[R-meta] stepadj argument ignored in an rma.mv model
Viechtbauer, Wolfgang (SP)
wo||g@ng@v|echtb@uer @end|ng |rom m@@@tr|chtun|ver@|ty@n|
Tue May 7 21:51:03 CEST 2019
Please do:
sav <- profile(es.re.mv, sigma2=1, plot=FALSE)
and then:
dput(sav)
and paste the output here.
This aside, you are fitting a rather complex model with 'only' 70 estimates. I cannot tell you whether this (and the model) makes sense.
Best,
Wolfgang
-----Original Message-----
From: Danka Puric [mailto:djaguard using gmail.com]
Sent: Tuesday, 07 May, 2019 20:59
To: Viechtbauer, Wolfgang (SP)
Cc: r-sig-meta-analysis using r-project.org
Subject: Re: [R-meta] stepadj argument ignored in an rma.mv model
Dear Wolfgang,
yes, you are correct, I was using Rstudio. Profiling in basic R resulted in this message:
Profiling sigma2 = 1
...
|=========================================================================================================================| 100%
Error in plot.window(...) : need finite 'ylim' values
In addition: Warning messages:
1: In min(x) : no non-missing arguments to min; returning Inf
2: In max(x) : no non-missing arguments to max; returning -Inf
Again, this is similar to what I get when plotting over single variance components.
I have installed the developer version of metafor, fitted the model again and plotted the variance components, but unfortunately I haven't noticed anything different (the model vaues are the same as well as the profile() warning messages).
Finally, here is the model I'm fitting as well as the full output:
> es.re.mv <- rma.mv(ES_corrected, SV, random = list(~ 1 | MA_data$IDeffect,
+ ~ MA_data$DV | MA_data$IDsubsample,
+ ~ MA_data$IDsubsample | MA_data$IDstudy),
+ struct="CS", data=MA_data, control=list(optimizer="optim", optmethod="Nelder-Mead"))
> es.re.mv
Multivariate Meta-Analysis Model (k = 70; method: REML)
Variance Components:
estim sqrt nlvls fixed factor
sigma^2 0.0000 0.0000 70 no MA_data$IDeffect
outer factor: MA_data$IDsubsample (nlvls = 55)
inner factor: MA_data$DV (nlvls = 5)
estim sqrt fixed
tau^2 0.0843 0.2903 no
rho 1.0000 no
outer factor: MA_data$IDstudy (nlvls = 16)
inner factor: MA_data$IDsubsample (nlvls = 55)
estim sqrt fixed
gamma^2 0.0000 0.0002 no
phi 1.0000 no
Test for Heterogeneity:
Q(df = 69) = 312.8672, p-val < .0001
Another huge thank you!
Danka
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