[R-meta] margins command

[Patricia Ziegelmann]

Thanks Reza, it was very helpful!

Best

Patricia

Em qui., 9 de mar. de 2023 às 14:08, Reza Norouzian via R-sig-meta-analysis
<r-sig-meta-analysis using r-project.org> escreveu:

> 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 using 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 using 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 using 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 Gonçalves, 9500 - Prédio 43-111 - Agronomia
> > >91509-900 Porto Alegre - RS - BRAZIL
> > _______________________________________________
> > R-sig-meta-analysis mailing list @ R-sig-meta-analysis using r-project.org
> > To manage your subscription to this mailing list, go to:
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> >
>
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-- 
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 Gonçalves, 9500 - Prédio 43-111 - Agronomia
91509-900 Porto Alegre - RS - BRAZIL

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