[R-meta] Specifying variable names in metafor::vec2mat()

Dear Yuhang,

Since coef(res) is of length 6, vec2mat() will put these values (i.e., the estimated average correlations) into a 4x4 (correlation) matrix. Given the ordering of the values in coef(res), the appropriate dimension names would be something like this:

vec2mat(coef(res), dimnames=c("acog","asom","conf","perf"))

However, there is no general rule for this, as it depends on the ordering of the values in coef(res).

I have tried to make various functions in metafor behave in a consistent manner when it comes to ordering certain elements, but in this case, it is really up to the user to check that adding dimension names even makes sense for the resulting matrix. For example:

vec2mat(sample(coef(res)))

is perfectly fine in principle (albeit a bit weird), but it no longer makes sense to say that all values in the first column/row pertain to the correlations between one of the four variables and the other three variables (unless you get lucky) and so on.

But say the coefficients had been ordered like this:

coef(res)[c(3,5,6,1,2,4)]

Then the appropriate dimension names would be:

vec2mat(coef(res)[c(3,5,6,1,2,4)], dimnames=c("perf","acog","asom","conf"))

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org] On
Behalf Of Yuhang Hu via R-sig-meta-analysis
Sent: Monday, 11 September, 2023 5:32
To: R meta
Cc: Yuhang Hu
Subject: [R-meta] Specifying variable names in metafor::vec2mat()

Hello Everyone,

I'm using the metafor::vec2mat() as shown in the toy example below.
However, I wonder how I should specify the order of the variables' names
defined in "dimnames=" when converting the vector of coefficients to a
correlation matrix using metafor::vec2mat()?

Is there generally a rule or a technique to order the variables' names
correctly in "vec2mat()", especially if there are many variable names?

Thanks,
Yuhang

# EXAMPLE:
tmp <- rcalc(ri ~ var1 + var2 | study, ni=ni, data=dat.craft2003)
V <- tmp$V
dat <- tmp$dat

res <- rma.mv(yi~ var1.var2 - 1, V,
             random = ~  var1.var2| study, struct = "UN",
             data=dat)

vec2mat(coef(res), dimnames= ??????)