Introduction to broom.mixed
Author: Ben Bolker
Date: 2026-02-16

# Introduction to `broom.mixed`

#### Ben Bolker

#### 2026-02-16

# Introduction

`broom.mixed` is a spinoff of the broom package. The goal of `broom` is to bring the modeling process into a “tidy”(TM) workflow, in particular by providing standardized verbs that provide information on

- `tidy`: estimates, standard errors, confidence intervals, etc.
- `augment`: residuals, fitted values, influence measures, etc.
- `glance`: whole-model summaries: AIC, R-squared, etc.

`broom.mixed` aims to provide these methods for as many packages and model types in the R ecosystem as possible. These methods have been separated from those in the main `broom` package because there are issues that need to be dealt with for these models (e.g. different types of parameters: fixed, random-effects parameters, conditional modes/BLUPs of random effects, etc.) that are not especially relevant to the broader universe of models that `broom` deals with.

# Mixed-model-specific issues

## Terminology

- the upper-level parameters that describe the distribution of random variables (variance, covariance, precision, standard deviation, or correlation) are called random-effect parameters (`ran_pars` in the `effects` argument when tidying)
- the values that describe the deviation of the observations in a group level from the population-level effect (which could be posterior means or medians, conditional modes, or BLUPs depending on the model) are called random-effect values (`ran_vals` in the `effects` argument when tidying)
- the parameters that describe the population-level effects of (categorical and continuous) predictor variables are called fixed effects (`fixed` in the `effects` argument when tidying)
- the categorical variable (factor) that identifies which group or cluster an observation belongs to is called a grouping variable (`group` column in `tidy()` output)
- the particular level of a factor that specifies which level of the grouping variable an observation belongs to is called a group level (`level` column in `tidy()` output)
- the categorical or continuous predictor variables that control the expected value (i.e., enter into the linear predictor for some part of the model) are called terms (`term` column in `tidy()` output); note that unlike in base `broom`, the term column may have duplicated values, because the same term may enter multiple model components (e.g. zero-inflated and conditional models; models for more than one response; fixed effects and random effects)

## Time-consuming computations

Some kinds of computations needed for mixed model summaries are computationally expensive, e.g. likelihood profiles or parametric bootstrapping. In this case `broom.mixed` may offer an option for passing a pre-computed object to `tidy()`, eg. the `profile` argument in the `tidy.merMod` (`lmer`/`glmer`) method.

# Related packages

There are many, many things one might want to do with a fitted model, and `broom.mixed` can only do a few of them.

- `emmeans`
- `multcomp`
- `car`
- `afex`
- `sjStats`/`sjPlots`
- `rockchalk`

## huxtable + broom.mixed

## dotwhisker + broom.mixed

`dotwhisker` is a convenient platform for creating dot-whisker plots - either directly from models or lists of models (`tidy()` methods are automatically called to convert the models to a tidy format), or from the (possibly post-processed) output of a `tidy()` call. There are a couple of caveats and issues to be aware of when using `dotwhisker` in conjunction with `broom.mixed`, however.

1. For fixed effects, the `group` value is set to `NA`: in the current CRAN version (0.5.0), an unfortunate `na.omit()` within the `dwplot` code will eliminate all of the fixed effects unless you drop this column before passing the results to `dwplot` (this has been fixed in the current GitHub version, which you can install with `devtools::install_github("fsolt/dotwhisker")`).
2. In `broom.mixed` output, it is fairly common for a single tidied model to have duplicated entries in the `term` column (e.g. effects that appear in both the conditional and the zero-inflated model, or intercept standard deviations for several different grouping variables). `dotwhisker::dwplot` takes this as evidence that it has been handed a tidied object containing the results from several different models, and asks for a `model` column that will distinguish the non-unique terms. There are (at least) two strategies you can take:
   - `dwplot(list(fitted_model))` will plot all of the non-unique values together
   - `tidy(fitted_model) %>% tidyr::unite(term, group, term)` will create a new `term` column that’s the combination of the `group` and `term` columns (which will disambiguate random-effect terms from different grouping variables); `unite(term, component, term)` will disambiguate conditional and zero-inflation parameters. The code below shows a slightly more complicated (but prettier) approach. (Some sort of `disambiguate_terms()` function could be added in a future version of the package …)

    library(dplyr)
    library(tidyr)
    require(rstan) ## workaround for r-devel problem
    library(broom.mixed)
    if (require("brms") && require("dotwhisker") && require("ggplot2")) {
        L <- load(system.file("extdata", "brms_example.rda", package="broom.mixed"))
        gg0 <- (tidy(brms_crossedRE)
            ## disambiguate
            %>% mutate(term=ifelse(grepl("sd__(Int",term,fixed=TRUE),
                                   paste(group,term,sep="."),
                                   term))
            %>% dwplot
        )
        gg0 + geom_vline(xintercept=0,lty=2)
    }

# Capabilities

Automatically retrieve table of available methods:

    get_methods()

    ## # A tibble: 27 × 4
    ##    class       tidy  glance augment
    ##    <chr>       <lgl> <lgl>  <lgl>
    ##  1 MCMCglmm    TRUE  FALSE  FALSE
    ##  2 MixMod      TRUE  FALSE  FALSE
    ##  3 TMB         TRUE  TRUE   FALSE
    ##  4 VarCorr.lme TRUE  FALSE  FALSE
    ##  5 allFit      TRUE  TRUE   FALSE
    ##  6 brmsfit     TRUE  TRUE   TRUE
    ##  7 clmm        FALSE FALSE  TRUE
    ##  8 gamlss      TRUE  TRUE   FALSE
    ##  9 gamm4       TRUE  TRUE   TRUE
    ## 10 glmm        TRUE  FALSE  FALSE
    ## # ℹ 17 more rows

Manually compiled list of capabilities (possibly out of date):

package | object | tidy | glance | augment | effects.fixed | effects.ran_vals | effects.ran_pars | effects.ran_coefs | confint…Ww.ald. | confint..profile. | confint..boot. | component.zi | component.disp | covstruct
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
lme4 | glmer | y | y | y | y | y | y | y | NA | y | NA | NA | NA | ?
lme4 | lmer | y | y | y | y | y | y | y | NA | y | NA | NA | NA | ?
nlme | lme | y | y | y | y | y | y | y | NA | n | NA | NA | ? | ?
nlme | gls | y | y | y | y | NA | NA | NA | NA | n | NA | NA | ? | ?
nlme | nlme | y | y | y | y | y | n | y | NA | n | NA | NA | ? | ?
glmmTMB | glmmTMB | y | y | y | y | y | y | n | NA |  | NA | y | y | ?
glmmADMB | glmmadmb | y | y | y | y | y | y | n | NA |  | NA | y | ? | ?
brms | brmsfit | y | y | y | y | y | y | n | NA |  | NA | y | ? | ?
rstanarm | stanreg | y | y | y | y | y | y | n | NA |  | NA | NA | NA | ?
MCMCglmm | MCMCglmm | y | y | y | y | y | y | n | NA |  | NA | ? | ? | ?
TMB | TMB | y | n | n | n | n | n | n | NA |  | NA | NA | NA | ?
INLA |  | n | n | n | n | n | n | n | NA |  | NA | ? | ? | ?