[R-sig-ME] Using broom.mixed library with lme4

[Simon Harmel]

Thanks.  I think a point is being missed. I did have carefully read the
documentation.  And pool() is in fact supposed to apply the RUBIN RULE and
pool across the estimates from 5 imputed datasets.

For lm() objects, pool() does what I describe above. But it seems, it
doesn't do the same for the lmer() objects.

That was really the core of my question and l was wondering if a solution
to it might be available?


There is no documentation on that.

On Tue, Nov 10, 2020, 3:28 PM Zach Simpson <zpsimpso using gmail.com> wrote:

> Just to add to Phillip's answer, the ubar has to do with the multiple
> imputation procedure: ubar is the within-imputation variance of the
> sex term. You fitted 5 lmer models on 5 'completed' datasets whose
> NA's were filled using default imputation procedures from mice.
> mice::pool() combines these m=5 fits using Rubin's rules. There's
> uncertainty in model estimates due to the data itself as well as the
> imputation uncertainty.
>
> Stef van Buuren has put online an enormous amount of documentation,
> which would pay dividends to read:
>
> https://amices.org/mice/
>
> HTH
> Zach
>
> > Why do you think ubar is for the random effects? In my very quick skim
> > of the documentation, I didn't see anything indicating that. Looking at
> > the structures in `fit`, I see:
> >
> > > tidy(fit$analyses[[5]])
> > # A tibble: 4 x 6
> >   effect   group    term            estimate std.error statistic
> >   <chr>    <chr>    <chr>              <dbl>     <dbl>     <dbl>
> > 1 fixed    NA       (Intercept)        4.91     0.0855      57.4
> > 2 fixed    NA       sex                0.853    0.0350      24.4
> > 3 ran_pars school   sd__(Intercept)    0.820   NA           NA
> > 4 ran_pars Residual sd__Observation    0.767   NA           NA
> >
> > None of the random effects line up with the output of pool(). Moreover,
> > the pool() documentation notes that it needs the standard error of each
> > estimate, but lme4 doesn't produce those (for good reason) for random
> > effects, so pool() won't produce pooled estimates for the random effects.
> >
> > The pool() documentation mentions the mipo class, so I looked at ?mipo
> > and found this:
> >
> >
> >        ‘estimate’  Pooled complete data estimate
> >        ‘ubar’      Within-imputation variance of ‘estimate’
> >        ‘b’         Between-imputation variance of ‘estimate’
> >        ‘t’         Total variance, of ‘estimate’
> >        ‘dfcom’     Degrees of freedom in complete data
> >        ‘df’        Degrees of freedom of $t$-statistic
> >        ‘riv’       Relative increase in variance
> >        ‘lambda’    Proportion attributable to the missingness
> >        ‘fmi’       Fraction of missing information
> >
> >
> > So `ubar` and `b` are perhaps random effects, but not in the sense
> > you're thinking of, but rather the random effects that go into
> > imputation procedures (this is a guess on my part). I don't know much
> > about imputation, but I suspect this is analogous to the parallels
> > between mixed models and meta-analysis
> > (http://www.metafor-project.org/doku.php/tips:rma_vs_lm_lme_lmer). But
> > again, this is rapidly getting out of my area of expertise and into the
> > expertise of other members of this list (e.g. Wolfgang Viechtbauer for
> > meta analysis).
> >
> > Phillip
> >
> > On 10/11/20 7:17 am, Simon Harmel wrote:
> > > Dear All,
> > >
> > > Belwo, I've used library `broom.mixed` and imputed some data with
> library
> > > `mice` to then fit a "random-intercept" `lmer()` model.
> > >
> > > BUT I wonder why after I `pool()` my analyses, there is an extra "ubar"
> > > (random-effect) for slope (`sex`) which is not even in the model?!
> > >
> > > library(mice)
> > > library(lme4)
> > > library(broom.mixed)
> > >
> > > imp <- mice(popmis, m = 5) # `popmis` is a dataset from `mice`
> > >
> > > fit <- with(data = imp, exp = lme4::lmer(popular ~ sex + (1|school)))
> > >
> > > pool(fit)
> > >
> > > ### `ubar` is the random effect for intercept (0.007524509) BUT WHY we
> see
> > > a ubar ALSO for `sex` (0.001177781)?
> > >
> > > Class: mipo    m = 5
> > >          term m  estimate        ubar            b           t dfcom
> > >  df
> > > 1 (Intercept) 5 4.9007789 0.007524509 0.0004845564 0.008105977  1996
> > > 547.44383
> > > 2         sex 5 0.8617941 0.001177781 0.0015867795 0.003081916  1996
> > >  10.33653
> > >         riv     lambda       fmi
> > > 1 0.0772765 0.07173321 0.0751060
> > > 2 1.6167147 0.61784141 0.6751515
> > >
> > >       [[alternative HTML version deleted]]
> > >
> > > _______________________________________________
> > > R-sig-mixed-models using r-project.org mailing list
> > > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> > >
>

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