[R-meta] stepadj argument ignored in an rma.mv model

[Viechtbauer, Wolfgang (SP)]

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