[R-meta] rma.mv to lme possible?

[Viechtbauer, Wolfgang (SP)]

Hi Tim,

The internal structures of rma.mv and lme objects are so inherently different that it would take a lot of effort to write a converter function.

Best,
Wolfgang

>-----Original Message-----
>From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org] On
>Behalf Of Timothy MacKenzie
>Sent: Friday, 26 November, 2021 6:27
>To: R meta
>Subject: Re: [R-meta] rma.mv to lme possible?
>
>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 using 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)