broomExtra: Enhancements for broom and easystats Package Families

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Raison d’être

The goal of broomExtra is to provide helper functions that assist in data analysis workflows involving regression analyses. The goal is to combine the functionality offered by different set of packages through a common syntax to return tidy tibbles containing model parameters and summaries.

The package internally relies on the following packages that I contribute to:

Since it combines functionality from these two ecosystems, this package has the following advantages over the underlying individual packages (see examples below for concrete instantiations of these benefits):

If you want to add support for a regression model, the natural place to do this would be to contribute either to broom or to parameters.

Installation

To get the latest, stable CRAN release:

install.packages("broomExtra")

You can get the development version of the package from GitHub. To see what new changes (and bug fixes) have been made to the package since the last release on CRAN, you can check the detailed log of changes here: https://indrajeetpatil.github.io/broomExtra/news/index.html

If you are in hurry and want to reduce the time of installation, prefer-

# needed package to download from GitHub repo
install.packages("remotes")
remotes::install_github(
  repo = "IndrajeetPatil/broomExtra", # package path on GitHub
  quick = TRUE # skips docs, demos, and vignettes
)

If time is not a constraint-

remotes::install_github(
  repo = "IndrajeetPatil/broomExtra", # package path on GitHub
  dependencies = TRUE, # installs packages which broomExtra depends on
  upgrade_dependencies = TRUE # updates any out of date dependencies
)

Otherwise, the quicker option is-

remotes::install_github("IndrajeetPatil/broomExtra")

hybrid generics

The broom-family of packages are not the only ones which return such tidy summaries for model parameters and model performance. The easystats-family of packages also provide similar functions, more specifically parameters and performance. Sometimes the broom packages might not contain a tidy/glance method for a given regression object, while easystats packages would and vice versa.

The hybrid functions in broomExtra make it easy to retrieve these summaries with the appropriate method and do so robustly:

Benefits of using tidy_parameters

The tidy_parameters will return a model summary either from broomExtra::tidy or parameters::model_parameters.

Sometimes the method will not be available in broom, while it will be in parameters:

# mixor object
set.seed(123)
library("mixor")
data("SmokingPrevention")

# data frame must be sorted by id variable
SmokingPrevention <- SmokingPrevention[order(SmokingPrevention$class), ]

# school model
mod_mixor <-
  mixor(
    formula = thksord ~ thkspre + cc + tv + cctv,
    data = SmokingPrevention,
    id = school, link = "logit"
  )

# tidier in `broom`-family?
broomExtra::tidy(mod_mixor)
#> NULL

# using hybrid function
broomExtra::tidy_parameters(mod_mixor)
#> # A tibble: 8 x 9
#>   term               estimate std.error conf.low conf.high statistic df.error p.value effects
#>   <chr>                 <dbl>     <dbl>    <dbl>     <dbl>     <dbl>    <dbl>   <dbl> <chr>  
#> 1 (Intercept)          0.0882    0.313   -0.526      0.702     0.282      Inf  0.778  fixed  
#> 2 Threshold2           1.24      0.0883   1.07       1.41     14.1        Inf  0      fixed  
#> 3 Threshold3           2.42      0.0836   2.26       2.58     28.9        Inf  0      fixed  
#> 4 thkspre              0.403     0.0429   0.319      0.487     9.39       Inf  0      fixed  
#> 5 cc                   0.924     0.371    0.196      1.65      2.49       Inf  0.0128 fixed  
#> 6 tv                   0.275     0.315   -0.342      0.893     0.873      Inf  0.383  fixed  
#> 7 cctv                -0.466     0.406   -1.26       0.330    -1.15       Inf  0.251  fixed  
#> 8 Random.(Intercept)   0.0735    0.0495  -0.0235     0.170     1.49       Inf  0.137  random

While other times, it will be the other way around:

# model
library(orcutt)
set.seed(123)
reg <- stats::lm(formula = mpg ~ wt + qsec + disp, data = mtcars)
co <- orcutt::cochrane.orcutt(reg)

# no tidier available in `parameters`
parameters::model_parameters(co)
#> Error in `[.data.frame`(md, , needed.vars, drop = FALSE): undefined columns selected

# `tidy_parameters` still won't fail
broomExtra::tidy_parameters(co)
#> # A tibble: 4 x 5
#>   term        estimate std.error statistic p.value
#>   <chr>          <dbl>     <dbl>     <dbl>   <dbl>
#> 1 (Intercept) 21.8        6.63       3.29  0.00279
#> 2 wt          -4.85       1.33      -3.65  0.00112
#> 3 qsec         0.797      0.370      2.15  0.0402 
#> 4 disp        -0.00136    0.0110    -0.123 0.903

These functions are robust such that they won’t fail if the ... contains misspecified arguments.

This makes these functions much easier to work with while writing wrapper functions around broomExtra::tidy or parameters::model_parameters.

# setup
set.seed(123)
library(lavaan)

# model specs
HS.model <- " visual  =~ x1 + x2 + x3
              textual =~ x4 + x5 + x6
              speed   =~ x7 + x8 + x9 "

# model
mod_lavaan <-
  lavaan(
    HS.model,
    data = HolzingerSwineford1939,
    auto.var = TRUE,
    auto.fix.first = TRUE,
    auto.cov.lv.x = TRUE
  )

# tidy method with additional arguments
broom::tidy(mod_lavaan, exponentiate = TRUE)
#> Error in lavaan::parameterEstimates(x, ci = conf.int, level = conf.level, : unused argument (exponentiate = TRUE)

# parameters method with additional arguments
parameters::model_parameters(mod_lavaan, exponentiate = TRUE)
#> Error in lavaan::parameterEstimates(model, se = TRUE, level = ci, ...): unused argument (exponentiate = TRUE)

# using hybrid function
broomExtra::tidy_parameters(mod_lavaan, exponentiate = TRUE)
#> # A tibble: 12 x 9
#>    to      operator from    estimate std.error conf.low conf.high  p.value type       
#>    <chr>   <chr>    <chr>      <dbl>     <dbl>    <dbl>     <dbl>    <dbl> <chr>      
#>  1 visual  =~       x1         1        0        1          1     0.       Loading    
#>  2 visual  =~       x2         0.554    0.0997   0.358      0.749 2.80e- 8 Loading    
#>  3 visual  =~       x3         0.729    0.109    0.516      0.943 2.31e-11 Loading    
#>  4 textual =~       x4         1        0        1          1     0.       Loading    
#>  5 textual =~       x5         1.11     0.0654   0.985      1.24  0.       Loading    
#>  6 textual =~       x6         0.926    0.0554   0.817      1.03  0.       Loading    
#>  7 speed   =~       x7         1        0        1          1     0.       Loading    
#>  8 speed   =~       x8         1.18     0.165    0.857      1.50  8.56e-13 Loading    
#>  9 speed   =~       x9         1.08     0.151    0.785      1.38  8.40e-13 Loading    
#> 10 visual  ~~       textual    0.408    0.0735   0.264      0.552 2.82e- 8 Correlation
#> 11 visual  ~~       speed      0.262    0.0563   0.152      0.373 3.17e- 6 Correlation
#> 12 textual ~~       speed      0.173    0.0493   0.0768     0.270 4.35e- 4 Correlation

Additionally, the p-values and confidence intervals for regression estimates are consistently included.

# setup
set.seed(123)
library(MASS)
mod <- rlm(stack.loss ~ ., stackloss)

# broom output (no p-values present)
broomExtra::tidy(mod, conf.int = TRUE)
#> # A tibble: 4 x 6
#>   term        estimate std.error statistic conf.low conf.high
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>     <dbl>
#> 1 (Intercept)  -41.0       9.81     -4.18   -60.2     -21.8  
#> 2 Air.Flow       0.829     0.111     7.46     0.611     1.05 
#> 3 Water.Temp     0.926     0.303     3.05     0.331     1.52 
#> 4 Acid.Conc.    -0.128     0.129    -0.992   -0.380     0.125

# using `tidy_parameters` (p-values present)
broomExtra::tidy_parameters(mod)
#> # A tibble: 4 x 8
#>   term        estimate std.error conf.low conf.high statistic df.error     p.value
#>   <chr>          <dbl>     <dbl>    <dbl>     <dbl>     <dbl>    <int>       <dbl>
#> 1 (Intercept)  -41.0       9.81   -61.7     -20.3      -4.18        17 0.000624   
#> 2 Air.Flow       0.829     0.111    0.595     1.06      7.46        17 0.000000933
#> 3 Water.Temp     0.926     0.303    0.286     1.57      3.05        17 0.00720    
#> 4 Acid.Conc.    -0.128     0.129   -0.400     0.144    -0.992       17 0.335

Benefits of using glance_performance

The glance_performance will return a model summary either from broom::glance or performance::model_performance.

# mixor object
set.seed(123)
library("mixor")
data("SmokingPrevention")

# data frame must be sorted by id variable
SmokingPrevention <- SmokingPrevention[order(SmokingPrevention$class), ]

# school model
mod_mixor <-
  mixor(
    formula = thksord ~ thkspre + cc + tv + cctv,
    data = SmokingPrevention,
    id = school, link = "logit"
  )

# glance method in `broom`-family?
broomExtra::glance(mod_mixor)
#> NULL

# using hybrid function
broomExtra::glance_performance(mod_mixor)
#> # A tibble: 1 x 2
#>      aic    bic
#>    <dbl>  <dbl>
#> 1 -2128. -2133.

Sometimes the method will be available in broom, but not in easystats, but glance_performance will manage to choose the appropriate method for you. For example-

# model
library(orcutt)
set.seed(123)
reg <- stats::lm(formula = mpg ~ wt + qsec + disp, data = mtcars)
co <- orcutt::cochrane.orcutt(reg)

# no method available in `performance`
performance::model_performance(co)
#> Error in UseMethod("model_performance"): no applicable method for 'model_performance' applied to an object of class "orcutt"

# `glance_performance` doesn't fail
broomExtra::glance_performance(co)
#> # A tibble: 1 x 9
#>   r.squared adj.r.squared   rho number.interaction dw.original p.value.original dw.transformed p.value.transformed  nobs
#>       <dbl>         <dbl> <dbl>              <dbl>       <dbl>            <dbl>          <dbl>               <dbl> <int>
#> 1     0.799         0.777 0.268                  7        1.50           0.0406           2.06               0.521    32

Additionally, it will return model summary that contains combined metrics from these two packages.

For example, some unique performance measures are present only in the performance package contains (e.g., Nagelkerke’s R-squared, Tjur’s R-squared, etc.), but not broom package and vice versa.

# setup
set.seed(123)

# model
model <-
  stats::glm(
    formula = am ~ wt + cyl,
    data = mtcars,
    family = binomial
  )

# `broom` output
broomExtra::glance(model)
#> # A tibble: 1 x 8
#>   null.deviance df.null logLik   AIC   BIC deviance df.residual  nobs
#>           <dbl>   <int>  <dbl> <dbl> <dbl>    <dbl>       <int> <int>
#> 1          43.2      31  -7.37  20.7  25.1     14.7          29    32

# combined output
broomExtra::glance_performance(model)
#> # A tibble: 1 x 14
#>   null.deviance df.null loglik   aic   bic deviance df.residual  nobs r2.tjur  rmse logloss score.log score.spherical   pcp
#>           <dbl>   <int>  <dbl> <dbl> <dbl>    <dbl>       <int> <int>   <dbl> <dbl>   <dbl>     <dbl>           <dbl> <dbl>
#> 1          43.2      31  -7.37  20.7  25.1     14.7          29    32   0.705 0.678   0.230     -19.0           0.116 0.858

generic functions

Currently, S3 methods for mixed-effects model objects are included in the broom.mixed package, while the rest of the object classes are included in the broom package. This means that you constantly need to keep track of the class of the object (e.g., “if it is merMod object, use broom.mixed::tidy()/broom.mixed::glance()/broom.mixed::augment(), but if it is polr object, use broom::tidy()/broom::glance()/broom::augment()”).

Using generics from broomExtra means you no longer have to worry about this, as calling broomExtra::tidy()/broomExtra::glance()/broomExtra::augment() will search the appropriate method from these two packages and return the results.

tidy dataframe

Let’s get a tidy tibble back containing results from various regression models.

set.seed(123)
library(lme4)
library(ordinal)

# mixed-effects models (`broom.mixed` will be used)
lmm.mod <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
broomExtra::tidy(x = lmm.mod, effects = "fixed")
#> # A tibble: 2 x 5
#>   effect term        estimate std.error statistic
#>   <chr>  <chr>          <dbl>     <dbl>     <dbl>
#> 1 fixed  (Intercept)    251.       6.82     36.8 
#> 2 fixed  Days            10.5      1.55      6.77

# linear model (`broom` will be used)
lm.mod <- lm(Reaction ~ Days, sleepstudy)
broomExtra::tidy(lm.mod, conf.int = TRUE)
#> # A tibble: 2 x 7
#>   term        estimate std.error statistic  p.value conf.low conf.high
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
#> 1 (Intercept)    251.       6.61     38.0  2.16e-87   238.       264. 
#> 2 Days            10.5      1.24      8.45 9.89e-15     8.02      12.9

# another example with `broom`
# cumulative Link Models
clm.mod <- clm(rating ~ temp * contact, data = wine)
broomExtra::tidy(x = clm.mod, exponentiate = TRUE)
#> # A tibble: 7 x 6
#>   term                estimate std.error statistic  p.value coef.type
#>   <chr>                  <dbl>     <dbl>     <dbl>    <dbl> <chr>    
#> 1 1|2                    0.244     0.545    -2.59  9.66e- 3 intercept
#> 2 2|3                    3.14      0.510     2.24  2.48e- 2 intercept
#> 3 3|4                   29.3       0.638     5.29  1.21e- 7 intercept
#> 4 4|5                  140.        0.751     6.58  4.66e-11 intercept
#> 5 tempwarm              10.2       0.701     3.31  9.28e- 4 location 
#> 6 contactyes             3.85      0.660     2.04  4.13e- 2 location 
#> 7 tempwarm:contactyes    1.43      0.924     0.389 6.97e- 1 location

# unsupported object (the function will return `NULL` in such cases)
broomExtra::tidy(list(1, c("x", "y")))
#> NULL

model summaries

Getting a tibble containing model summary and other performance measures.

set.seed(123)
library(lme4)
library(ordinal)

# mixed-effects model
lmm.mod <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
broomExtra::glance(lmm.mod)
#> # A tibble: 1 x 6
#>   sigma logLik   AIC   BIC REMLcrit df.residual
#>   <dbl>  <dbl> <dbl> <dbl>    <dbl>       <int>
#> 1  25.6  -872. 1756. 1775.    1744.         174

# linear model
lm.mod <- lm(Reaction ~ Days, sleepstudy)
broomExtra::glance(lm.mod)
#> # A tibble: 1 x 12
#>   r.squared adj.r.squared sigma statistic  p.value    df logLik   AIC   BIC deviance df.residual  nobs
#>       <dbl>         <dbl> <dbl>     <dbl>    <dbl> <dbl>  <dbl> <dbl> <dbl>    <dbl>       <int> <int>
#> 1     0.286         0.282  47.7      71.5 9.89e-15     1  -950. 1906. 1916.  405252.         178   180

# another example with `broom`
# cumulative Link Models
clm.mod <- clm(rating ~ temp * contact, data = wine)
broomExtra::glance(clm.mod)
#> # A tibble: 1 x 6
#>     edf   AIC   BIC logLik   df.residual  nobs
#>   <int> <dbl> <dbl> <logLik>       <dbl> <dbl>
#> 1     7  187.  203. -86.4162          65    72

# in case no glance method is available (`NULL` will be returned)
broomExtra::glance(acf(lh, plot = FALSE))
#> NULL

augmented dataframe

Getting a tibble by augmenting data with information from an object.

set.seed(123)
library(lme4)
library(ordinal)

# mixed-effects model
lmm.mod <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
broomExtra::augment(lmm.mod)
#> # A tibble: 180 x 14
#>    Reaction  Days Subject .fitted  .resid   .hat .cooksd .fixed   .mu .offset .sqrtXwt .sqrtrwt .weights  .wtres
#>       <dbl> <dbl> <fct>     <dbl>   <dbl>  <dbl>   <dbl>  <dbl> <dbl>   <dbl>    <dbl>    <dbl>    <dbl>   <dbl>
#>  1     250.     0 308        254.   -4.10 0.229  0.00496   251.  254.       0        1        1        1   -4.10
#>  2     259.     1 308        273.  -14.6  0.170  0.0402    262.  273.       0        1        1        1  -14.6 
#>  3     251.     2 308        293.  -42.2  0.127  0.226     272.  293.       0        1        1        1  -42.2 
#>  4     321.     3 308        313.    8.78 0.101  0.00731   283.  313.       0        1        1        1    8.78
#>  5     357.     4 308        332.   24.5  0.0910 0.0506    293.  332.       0        1        1        1   24.5 
#>  6     415.     5 308        352.   62.7  0.0981 0.362     304.  352.       0        1        1        1   62.7 
#>  7     382.     6 308        372.   10.5  0.122  0.0134    314.  372.       0        1        1        1   10.5 
#>  8     290.     7 308        391. -101.   0.162  1.81      325.  391.       0        1        1        1 -101.  
#>  9     431.     8 308        411.   19.6  0.219  0.106     335.  411.       0        1        1        1   19.6 
#> 10     466.     9 308        431.   35.7  0.293  0.571     346.  431.       0        1        1        1   35.7 
#> # ... with 170 more rows

# linear model
lm.mod <- lm(Reaction ~ Days, sleepstudy)
broomExtra::augment(lm.mod)
#> # A tibble: 180 x 8
#>    Reaction  Days .fitted .resid .std.resid    .hat .sigma   .cooksd
#>       <dbl> <dbl>   <dbl>  <dbl>      <dbl>   <dbl>  <dbl>     <dbl>
#>  1     250.     0    251.  -1.85    -0.0390 0.0192    47.8 0.0000149
#>  2     259.     1    262.  -3.17    -0.0669 0.0138    47.8 0.0000313
#>  3     251.     2    272. -21.5     -0.454  0.00976   47.8 0.00101  
#>  4     321.     3    283.  38.6      0.813  0.00707   47.8 0.00235  
#>  5     357.     4    293.  63.6      1.34   0.00572   47.6 0.00514  
#>  6     415.     5    304. 111.       2.33   0.00572   47.1 0.0157   
#>  7     382.     6    314.  68.0      1.43   0.00707   47.6 0.00728  
#>  8     290.     7    325. -34.5     -0.727  0.00976   47.8 0.00261  
#>  9     431.     8    335.  95.4      2.01   0.0138    47.3 0.0284   
#> 10     466.     9    346. 121.       2.56   0.0192    47.0 0.0639   
#> # ... with 170 more rows

# another example with `broom`
# cumulative Link Models
clm.mod <- clm(rating ~ temp * contact, data = wine)
broomExtra::augment(x = clm.mod, newdata = wine, type.predict = "prob")
#> # A tibble: 72 x 7
#>    response rating temp  contact bottle judge .fitted
#>       <dbl> <ord>  <fct> <fct>   <fct>  <fct>   <dbl>
#>  1       36 2      cold  no      1      1      0.562 
#>  2       48 3      cold  no      2      1      0.209 
#>  3       47 3      cold  yes     3      1      0.435 
#>  4       67 4      cold  yes     4      1      0.0894
#>  5       77 4      warm  no      5      1      0.190 
#>  6       60 4      warm  no      6      1      0.190 
#>  7       83 5      warm  yes     7      1      0.286 
#>  8       90 5      warm  yes     8      1      0.286 
#>  9       17 1      cold  no      1      2      0.196 
#> 10       22 2      cold  no      2      2      0.562 
#> # ... with 62 more rows

# in case no augment method is available (`NULL` will be returned)
broomExtra::augment(stats::anova(stats::lm(wt ~ am, mtcars)))
#> NULL

grouped_ variants of generics

grouped variants of the generic functions (tidy, glance, and augment) make it easy to execute the same analysis for all combinations of grouping variable(s) in a dataframe. Currently, these functions work only for methods that depend on a data argument (e.g., stats::lm), but not for functions that don’t (e.g., stats::prop.test()).

grouped_tidy

# to speed up computation, let's use only 50% of the data
set.seed(123)
library(lme4)
library(ggplot2)

# linear model (tidy analysis across grouping combinations)
broomExtra::grouped_tidy(
  data = dplyr::sample_frac(tbl = ggplot2::diamonds, size = 0.5),
  grouping.vars = c(cut, color),
  formula = price ~ carat - 1,
  ..f = stats::lm,
  na.action = na.omit,
  tidy.args = list(quick = TRUE)
)
#> # A tibble: 35 x 7
#>    cut   color term  estimate std.error statistic   p.value
#>    <ord> <ord> <chr>    <dbl>     <dbl>     <dbl>     <dbl>
#>  1 Fair  D     carat    5246.     207.       25.3 4.45e- 41
#>  2 Fair  E     carat    4202.     158.       26.6 3.52e- 47
#>  3 Fair  F     carat    4877.     149.       32.7 1.68e- 71
#>  4 Fair  G     carat    4538.     152.       29.8 1.03e- 66
#>  5 Fair  H     carat    4620.     146.       31.6 7.68e- 66
#>  6 Fair  I     carat    3969.     136.       29.2 4.86e- 44
#>  7 Fair  J     carat    4024.     197.       20.4 4.80e- 27
#>  8 Good  D     carat    5207.     115.       45.4 2.66e-145
#>  9 Good  E     carat    5102.      91.9      55.5 2.50e-206
#> 10 Good  F     carat    5151.      92.4      55.8 1.76e-204
#> # ... with 25 more rows

# linear mixed effects model (tidy analysis across grouping combinations)
broomExtra::grouped_tidy(
  data = dplyr::sample_frac(tbl = ggplot2::diamonds, size = 0.5),
  grouping.vars = cut,
  ..f = lme4::lmer,
  formula = price ~ carat + (carat | color) - 1,
  control = lme4::lmerControl(optimizer = "bobyqa"),
  tidy.args = list(conf.int = TRUE, conf.level = 0.99)
)
#> # A tibble: 25 x 9
#>    cut   effect   group    term                   estimate std.error statistic conf.low conf.high
#>    <ord> <chr>    <chr>    <chr>                     <dbl>     <dbl>     <dbl>    <dbl>     <dbl>
#>  1 Fair  fixed    <NA>     carat                  3800.         228.      16.7    3212.     4387.
#>  2 Fair  ran_pars color    sd__(Intercept)        2158.          NA       NA        NA        NA 
#>  3 Fair  ran_pars color    cor__(Intercept).carat   -0.975       NA       NA        NA        NA 
#>  4 Fair  ran_pars color    sd__carat              2545.          NA       NA        NA        NA 
#>  5 Fair  ran_pars Residual sd__Observation        1830.          NA       NA        NA        NA 
#>  6 Good  fixed    <NA>     carat                  9217.         105.      87.6    8946.     9488.
#>  7 Good  ran_pars color    sd__(Intercept)        2686.          NA       NA        NA        NA 
#>  8 Good  ran_pars color    cor__(Intercept).carat    0.998       NA       NA        NA        NA 
#>  9 Good  ran_pars color    sd__carat              1609.          NA       NA        NA        NA 
#> 10 Good  ran_pars Residual sd__Observation        1373.          NA       NA        NA        NA 
#> # ... with 15 more rows

grouped_glance

# to speed up computation, let's use only 50% of the data
set.seed(123)

# linear model (model summaries across grouping combinations)
broomExtra::grouped_glance(
  data = dplyr::sample_frac(tbl = ggplot2::diamonds, size = 0.5),
  grouping.vars = c(cut, color),
  formula = price ~ carat - 1,
  ..f = stats::lm,
  na.action = na.omit
)
#> # A tibble: 35 x 14
#>    cut   color r.squared adj.r.squared sigma statistic p.value    df logLik   AIC   BIC    deviance df.residual  nobs
#>    <ord> <ord>     <dbl>         <dbl> <dbl>     <dbl>   <dbl> <dbl>  <dbl> <dbl> <dbl>       <dbl>       <int> <int>
#>  1 Fair  D         0.884         0.883 1857.        NA      NA    NA  -760. 1524. 1529.  289568733.          84    85
#>  2 Fair  E         0.876         0.875 1370.        NA      NA    NA  -872. 1749. 1754.  187724139.         100   101
#>  3 Fair  F         0.874         0.873 1989.        NA      NA    NA -1406. 2816. 2822.  613473518.         155   156
#>  4 Fair  G         0.849         0.848 2138.        NA      NA    NA -1444. 2893. 2899.  722351124.         158   159
#>  5 Fair  H         0.876         0.875 2412.        NA      NA    NA -1307. 2618. 2624.  820050299.         141   142
#>  6 Fair  I         0.915         0.914 1499.        NA      NA    NA  -698. 1400. 1405.  177605917.          79    80
#>  7 Fair  J         0.885         0.883 2189.        NA      NA    NA  -501. 1005. 1009.  258660541.          54    55
#>  8 Good  D         0.860         0.860 1729.        NA      NA    NA -2981. 5966. 5974. 1001144317.         335   336
#>  9 Good  E         0.870         0.870 1674.        NA      NA    NA -4084. 8173. 8181. 1291712250.         461   462
#> 10 Good  F         0.873         0.873 1677.        NA      NA    NA -3997. 7998. 8006. 1267954026.         451   452
#> # ... with 25 more rows

# linear mixed effects model (model summaries across grouping combinations)
broomExtra::grouped_glance(
  data = dplyr::sample_frac(tbl = ggplot2::diamonds, size = 0.5),
  grouping.vars = cut,
  ..f = lme4::lmer,
  formula = price ~ carat + (carat | color) - 1,
  control = lme4::lmerControl(optimizer = "bobyqa")
)
#> # A tibble: 5 x 7
#>   cut       sigma  logLik     AIC     BIC REMLcrit df.residual
#>   <ord>     <dbl>   <dbl>   <dbl>   <dbl>    <dbl>       <int>
#> 1 Fair      1830.  -7257.  14525.  14548.   14515.         806
#> 2 Good      1373. -21027.  42064.  42093.   42054.        2425
#> 3 Very Good 1362. -51577. 103165. 103198.  103155.        5964
#> 4 Premium   1557. -60736. 121482. 121516.  121472.        6917
#> 5 Ideal     1257. -92766. 185542. 185579.  185532.       10833

grouped_augment

# to speed up computation, let's use only 50% of the data
set.seed(123)

# linear model
broomExtra::grouped_augment(
  data = ggplot2::diamonds,
  grouping.vars = c(cut, color),
  ..f = stats::lm,
  formula = price ~ carat - 1
)
#> # A tibble: 53,940 x 10
#>    cut   color price carat .fitted .resid .std.resid    .hat .sigma  .cooksd
#>    <ord> <ord> <int> <dbl>   <dbl>  <dbl>      <dbl>   <dbl>  <dbl>    <dbl>
#>  1 Fair  D      2848  0.75   3795.  -947.     -0.522 0.00342  1822. 0.000933
#>  2 Fair  D      2858  0.71   3593.  -735.     -0.405 0.00306  1823. 0.000503
#>  3 Fair  D      2885  0.9    4554. -1669.     -0.920 0.00492  1819. 0.00419 
#>  4 Fair  D      2974  1      5060. -2086.     -1.15  0.00607  1816. 0.00809 
#>  5 Fair  D      3003  1.01   5111. -2108.     -1.16  0.00620  1816. 0.00843 
#>  6 Fair  D      3047  0.73   3694.  -647.     -0.356 0.00324  1823. 0.000412
#>  7 Fair  D      3077  0.71   3593.  -516.     -0.284 0.00306  1823. 0.000248
#>  8 Fair  D      3079  0.91   4605. -1526.     -0.841 0.00503  1820. 0.00358 
#>  9 Fair  D      3205  0.9    4554. -1349.     -0.744 0.00492  1821. 0.00274 
#> 10 Fair  D      3205  0.9    4554. -1349.     -0.744 0.00492  1821. 0.00274 
#> # ... with 53,930 more rows

# linear mixed-effects model
broomExtra::grouped_augment(
  data = dplyr::sample_frac(tbl = ggplot2::diamonds, size = 0.5),
  grouping.vars = cut,
  ..f = lme4::lmer,
  formula = price ~ carat + (carat | color) - 1,
  control = lme4::lmerControl(optimizer = "bobyqa")
)
#> # A tibble: 26,970 x 15
#>    cut   price carat color .fitted .resid    .hat   .cooksd .fixed   .mu .offset .sqrtXwt .sqrtrwt .weights .wtres
#>    <ord> <int> <dbl> <ord>   <dbl>  <dbl>   <dbl>     <dbl>  <dbl> <dbl>   <dbl>    <dbl>    <dbl>    <dbl>  <dbl>
#>  1 Fair   8818  1.52 H       7001.  1817. 0.00806 0.00837    3519. 7001.       0        1        1        1  1817.
#>  2 Fair   1881  0.65 F       2104.  -223. 0.00225 0.0000346  1505. 2104.       0        1        1        1  -223.
#>  3 Fair   2376  1.2  G       5439. -3063. 0.00651 0.0191     2778. 5439.       0        1        1        1 -3063.
#>  4 Fair   1323  0.5  D       1069.   254. 0.00281 0.0000565  1158. 1069.       0        1        1        1   254.
#>  5 Fair   3282  0.92 F       3935.  -653. 0.00338 0.000448   2130. 3935.       0        1        1        1  -653.
#>  6 Fair   2500  0.7  H       2259.   241. 0.00219 0.0000396  1621. 2259.       0        1        1        1   241.
#>  7 Fair  13853  1.5  F       7868.  5985. 0.0149  0.170      3473. 7868.       0        1        1        1  5985.
#>  8 Fair   3869  1.01 H       4052.  -183. 0.00287 0.0000297  2338. 4052.       0        1        1        1  -183.
#>  9 Fair   1811  0.7  H       2259.  -448. 0.00219 0.000137   1621. 2259.       0        1        1        1  -448.
#> 10 Fair   2788  1.01 E       4406. -1618. 0.0135  0.0112     2338. 4406.       0        1        1        1 -1618.
#> # ... with 26,960 more rows

Code coverage

As the code stands right now, here is the code coverage for all primary functions involved: https://codecov.io/gh/IndrajeetPatil/broomExtra/tree/master/R

Contributing

I’m happy to receive bug reports, suggestions, questions, and (most of all) contributions to fix problems and add features. I personally prefer using the GitHub issues system over trying to reach out to me in other ways (personal e-mail, Twitter, etc.). Pull requests for contributions are encouraged.

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

Acknowledgments

The hexsticker was generously designed by Sarah Otterstetter (Max Planck Institute for Human Development, Berlin). Thanks are also due to the maintainers and contributors to broom- and easystats-package families who have indulged in all my feature requests. 😄