apologize due to the lack of specificity or rugurosity

in my

question, since I was only thinking --but did not write it--in my

particular

case, mainly of an rma.rm() function with the simplest the possible

"random"

arguments rather than the more the complex nested random effect models,

such as

for instance:

data_1 <- rma.mv(yi = yi,
                    V = vi,
                    mods = ~Type - 1,
                    random = ~1 | alloc,
                    data = dat)

where the "alloc" variable refers just whether the correlation ( yi )

has either

positive or negative sign (numerical variable  taking either -1 or 1)

and "Type"

is a just categorical variable (of char class) composed of 3 clases

Types namly

"R", "E", and "CS".

Maybe there is instead of a reporter() function output results some

bibliography

that can lead me to a nice manner in which I can report the results of

my simple

application of rma.mv() function output as an example to support the

bases of

the

writing of my report.

Thanks a lot.
Kind regards,
Gabriel

On Thu, Jun 15, 2023 at 12:05?PM Yefeng Yang <yefeng.yang1 at unsw.edu.au>

wrote:

Actually, I have a different opinion about reporter() function. Just

for an open

discussion and no offensive. Instead of automatically generating an

analysis

report based on the fitted model (via rma()), it is probably more

useful to have

a helper function to automatically generate a "publication-ready" table

that

shows the quantities recommended by various PRISMA-related reporting

guidelines,

such as the name of the moderator, tau, I2, point estimate, CIs, k, t,

p, et al.

The analysis report is something that should be (and must be at some

point) done

by the analysts themselves and they are responsible for the proper
interpretation.

Regards,
Yefeng

on behalf

of Viechtbauer, Wolfgang (NP) via R-sig-meta-analysis

<r-sig-meta-analysis at r-

project.org>
Sent: Thursday, 15 June 2023 18:38
To: R Special Interest Group for Meta-Analysis <r-sig-meta-analysis at r-
project.org>
Cc: Viechtbauer, Wolfgang (NP) <

wolfgang.viechtbauer at maastrichtuniversity.nl>;

Gabriel Cotlier <gabiklm01 at gmail.com>
Subject: Re: [R-meta] Question about function reporter()

Dear Gabriel,

You can't, since reporter() doesn't currently work for 'rma.mv'

objects.

Models that can be fitted with rma() (same as rma.uni()) are relatively

simple

and the number of possibilities that need to be covered for translating

the

results from such a model into text are managable. Although even here,

there are

currently restrictions. For example, reporter() currently only works for
'intercept-only models' (i.e., models without moderators), it doesn't

work when

robust() was used on the model, and it doesn't work for location-scale

models.

Allowing reporter() to work with meta-regression models is on my radar,

but not

sure when I will get to this.

Models that can be fitted with rma.mv() are an entirely different

beast. This

function allows users to fit multilevel models (with essentially no

limits on

the

number of levels), multivariate models (with multiple correlated random

effects),

network meta-analyses, phylogenetic meta-analyses, spatio-temporal

models,

models

with random slopes, models with crossed random effects, and

combinations thereof

(e.g., multivariate network meta-analysis). Such models will also

typically

involve one or multiple moderators (e.g., to distinguish different

outcomes,

treatments, time points, etc.). Depending on the type of model,

different

aspects

of the results are also more or less relevant (e.g., in a phylogenetic

MA, there

would be a lot of focus on the random effects for species, while in a

network

MA,

focus would be more on contrasting the different treatments with each

other).

There is essentially no way in hell that one could write reporter()-like
functionality for 'rma.mv' type models that covers all these
aspects/possibilities in a sensible way.

Of course, one could consider writing a version that only covers a few

special

cases; for example, models of the form rma.mv(yi, V, random = ~ 1 |
level1/level2/level3/...) or rma.mv(yi, V, random = ~ var1 | var2)

although the

latter type of model would often be used when var1 corresponds to

different

outcomes in which case the model would probably involve moderators and

be of the

form rma.mv(yi, V, mods = ~ outcome, random = ~ outcome | study), but

in the

end,

the reporter() function cannot read the users mind as to what the goal

and focus

of their analysis was.

Alternatively, one could generate very generic text that does cover many
possibilities, but this would add essentially nothing to just reading

the output

directly.

Maybe if we wait another 20-30 years, ChatGPT (or Skynet or whatever it

will be

called then) will be able to do something like this automatically.

However, we

might be too busy fighting off the Terminators at that point to worry

about

rma.mv() models ...

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis [mailto:

r-sig-meta-analysis-bounces at r-project.org] On

Behalf Of Gabriel Cotlier via R-sig-meta-analysis
Sent: Thursday, 15 June, 2023 6:37
To: R Special Interest Group for Meta-Analysis
Cc: Gabriel Cotlier
Subject: [R-meta] Question about function reporter()

Hello all,

I am using an object of class "rma.mv" "rma" as :

class(data)
[1] "rma.mv" "rma"

and would like to use the function reporter().
How could this possibly be done either directly in metafor in R or

maybe in

JAMOVI or in other software where the metafor package is included?

Thanks a lot.
Kind regards,
Gabriel