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

[Timothy MacKenzie]

BTW, "aa" could also be as simple as:

aa <- lmer(yi ~ 0 + study_type + (1 | study), data = dat1)

On Fri, Nov 26, 2021 at 9:40 AM Timothy MacKenzie <fswfswt using gmail.com> wrote:
>
> Sure, but is it possible to get only the fixed effect estimates and
> their SEs and replace them for their corresponding slots in a new
> lme() or lmer() object?
>
> For example, for my small data, the new lmer() call (disregarding the
> warnings) is below. Once replaced (new "aa2" object below), it should
> work as in:     effects::allEffects(aa2)
>
> Thanks,
> Tim M
>
> aa <- lmer(yi ~ 0 + study_type + (study_type | study) +
>          (interaction(study_type,reporting) | obs),
>          data = dat1,
>          control =
>          lmerControl(check.nobs.vs.nRE="ignore",check.nobs.vs.nlev="ignore"))
>
> aa2 <- "aa" object with fixed fixed effect estimates and their SEs
> replaced with those from "g"
>
> On Fri, Nov 26, 2021 at 4:05 AM Viechtbauer, Wolfgang (SP)
> <wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
> >
> > 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)