[R-meta] Plotting rstandard and rstudent values from cluster

[Martineau, Roger]

Dear Wolfgang,

yes, I would like to visualize residuals for the cluster studyID.

May be a scatterplot of standardized (and studentitized) residuals versus fitted values ?

best,

roger ☺

________________________________________
De : Viechtbauer Wolfgang (SP) [wolfgang.viechtbauer at maastrichtuniversity.nl]
Envoyé : 18 août 2017 11:08
À : Martineau, Roger; r-sig-meta-analysis at r-project.org
Objet : RE: Plotting rstandard and rstudent values from cluster

I am not sure I understand your question. If you do:

rstandard(multiplemoderator, cluster=dataset$studyID)
rstudent(multiplemoderator, cluster=dataset$studyID)

you will get a list with two elements. The second element is called 'cluster' and this contains the cluster (in this case, study) level multivariate (internally or externally) standardized residuals (and the number of estimates used to compute them). You want to plot those?

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org] On Behalf Of Martineau, Roger
Sent: Friday, August 18, 2017 16:08
To: r-sig-meta-analysis at r-project.org
Subject: [R-meta] Plotting rstandard and rstudent values from cluster

Dear metafor users,

I downloaded the data set (i.e., p154-dataset.csv) from  Assinka and Wibbelink (2016) available from http://www.tqmp.org/RegularArticles/vol12-3/p154/p154.pdf

The model is:

> multiplemoderator <- rma.mv(y, v, mods = ~ pyear + typeovert + typecovert,
+                             random =list(~ 1 | effectsizeID, ~ 1 | studyID),
+                             tdist=TRUE, data=dataset)

To plot standardized residuals, I do:

> #### STD residuals ###
> par(mfrow=c(1,1))
> plot(fitted(multiplemoderator), rstandard(multiplemoderator)$z, pch=19)
> abline(h=0)
> which(rstandard(multiplemoderator)$z > 3)
integer(0)

Recently, Dr Viechtbauer reported the following:

By the way, there is now also rstudent() for 'rma.mv' objects (also with a 'cluster' argument, option for parallel processing, and 'reestimate' argument). Also, rstandard() now has a cluster argument. When using the cluster argument with rstandard() and rstudent(), the functions also compute cluster-level multivariate (internally or externally) standardized residuals. So, all of the tools are there for proper outlier diagnostics in 'rma.mv' models (i.e., one can check for outlying estimates and clusters).

What should I write in metafor to plot rstandard and rstudent values for each cluster separately.

Thanks in advance,

Roger ☺