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

[Zach Simpson]

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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