[R-meta] margins command

Nice addition, Wolfagang.

Patricia, I'm adding a couple of points to Wolfgang's informative comments.

First, you may want to set ** test = "knha" ** in your initial rma() call
for the dfs passed to emmeans to be a bit more conservative compared to the
default setting (i.e., test = "z") which translates into * Inf * df for the
t distribution-based inferences used in emmeans.

Second, you can also test the estimated marginal means (EMMs) in additional
ways that might be useful for various applications:

1- Testing contrasts among EMMs for each categorical variable against zero:
joint_tests(sav)

2- Testing EMMs for each categorical variable (rather than contrasts among
them) against zero:
test(emmeans(sav,"wic"), joint=TRUE)
test(emmeans(sav,"info"), joint=TRUE)

3- Jointly testing if the EMMs *across* multiple categorical variables
(blocks) are equal to each other:
com <- comb_facs(sav, c("wic","info")) # my suggestion for emmeans package
added by Russ
joint_tests(com)

Kind regards,
Reza


On Thu, Mar 9, 2023 at 9:51?AM Viechtbauer, Wolfgang (NP) via
R-sig-meta-analysis <r-sig-meta-analysis at r-project.org> wrote:

Dear Patricia,

To start: 'sei = vi' looks wrong. Argument 'sei' is for passing the
standard errors to the rma() function, but 'vi' sounds like a variable

that

contains sampling variances.

This aside, the answer to your question is yes. You can do this with:
predict(res, newmods=c(1,1), intercept=FALSE).

As for marginal effects, you can use emmprep() together with the emmeans
package (note that you will need to install the 'devel' version of

metafor

for this, since emmprep() was recent added;
https://wviechtb.github.io/metafor/#installation).

An example:

library(metafor)

dat <- dat.bangertdrowns2004

res <- rma(yi, vi, mods = ~ wic + info, data=dat)
res

predict(res, newmods=c(1,1), intercept=FALSE)

sav <- emmprep(res)

library(emmeans)

emmeans(sav, specs="1", weights="proportional")

# this is identical to
predict(res, newmods = colMeans(model.matrix(res))[-1], digits=3)

# marginal means for 'wic' and 'info'
emmeans(sav, specs="wic", weights="proportional")
emmeans(sav, specs="info", weights="proportional")

If you do not want proportional weights, then just leave out the
'weights="proportional"' part.

Best,
Wolfgang

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

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

Behalf Of Patricia Ziegelmann via R-sig-meta-analysis
Sent: Thursday, 09 March, 2023 14:17
To: r-sig-meta-analysis at r-project.org
Cc: Patricia Ziegelmann
Subject: [R-meta] margins command

Dear all,

I?m adjusting a simple metaregression model:

meta <- rma(yi = yi,
             sei = vi,
             mods = ~ risk1 + risk2,
             data = data1,
             method = "REML")

Both risk1 and risk2 are binary factors.

Is it possible to estimate a confidence interval for a function of the
fitted coefficients, let's say beta[1]+beta[2]?

Also, is there a command like the "margins" to have marginal effects
summaries?

Thanks in advance for any help,

Patricia

--
Patricia Klarmann Ziegelmann
Department of Statistics
Graduate Program in Epidemiology
Universidade Federal do Rio Grande do Sul (UFRGS)
http://lattes.cnpq.br/8486215358933890
Av. Bento Gon?alves, 9500 - Pr?dio 43-111 - Agronomia
91509-900 Porto Alegre - RS - BRAZIL

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