stopifnot(require(knitr)) options(width = 90) opts_chunk$set( comment = NA, message = FALSE, warning = FALSE, eval = if (isTRUE(exists("params"))) params$EVAL else FALSE, dev = "png", dpi = 150, fig.asp = 0.8, fig.width = 5, out.width = "60%", fig.align = "center" ) library(brms) ggplot2::theme_set(theme_default())

Many real world data sets contain missing values for various reasons. Generally,
we have quite a few options to handle those missing values. The easiest solution
is to remove all rows from the data set, where one or more variables are
missing. However, if values are not missing completely at random, this will
likely lead to bias in our analysis. Accordingly, we usually want to impute
missing values in one way or the other. Here, we will consider two very general
approaches using **brms**: (1) Impute missing values *before* the model fitting
with multiple imputation, and (2) impute missing values on the fly *during*
model fitting[^1]. As a simple example, we will use the `nhanes`

data set, which
contains information on participants' `age`

, `bmi`

(body mass index), `hyp`

(hypertensive), and `chl`

(total serum cholesterol). For the purpose of the
present vignette, we are primarily interested in predicting `bmi`

by `age`

and
`chl`

.

data("nhanes", package = "mice") head(nhanes)

There are many approaches allowing us to impute missing data before the actual
model fitting takes place. From a statistical perspective, multiple imputation
is one of the best solutions. Each missing value is not imputed once but
`m`

times leading to a total of `m`

fully imputed data sets. The model
can then be fitted to each of those data sets separately and results are pooled
across models, afterwards. One widely applied package for multiple imputation is
**mice** (Buuren & Groothuis-Oudshoorn, 2010) and we will use it in the
following in combination with **brms**. Here, we apply the default settings of
**mice**, which means that all variables will be used to impute missing values
in all other variables and imputation functions automatically chosen based on
the variables' characteristics.

library(mice) imp <- mice(nhanes, m = 5, print = FALSE)

Now, we have `m = 5`

imputed data sets stored within the `imp`

object. In
practice, we will likely need more than `5`

of those to accurately account for
the uncertainty induced by the missingness, perhaps even in the area of `100`

imputed data sets (Zhou & Reiter, 2010). Of course, this increases the
computational burden by a lot and so we stick to `m = 5`

for the purpose of this
vignette. Regardless of the value of `m`

, we can either extract those data sets
and then pass them to the actual model fitting function as a list of data
frames, or pass `imp`

directly. The latter works because **brms** offers special
support for data imputed by **mice**. We will go with the latter approach, since
it is less typing. Fitting our model of interest with **brms** to the multiple
imputed data sets is straightforward.

fit_imp1 <- brm_multiple(bmi ~ age*chl, data = imp, chains = 2)

The returned fitted model is an ordinary `brmsfit`

object containing the
posterior draws of all `m`

submodels. While pooling across models is not
necessarily straightforward in classical statistics, it is trivial in a Bayesian
framework. Here, pooling results of multiple imputed data sets is simply
achieved by combining the posterior draws of the submodels. Accordingly, all
post-processing methods can be used out of the box without having to worry about
pooling at all.

```
summary(fit_imp1)
```

In the summary output, we notice that some `Rhat`

values are higher than $1.1$
indicating possible convergence problems. For models based on multiple imputed
data sets, this is often a **false positive**: Chains of different submodels may
not overlay each other exactly, since there were fitted to different data. We
can see the chains on the right-hand side of

plot(fit_imp1, variable = "^b", regex = TRUE)

Such non-overlaying chains imply high `Rhat`

values without there actually being
any convergence issue. Accordingly, we have to investigate the convergence of
the submodels separately, which we can do by looking at

round(fit_imp1$rhats, 2)

The convergence of each of the submodels looks good. Accordingly, we can proceed
with further post-processing and interpretation of the results. For instance, we
could investigate the combined effect of `age`

and `chl`

.

conditional_effects(fit_imp1, "age:chl")

To summarize, the advantages of multiple imputation are obvious: One can apply it to all kinds of models, since model fitting functions do not need to know that the data sets were imputed, beforehand. Also, we do not need to worry about pooling across submodels when using fully Bayesian methods. The only drawback is the amount of time required for model fitting. Estimating Bayesian models is already quite slow with just a single data set and it only gets worse when working with multiple imputation.

**brms** offers built-in support for **mice** mainly because I use the latter in
some of my own research projects. Nevertheless, `brm_multiple`

supports all
kinds of multiple imputation packages as it also accepts a *list* of data frames
as input for its `data`

argument. Thus, you just need to extract the imputed
data frames in the form of a list, which can then be passed to `brm_multiple`

.
Most multiple imputation packages have some built-in functionality for this
task. When using the **mi** package, for instance, you simply need to call the
`mi::complete`

function to get the desired output.

Imputation during model fitting is generally thought to be more complex than
imputation before model fitting, because one has to take care of everything
within one step. This remains true when imputing missing values with **brms**,
but possibly to a somewhat smaller degree. Consider again the `nhanes`

data with
the goal to predict `bmi`

by `age`

, and `chl`

. Since `age`

contains no missing
values, we only have to take special care of `bmi`

and `chl`

. We need to tell
the model two things. (1) Which variables contain missing values and how they
should be predicted, as well as (2) which of these imputed variables should be
used as predictors. In **brms** we can do this as follows:

bform <- bf(bmi | mi() ~ age * mi(chl)) + bf(chl | mi() ~ age) + set_rescor(FALSE) fit_imp2 <- brm(bform, data = nhanes)

The model has become multivariate, as we no longer only predict `bmi`

but also
`chl`

(see `vignette("brms_multivariate")`

for details about the multivariate
syntax of **brms**). We ensure that missings in both variables will be modeled
rather than excluded by adding `| mi()`

on the left-hand side of the
formulas[^2]. We write `mi(chl)`

on the right-hand side of the formula for `bmi`

to ensure that the estimated missing values of `chl`

will be used in the
prediction of `bmi`

. The summary is a bit more cluttered as we get coefficients
for both response variables, but apart from that we can interpret coefficients
in the usual way.

summary(fit_imp2) conditional_effects(fit_imp2, "age:chl", resp = "bmi")

The results look pretty similar to those obtained from multiple imputation, but
be aware that this may not be generally the case. In multiple imputation, the
default is to impute all variables based on all other variables, while in the
'one-step' approach, we have to explicitly specify the variables used in the
imputation. Thus, arguably, multiple imputation is easier to apply. An obvious
advantage of the 'one-step' approach is that the model needs to be fitted only
once instead of `m`

times. Also, within the **brms** framework, we can use
multilevel structure and complex non-linear relationships for the imputation of
missing values, which is not achieved as easily in standard multiple imputation
software. On the downside, it is currently not possible to impute discrete
variables, because **Stan** (the engine behind **brms**) does not allow
estimating discrete parameters.

Missing value terms in **brms** cannot only handle missing values but also
measurement error, or arbitrary combinations of the two. In fact, we can think
of a missing value as a value with infinite measurement error. Thus, `mi`

terms
are a natural (and somewhat more verbose) generalization of the now soft deprecated
`me`

terms. Suppose we had measured the variable `chl`

with some known error:

nhanes$se <- rexp(nrow(nhanes), 2)

Then we can go ahead an include this information into the model as follows:

bform <- bf(bmi | mi() ~ age * mi(chl)) + bf(chl | mi(se) ~ age) + set_rescor(FALSE) fit_imp3 <- brm(bform, data = nhanes)

Summarizing and post-processing the model continues to work as usual.

[^1]: Actually, there is a third approach that only applies to missings in
response variables. If we want to impute missing responses, we just fit the
model using the observed responses and than impute the missings *after* fitting
the model by means of posterior prediction. That is, we supply the predictor
values corresponding to missing responses to the `predict`

method.

[^2]: We don't really need this for `bmi`

, since `bmi`

is not used as a
predictor for another variable. Accordingly, we could also -- and equivalently
-- impute missing values of `bmi`

*after* model fitting by means of posterior
prediction.

Buuren, S. V. & Groothuis-Oudshoorn, K. (2010). mice: Multivariate imputation by
chained equations in R. *Journal of Statistical Software*, 1-68.
doi.org/10.18637/jss.v045.i03

Zhou, X. & Reiter, J. P. (2010). A Note on Bayesian Inference After Multiple
Imputation. *The American Statistician*, 64(2), 159-163.
doi.org/10.1198/tast.2010.09109

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