[R-meta] Plotting confidence intervals around regression slope using rma.mv

[Viechtbauer Wolfgang (SP)]

The 'newmods' argument should be a matrix. So use this:

sav <- predict(m1, newmods = as.matrix(df1))

(I'll consider making predict.rma() also work with data frames)

'd1f$preds<-' won't work either, since predict.rma() returns not just a vector, but a list with elements of various length (unclass(sav) will show you the contents). So just put things in another object and then you can use it.

Also, predict.rma() does not have an 'interval' argument, but it always returns the CI anyway.

Finally, I know this is just a toy example, but the struct="DIAG" has no relevance when random=(~1|ID). The struct argument only matters when using things like 'random = ~ inner | outer'.

So, to create the plot:

plot(df$x, df$y, pch=19)
lines(df1$x, sav$pred)
lines(df1$x, sav$ci.lb, lty="dotted")
lines(df1$x, sav$ci.ub, lty="dotted")

For a more complete example, see:

http://www.metafor-project.org/doku.php/plots:meta_analytic_scatterplot

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org] On Behalf Of Bronwen Stanford
Sent: Monday, 12 March, 2018 20:03
To: r-sig-meta-analysis at r-project.org
Subject: [R-meta] Plotting confidence intervals around regression slope using rma.mv

I am trying to plot the regression line estimated using metafor and 95%
confidence intervals on the regression line, and I'm not sure how to do
this. Here is an example of what I'm hoping to create using lm from base R:
https://stat.ethz.ch/pipermail/r-help/2007-November/146285.html

The model has random effects, but for the purposes of this plot, I'm
interested in just plotting the slope of one predictor and the CI for that
predictor, ignoring the random effects.

Do I need to use a model matrix (along these lines
http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#lme4) ? And if so,
does anyone have any example syntax that I could follow? I tried adapting
the linked example, and ran into a lot of problems.

Here is a dummy example (that isn't working). I'm interested in plotting
values of y against x while holding "a" constant:

#create example data
df<-data.frame(expand.grid(
  a=seq(0,1, length=2), #covariate
  x=seq(0,1, length=5),  #predictor I want to plot
  y=seq(0,5,  length=5), #response
  vi=seq(.1,.5, length=2),  #variance
  ID=c("A","B")   #random effect
))

#run the model
m1<-rma.mv(y, V=vi,
           mods= ~ a + x,
           random=(~1|ID), struct="DIAG",
           data=df)

#create new df to use for predictions, only allowing x to vary
df1<-data.frame(expand.grid(
  a=mean(df$a),
  x=seq(0,1, length=5)
))

d1f$preds<-predict(m1, newmods=df1, c=c("confidence"))

I get the message "Error in Xi.new %*% x$beta : requires numeric/complex
matrix/vector arguments".