[R-meta] Inner|outer model vs multiple random id terms in rma.mv
Viechtbauer, Wolfgang (SP)
wo||g@ng@v|echtb@uer @end|ng |rom m@@@tr|chtun|ver@|ty@n|
Wed Apr 29 20:32:07 CEST 2020
Yes, negative rho means negative correlation (rho is the correlation between random effects that have the same level of the 'outer' variable).
I don't know what you mean with "more applicable". The '~1| inner , ~1|outer' formulation implicitly assumes that the correlation is >= 0. If this is what you would like to assume, then you can use this formulation.
Best,
Wolfgang
>-----Original Message-----
>From: Divya Ravichandar [mailto:divya using secondgenome.com]
>Sent: Wednesday, 29 April, 2020 20:15
>To: Viechtbauer, Wolfgang (SP)
>Cc: r-sig-meta-analysis using r-project.org
>Subject: Re: [R-meta] Inner|outer model vs multiple random id terms in
>rma.mv
>
>Thank you Prof.Wolfgang. I was wondering how one would interpret negative
>rho (does this imply there is negative correlation between the inner
>levels?)
>Also for a case where rho is negative is there a preference on whether the
>`~inner| outer` or `~1| inner , ~1|outer` is more applicable?
>
>On Wed, Apr 29, 2020 at 10:45 AM Viechtbauer, Wolfgang (SP)
><wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
>Hi Divya,
>
>These two formulations will only yield the same results when rho is
>estimated to be >= 0 (which is not the case in the second example).
>
>Best,
>Wolfgang
>
>>-----Original Message-----
>>From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-
>project.org]
>>On Behalf Of Divya Ravichandar
>>Sent: Wednesday, 29 April, 2020 19:00
>>To: r-sig-meta-analysis using r-project.org
>>Subject: Re: [R-meta] Inner|outer model vs multiple random id terms in
>>rma.mv
>>
>>Hi all
>>
>>Following a recommendation from Prof.Wolfgang to make access to input data
>>easier, I have reformatted the above example to avoid using an external csv
>>file and such.
>>
>>Hi all,
>>
>>I am trying to understand why results from running a model of the form
>>~lvl1|lv2 are not comparable to results of running ~1 | lvl1 ,~ 1 | lvl2
>>
>>In a simple example (case_simple in code below),results of the 2 models are
>>comparable as expected.
>>However, when running the 2 models on a more complex example (case_complex)
>>markedly different results are obtained with ~ Dataset | Cohort estimating
>>a pvalue of .02 and list(~ 1 | Dataset,~ 1 | Cohort) estimating a pvalue of
>>.2
>>
>>Thank you
>>
>>*Reproducible example*
>>library(metafor)
>># example where results of the 2 models agree
>>case_simple <- data.frame(Dataset=
>>c("a","b","c","d"),Cohort=c("c1","c1","c2","c3"), Tech=
>>c("a1","a2","a1","a1"),Effect_size=c(-1.5,-
>>3,1.5,3),Standard_error=c(.2,.4,.2,.4))
>>res1 = rma.mv(Effect_size, Standard_error^2, random = list(~ 1 | Dataset,~
>>1 | Cohort), data=case_simple)
>>res2=rma.mv(Effect_size, Standard_error^2, random = ~ Dataset | Cohort,
>>data=case_simple)
>>
>># example where results of the 2 models DONT agree
>>case_complex <-
>>data.frame(Dataset=c("Dt1","Dt2","Dt3","Dt4","Dt5","Dt5","Dt6","Dt7","Dt8",
>"
>>Dt9"),Cohort=c("C1","C2",rep("C3",5),rep("C4",2),"C5"),
>>
>>Effect_size=c(-0.002024454,-0.003915314,-0.042282757,-1.43826175,-
>>0.045423574,-0.17682309,-21.72691245,-2.559727204,-0.091972279,-
>>0.763332081),
>>
>>Standard_error=c(0.15283972,0.117452325,0.262002289,0.555230971,0.708917912
>,
>>0.682989908,2.704749864,1.40514335,0.735696048,0.713557015))
>>res1 = rma.mv(Effect_size, Standard_error^2, random = list(~ 1 | Dataset,~
>>1 | Cohort), data=case_complex)
>>res2=rma.mv(Effect_size, Standard_error^2, random = ~ Dataset | Cohort,
>>data=case_complex)
>>
>>On Wed, Apr 22, 2020 at 9:51 AM Divya Ravichandar <divya using secondgenome.com>
>>wrote:
>>
>>> Hi all,
>>>
>>> I am trying to understand why results from running a model of the form
>>> ~lvl1|lv2 are not comparable to results of running ~1 | lvl1 ,~ 1 | lvl2
>>>
>>> In a simple example case below,results of the 2 models are comparable as
>>> expected.
>>>
>>> ```case <- data.frame(Dataset=
>>> c("a","b","c","d"),Cohort=c("c1","c1","c2","c3"), Tech=
>>> c("a1","a2","a1","a1"),Effect_size=c(-1.5,-
>>3,1.5,3),Standard_error=c(.2,.4,.2,.4))
>>> res1 = rma.mv(Effect_size, Standard_error^2, random = list(~ 1 |
>>> Dataset,~ 1 | Cohort), data=case)
>>> res2=rma.mv(Effect_size, Standard_error^2, random = ~ Dataset | Cohort,
>>> data=case)
>>> ```
>>> However, when running the 2 model on a more complex example [attached]
>>> markedly different results are obtained with ~ Dataset | Cohort
>>> estimating a pvalue of .02 and list(~ 1 | Dataset,~ 1 | Cohort)
>>> estimating a pvalue of .2
>>> --
>>> *Divya Ravichandar*
>>> Scientist
>>> Second Genome
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