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

[Danka Puric]

Dear Wolfgang,

here is the output of the dput(sav):

> dput(sav)
structure(list(sigma2 = c(0, 0.00526315789473684, 0.0105263157894737,
0.0157894736842105, 0.0210526315789474, 0.0263157894736842,
0.0315789473684211,
0.0368421052631579, 0.0421052631578947, 0.0473684210526316,
0.0526315789473684,
0.0578947368421053, 0.0631578947368421, 0.068421052631579,
0.0736842105263158,
0.0789473684210526, 0.0842105263157895, 0.0894736842105263,
0.0947368421052632,
0.1), ll = c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_), beta = structure(list(intrcpt = c(NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_)), class = "data.frame", row.names =
c(NA,
-20L)), ci.lb = structure(list(intrcpt = c(NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_)), class = "data.frame", row.names =
c(NA,
-20L)), ci.ub = structure(list(intrcpt = c(NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_)), class = "data.frame", row.names =
c(NA,
-20L)), comps = 1, ylim = c(Inf, -Inf), method = "REML", vc = 0,
    maxll = structure(-26.6009639211932, nall = 70L, nobs = 69L, df = 6,
class = "logLik"),
    xlab = expression(paste(sigma^2, " Value")), title = expression(
        paste("Profile Plot for ", sigma^2))), class = "profile.rma")

And yes, we realize that the number of effect sizes is rather small. We
were only able to identify 14 papers with 16 independent studies, so we're
trying to maximize the information we can extract by fitting a model with
nested random effects. However, we understand it might ultimately be
necessary to switch to another approach for dealing with effect size
dependence.

Thanks for all your help!
Danka

On Tue, May 7, 2019 at 9:51 PM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:

> 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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