[R-sig-ME] Using broom.mixed library with lme4
Phillip Alday
ph||||p@@|d@y @end|ng |rom mp|@n|
Tue Nov 10 23:50:51 CET 2020
I'm a bit confused now and think a point is being missed, although I'm
not sure where.
Zach pointed out that the Rubin Rule is being applied. Both Zach and I
pointed out that you still get the pooled estimate and that the ubar and
b columns are uncertainty on the pooled estimate. In some quick tries
with example datasets, it seems that you don't get the ubar and b
columns for lm() fits, but I'm guessing that this means that it's not
possible to break down the variance of t (total variance of estimate)
into between and within estimates for non mixed models.
In other words, it seems that pool does more for lmer() than it does for
lm(), not less.
Your original question asked about the random effects, and we pointed
out that the the pool() table doesn't have the random effects from the
lmer() fit.
This becomes even clearer if you call summary() on the value of pool():
> summary(pool(fit))
term estimate std.error statistic df p.value
1 (Intercept) 4.8932366 0.08966606 54.57178 521.81717 0.000000e+00
2 sex 0.8772664 0.05428838 16.15938 11.17712 4.226618e-09
(and that use of summary() is from the documentation Zach linked).
Can you try to pose your question a different way if we're
misunderstanding?
Best,
Phillip
On 10/11/20 11:26 pm, Simon Harmel wrote:
> 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
> <mailto: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]]
> > >
> > > _______________________________________________
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> > >
>
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