`broom.mixed`

**UNDER CONSTRUCTION**

`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.

- 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)

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.

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 | ? | ? | ? |