[R-meta] Model with intercept gives 0 heterogeneity but without intercept is ok
Luke Martinez
m@rt|nez|ukerm @end|ng |rom gm@||@com
Mon Aug 30 20:46:25 CEST 2021
Dear Wolfgang,
Thank you.
1- To make sure I understand this correctly, you're saying that because I
killed the intercept, then the intercept for one or more
continuous moderators equals 0 for all studies, thus, there is no intercept
to vary across the levels of study, hence no between-study variance
component can be estimated (sigma^2.1 == 0), correct?
2- Under this circumstance (killing intercept with continuous moderators
only), the intercepts (or averages) for "study/outcome" combinations can
still vary across study-outcome combinations, and thus, in isolation from
"sigma^2.1", the other "sigma^2.2" can be [correctly] estimated, correct?
3- I have seen models where the intercept is killed in the fixed part, but
present in the random part. Based on what you said, in such models at least
1 categorical moderator must be present so the between-study variance
component can be estimated (e.g., below), correct?
data$gender <- sample(c("M","F"),nrow(data),replace = TRUE)
rma.mv(yi ~ 0 + X + gender, vi, random = ~ 1 | study/outcome)
estim sqrt nlvls fixed factor
sigma^2.1 0.0000 0.0001 60 no study <--- Still "0" ?
sigma^2.2 0.5932 0.7702 120 no study/outcome
Thank you very much,
Luke
On Mon, Aug 30, 2021 at 11:26 AM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
> Dear Luke,
>
> If X is a continuous moderator, removing the intercept forces the line to
> go through the origin. That is very rarely a sensible thing to do. See also:
>
>
> https://www.metafor-project.org/doku.php/tips:models_with_or_without_intercept
>
> Best,
> Wolfgang
>
> >-----Original Message-----
> >From: R-sig-meta-analysis [mailto:
> r-sig-meta-analysis-bounces using r-project.org] On
> >Behalf Of Luke Martinez
> >Sent: Monday, 30 August, 2021 18:02
> >To: R meta
> >Subject: [R-meta] Model with intercept gives 0 heterogeneity but without
> intercept
> >is ok
> >
> >Dear Colleagues.
> >
> >I fitted two exact same models except that for one I included the
> intercept
> >(Model 1) in the model, for the other, I didn't (Model 2).
> >
> >I wonder why for Model 1 the estimate of between-study heterogeneity is
> "0"
> >but for Model 2 that estimate is not "0"?
> >
> >Thank you very much,
> >Luke
> >
> >set.seed(132)
> >data <- expand.grid(study = 1:60, outcome = rep(1:2,2))
> >data$X <- rnorm(nrow(data))
> >e <- rnorm(nrow(data))
> >data$yi <- .8+.6*data$X + e
> >data$vi <- runif(nrow(data))
> >
> >Model1 <- rma.mv(yi ~ 1 + X, vi, random = ~ 1 | study/outcome, data =
> dat)
> >
> > estim sqrt nlvls fixed factor
> >sigma^2.1 0.0000 0.0001 60 no study
> >sigma^2.2 0.4707 0.6861 120 no study/outcome
> >
> >
> >Model2 <- rma.mv(yi ~ 0 + X, vi, random = ~ 1 | study/outcome, data =
> dat)
> >
> > estim sqrt nlvls fixed factor
> >sigma^2.1 0.5634 0.7506 60 no study
> >sigma^2.2 0.4878 0.6984 120 no study/outcome
>
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