[R-meta] Model with intercept gives 0 heterogeneity but without intercept is ok

-----Original Message-----
From: Luke Martinez [mailto:martinezlukerm at gmail.com]
Sent: Monday, 30 August, 2021 23:17
To: Viechtbauer, Wolfgang (SP)
Cc: R meta
Subject: Re: [R-meta] Model with intercept gives 0 heterogeneity but without
intercept is ok

Thank you, Wolfgang. I visited the link you kindly shared. But that link only
discusses the effect of removing the intercept on the fixed parts,?not random
parts.

Also, in that link the fixed parts only?include either a categorical or only a
continuous?moderator,?but not both types of moderators together. For example, if
we have two categorical moderators?and one continuous?moderator, as in:

data$gender <- sample(c("M","F"),nrow(data),replace = TRUE)
data$sector <- sample(c("Pr","Pv", "NGO"),nrow(data),replace = TRUE)

Then, removing the intercept is the matter of which categorical moderator appears
last in the formula! For example, in:

(A): rma.mv(yi ~? 0 + gender + sector + ?X , vi, random = ~ 1 | study/outcome,
data = data)

R removes the intercept for "sector"?because?it appears last. But, in:

(B): rma.mv(yi ~ ?0 + sector + gender + ?X , vi, random = ~ 1 | study/outcome,
data = data)

R removes the intercept for "gender" because?it appears last.

My question is that do these behaviors in the fixed-part, essentially, change the
meaning/nature (e.g., what average is varying across study levels) of the random
parts?

Apparently, the random part is not related to the fixed-part in rma.mv(), and
that's why both (A) and (B), with or without the intercepts (i.e., 4
specification) all give the exact same estimates of their two variance components?

Thank you,
Luke