Message-ID: <CADreqiz7kNb53KQROD9L-SumYto1bo-=z9ogEonc5MO95u40mA@mail.gmail.com>
Date: 2021-11-26T05:27:19Z
From: Timothy MacKenzie
Subject: [R-meta] rma.mv to lme possible?
In-Reply-To: <CADreqiyWKvd5+=WUFSGPbEP3TFAMpdpi_7ZzOY0XukN72JJQ3Q@mail.gmail.com>
More generally, is there a way to convert/switch an rma.mv() fitted
object to a corresponding lme() fitted object such that then I can use
the resulting object in R packages like "library(effects)"
(https://cran.r-project.org/web/packages/effects/index.html) for
plotting purposes?
Thanks,
Tim M
On Thu, Nov 25, 2021 at 7:57 PM Timothy MacKenzie <fswfswt at gmail.com> wrote:
>
> Dear All,
>
> I've specified the following rma.mv() model for my meta-analysis.
> However, I'm wondering how to replicate this model using lme() from
> the nlme package (sample data is provided below)?
>
> V <- with(dat1, clubSandwich::impute_covariance_matrix(vi,study,r=.6))
>
> g<-rma.mv(yi ~ 0 + study_type, V, random = list(~study_type | study,
> ~interaction(study_type,reporting) | obs), struct = c("DIAG","DIAG"),
> data = dat1)
>
> I'm open to either ML or REML methods of estimation. I have tried the
> following with no success:
>
> lme(yi ~ 0 + study_type,
> random = list(~study_type | study,
> ~interaction(study_type,reporting) | obs),
> weights = varComb(varFixed(~vi),
> varIdent(form = ~study | study_type),
> varIdent(form = ~obs |
> interaction(study_type,reporting))),
> correlation = corCompSymm(.6, ~1|study, fixed = TRUE),
> data = dat1,
> control=lmeControl(sigma = 1,returnObject=TRUE))
>
> Thanks,
> Tim M
> d="
> study subscale reporting obs include yi vi study_type
> 1 A subscale 1 yes 1.94 0.33503768 standard
> 1 A subscale 2 yes 1.06 0.01076604 standard
> 2 A subscale 3 yes 2.41 0.23767389 standard
> 2 A subscale 4 yes 2.34 0.37539841 standard
> 3 A&C composite 5 yes 3.09 0.31349510 standard
> 3 A&C composite 6 yes 3.99 0.01349510 standard
> 4 A&B composite 7 yes 2.90 0.31349510 standard
> 4 A&B composite 8 yes 3.01 0.91349510 standard
> 5 G&H composite 9 yes 2.01 0.97910095 alternative
> 5 G&H composite 10 yes 2.11 0.37910095 alternative
> 6 E&G composite 11 yes 2.01 0.67910095 alternative
> 6 E&G composite 12 yes 2.11 0.87910095 alternative
> 7 E subscale 13 yes 0.08 0.21670360 alternative
> 7 G subscale 14 yes 0.77 0.91297170 alternative
> 8 F subscale 15 yes 1.08 0.81670360 alternative
> 8 E subscale 16 yes 1.07 0.91297170 alternative"
>
> dat1 <- read.table(text=d,h=T)