broomExtra: Enhancements for broom Package Family

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The goal of broomExtra is to provide helper functions that assist in data analysis workflows involving packages broom and broom.mixed.

Installation

To get the latest, stable CRAN release:

utils::install.packages(pkgs = "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
utils::install.packages(pkgs = "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  upgrade_dependencies = TRUE # updates any out of date dependencies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")

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(x = 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,
  conf.int = TRUE,
  conf.type = "Wald"
)
#> # A tibble: 7 x 8
#>   term        estimate std.error statistic  p.value conf.low conf.high coef.type
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl> <chr>    
#> 1 1|2            0.244     0.545    -2.59  9.66e- 3   0.0837     0.710 intercept
#> 2 2|3            3.14      0.510     2.24  2.48e- 2   1.16       8.52  intercept
#> 3 3|4           29.3       0.638     5.29  1.21e- 7   8.38     102.    intercept
#> 4 4|5          140.        0.751     6.58  4.66e-11  32.1      610.    intercept
#> 5 tempwarm      10.2       0.701     3.31  9.28e- 4   2.58      40.2   location 
#> 6 contactyes     3.85      0.660     2.04  4.13e- 2   1.05      14.0   location 
#> 7 tempwarm:c~    1.43      0.924     0.389 6.97e- 1   0.234      8.76  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
#>       <dbl>         <dbl> <dbl>     <dbl>    <dbl> <dbl>  <dbl> <dbl> <dbl>
#> 1     0.286         0.282  47.7      71.5 9.89e-15     1  -950. 1906. 1916.
#> # ... with 3 more variables: deviance <dbl>, df.residual <int>, nobs <int>

# 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>  <dbl>       <dbl> <dbl>
#> 1     7  187.  203.  -86.4          65    72

# in case no glance method is available (`NULL` will be returned)
broomExtra::glance(stats::anova(stats::lm(wt ~ am, mtcars)))
#> 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
#>       <dbl> <dbl> <fct>     <dbl>   <dbl>  <dbl>   <dbl>  <dbl> <dbl>   <dbl>
#>  1     250.     0 308        254.   -4.10 0.229  0.00496   251.  254.       0
#>  2     259.     1 308        273.  -14.6  0.170  0.0402    262.  273.       0
#>  3     251.     2 308        293.  -42.2  0.127  0.226     272.  293.       0
#>  4     321.     3 308        313.    8.78 0.101  0.00731   283.  313.       0
#>  5     357.     4 308        332.   24.5  0.0910 0.0506    293.  332.       0
#>  6     415.     5 308        352.   62.7  0.0981 0.362     304.  352.       0
#>  7     382.     6 308        372.   10.5  0.122  0.0134    314.  372.       0
#>  8     290.     7 308        391. -101.   0.162  1.81      325.  391.       0
#>  9     431.     8 308        411.   19.6  0.219  0.106     335.  411.       0
#> 10     466.     9 308        431.   35.7  0.293  0.571     346.  431.       0
#> # ... with 170 more rows, and 4 more variables: .sqrtXwt <dbl>, .sqrtrwt <dbl>,
#> #   .weights <dbl>, .wtres <dbl>

# 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

hybrid generics

broom-family of packages are not the only ones which return such tidy summaries for model parameters and performance. There is also easystats-family of packages that provide similar functions, more specifically parameters and performance. Sometimes although broom packages might not contain a method for a given regression object, easystats packages would and vice versa. The hybrid functions in broomExtra make it easy to retrieve these summaries with the appropriate method and does so robustly.

tidy_parameters

The tidy_parameters will return a model summary either from broom::tidy or parameters::model_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 8
#>   term           estimate std.error conf.low conf.high statistic p.value effects
#>   <chr>             <dbl>     <dbl>    <dbl>     <dbl>     <dbl>   <dbl> <chr>  
#> 1 (Intercept)      0.0882    0.313   -0.526      0.702     0.282  0.778  fixed  
#> 2 Threshold2       1.24      0.0883   1.07       1.41     14.1    0      fixed  
#> 3 Threshold3       2.42      0.0836   2.26       2.58     28.9    0      fixed  
#> 4 thkspre          0.403     0.0429   0.319      0.487     9.39   0      fixed  
#> 5 cc               0.924     0.371    0.196      1.65      2.49   0.0128 fixed  
#> 6 tv               0.275     0.315   -0.342      0.893     0.873  0.383  fixed  
#> 7 cctv            -0.466     0.406   -1.26       0.330    -1.15   0.251  fixed  
#> 8 Random.(Inter~   0.0735    0.0495  -0.0235     0.170     1.49   0.137  random

These functions are also pretty 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 broom::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: 24 x 11
#>    term  op    estimate std.error statistic   p.value conf.low conf.high std.lv
#>    <chr> <chr>    <dbl>     <dbl>     <dbl>     <dbl>    <dbl>     <dbl>  <dbl>
#>  1 visu~ =~       1        0          NA    NA           1         1      0.900
#>  2 visu~ =~       0.554    0.0997      5.55  2.80e- 8    0.358     0.749  0.498
#>  3 visu~ =~       0.729    0.109       6.68  2.31e-11    0.516     0.943  0.656
#>  4 text~ =~       1        0          NA    NA           1         1      0.990
#>  5 text~ =~       1.11     0.0654     17.0   0.          0.985     1.24   1.10 
#>  6 text~ =~       0.926    0.0554     16.7   0.          0.817     1.03   0.917
#>  7 spee~ =~       1        0          NA    NA           1         1      0.619
#>  8 spee~ =~       1.18     0.165       7.15  8.56e-13    0.857     1.50   0.731
#>  9 spee~ =~       1.08     0.151       7.15  8.40e-13    0.785     1.38   0.670
#> 10 x1 ~~ ~~       0.549    0.114       4.83  1.34e- 6    0.326     0.772  0.549
#> # ... with 14 more rows, and 2 more variables: std.all <dbl>, std.nox <dbl>

Additional benefit of using this function is that it will also make sure that 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 7
#>   term        estimate std.error statistic conf.low conf.high     p.value
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>     <dbl>       <dbl>
#> 1 (Intercept)  -41.0       9.81     -4.18   -60.2     -21.8   0.000624   
#> 2 Air.Flow       0.829     0.111     7.46     0.611     1.05  0.000000933
#> 3 Water.Temp     0.926     0.303     3.05     0.331     1.52  0.00720    
#> 4 Acid.Conc.    -0.128     0.129    -0.992   -0.380     0.125 0.335

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.

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_pa~ color  sd__(In~ 2158.          NA       NA        NA        NA 
#>  3 Fair  ran_pa~ color  cor__(I~   -0.975       NA       NA        NA        NA 
#>  4 Fair  ran_pa~ color  sd__car~ 2545.          NA       NA        NA        NA 
#>  5 Fair  ran_pa~ Resid~ sd__Obs~ 1830.          NA       NA        NA        NA 
#>  6 Good  fixed   <NA>   carat    9217.         105.      87.6    8946.     9488.
#>  7 Good  ran_pa~ color  sd__(In~ 2686.          NA       NA        NA        NA 
#>  8 Good  ran_pa~ color  cor__(I~    0.998       NA       NA        NA        NA 
#>  9 Good  ran_pa~ color  sd__car~ 1609.          NA       NA        NA        NA 
#> 10 Good  ran_pa~ Resid~ sd__Obs~ 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
#>    <ord> <ord>     <dbl>         <dbl> <dbl>     <dbl>     <dbl> <dbl>  <dbl>
#>  1 Fair  D         0.884         0.883 1857.      641. 4.45e- 41     1  -760.
#>  2 Fair  E         0.876         0.875 1370.      708. 3.52e- 47     1  -872.
#>  3 Fair  F         0.874         0.873 1989.     1071. 1.68e- 71     1 -1406.
#>  4 Fair  G         0.849         0.848 2138.      887. 1.03e- 66     1 -1444.
#>  5 Fair  H         0.876         0.875 2412.      998. 7.68e- 66     1 -1307.
#>  6 Fair  I         0.915         0.914 1499.      850. 4.86e- 44     1  -698.
#>  7 Fair  J         0.885         0.883 2189.      416. 4.80e- 27     1  -501.
#>  8 Good  D         0.860         0.860 1729.     2065. 2.66e-145     1 -2981.
#>  9 Good  E         0.870         0.870 1674.     3084. 2.50e-206     1 -4084.
#> 10 Good  F         0.873         0.873 1677.     3110. 1.76e-204     1 -3997.
#> # ... with 25 more rows, and 5 more variables: AIC <dbl>, BIC <dbl>,
#> #   deviance <dbl>, df.residual <int>, nobs <int>

# 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
#>    <ord> <int> <dbl> <ord>   <dbl>  <dbl>   <dbl>   <dbl>  <dbl> <dbl>   <dbl>
#>  1 Fair   8818  1.52 H       7001.  1817. 0.00806 8.37e-3  3519. 7001.       0
#>  2 Fair   1881  0.65 F       2104.  -223. 0.00225 3.46e-5  1505. 2104.       0
#>  3 Fair   2376  1.2  G       5439. -3063. 0.00651 1.91e-2  2778. 5439.       0
#>  4 Fair   1323  0.5  D       1069.   254. 0.00281 5.65e-5  1158. 1069.       0
#>  5 Fair   3282  0.92 F       3935.  -653. 0.00338 4.48e-4  2130. 3935.       0
#>  6 Fair   2500  0.7  H       2259.   241. 0.00219 3.96e-5  1621. 2259.       0
#>  7 Fair  13853  1.5  F       7868.  5985. 0.0149  1.70e-1  3473. 7868.       0
#>  8 Fair   3869  1.01 H       4052.  -183. 0.00287 2.97e-5  2338. 4052.       0
#>  9 Fair   1811  0.7  H       2259.  -448. 0.00219 1.37e-4  1621. 2259.       0
#> 10 Fair   2788  1.01 E       4406. -1618. 0.0135  1.12e-2  2338. 4406.       0
#> # ... with 26,960 more rows, and 4 more variables: .sqrtXwt <dbl>,
#> #   .sqrtrwt <dbl>, .weights <dbl>, .wtres <dbl>

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.