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README.md
In broomExtra: Enhancements for 'broom' and 'easystats' Package Families
{broomExtra}: Enhancements for {broom} and {easystats} Package Families
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.
It combines functionality from {broom} and {easystats} ecosystems,
and this package has the following advantages over the underlying
individual packages (see examples below for concrete instantiations of
these benefits):
- Covers more number of regression models than these individual
packages.
- If possible to compute, the output tibbles contains a p-value
column.
- More robust to extraneous input arguments that might sometimes cause
problems for the underlying methods.
- Follows consistent
{tidymodels} column-naming schema.
- Returns a more comprehensive model performance measure summary.
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
| Type | Source | Command |
|-------------|--------|--------------------------------------------------------|
| Release | CRAN | install.packages("broomExtra") |
| Development | GitHub | remotes::install_github("IndrajeetPatil/broomExtra") |
Lifecycle
| Function | Lifecycle |
|-----------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------|
| tidy_parameters | |
| glance_performance | |
| tidy, glance, augment | |
| grouped_tidy, grouped_glance, grouped_augment | |
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:
-
broom::tidy + parameters::model_parameters =
broomExtra::tidy_parameters
-
broom::glance + performance::model_performance =
broomExtra::glance_performance
Benefits of using hybrid generics
-
The tidy_parameters will return a model summary either from
broomExtra::tidy or parameters::model_parameters, so you get
best of the both worlds.
-
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.
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)
library(broomExtra)
library(dplyr)
## 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 × 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 × 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 × 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 × 7
#> nobs sigma logLik AIC BIC REMLcrit df.residual
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 180 25.6 -872. 1756. 1775. 1744. 174
## linear model
lm.mod <- lm(Reaction ~ Days, sleepstudy)
broomExtra::glance(lm.mod)
#> # A tibble: 1 × 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.
#> deviance df.residual nobs
#> <dbl> <int> <int>
#> 1 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 × 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 × 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
#> .sqrtXwt .sqrtrwt .weights .wtres
#> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 1 -4.10
#> 2 1 1 1 -14.6
#> 3 1 1 1 -42.2
#> 4 1 1 1 8.78
#> 5 1 1 1 24.5
#> 6 1 1 1 62.7
#> 7 1 1 1 10.5
#> 8 1 1 1 -101.
#> 9 1 1 1 19.6
#> 10 1 1 1 35.7
#> # … with 170 more rows
## linear model
lm.mod <- lm(Reaction ~ Days, sleepstudy)
broomExtra::augment(lm.mod)
#> # A tibble: 180 × 8
#> Reaction Days .fitted .resid .hat .sigma .cooksd .std.resid
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 250. 0 251. -1.85 0.0192 47.8 0.0000149 -0.0390
#> 2 259. 1 262. -3.17 0.0138 47.8 0.0000313 -0.0669
#> 3 251. 2 272. -21.5 0.00976 47.8 0.00101 -0.454
#> 4 321. 3 283. 38.6 0.00707 47.8 0.00235 0.813
#> 5 357. 4 293. 63.6 0.00572 47.6 0.00514 1.34
#> 6 415. 5 304. 111. 0.00572 47.1 0.0157 2.33
#> 7 382. 6 314. 68.0 0.00707 47.6 0.00728 1.43
#> 8 290. 7 325. -34.5 0.00976 47.8 0.00261 -0.727
#> 9 431. 8 335. 95.4 0.0138 47.3 0.0284 2.01
#> 10 466. 9 346. 121. 0.0192 47.0 0.0639 2.56
#> # … 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 × 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)
library(broomExtra)
## linear model (tidy analysis across grouping combinations)
grouped_tidy(
data = sample_frac(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 × 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)
grouped_tidy(
data = sample_frac(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 × 9
#> cut effect group term estimate std.error statistic
#> <ord> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 Fair fixed <NA> carat 3800. 228. 16.7
#> 2 Fair ran_pars color sd__(Intercept) 2158. NA NA
#> 3 Fair ran_pars color cor__(Intercept).carat -0.975 NA NA
#> 4 Fair ran_pars color sd__carat 2545. NA NA
#> 5 Fair ran_pars Residual sd__Observation 1830. NA NA
#> 6 Good fixed <NA> carat 9217. 105. 87.6
#> 7 Good ran_pars color sd__(Intercept) 2686. NA NA
#> 8 Good ran_pars color cor__(Intercept).carat 0.998 NA NA
#> 9 Good ran_pars color sd__carat 1609. NA NA
#> 10 Good ran_pars Residual sd__Observation 1373. NA NA
#> conf.low conf.high
#> <dbl> <dbl>
#> 1 3212. 4387.
#> 2 NA NA
#> 3 NA NA
#> 4 NA NA
#> 5 NA NA
#> 6 8946. 9488.
#> 7 NA NA
#> 8 NA NA
#> 9 NA NA
#> 10 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)
grouped_glance(
data = sample_frac(ggplot2::diamonds, size = 0.5),
grouping.vars = c(cut, color),
formula = price ~ carat - 1,
..f = stats::lm,
na.action = na.omit
)
#> # A tibble: 35 × 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. NA NA NA -760.
#> 2 Fair E 0.876 0.875 1370. NA NA NA -872.
#> 3 Fair F 0.874 0.873 1989. NA NA NA -1406.
#> 4 Fair G 0.849 0.848 2138. NA NA NA -1444.
#> 5 Fair H 0.876 0.875 2412. NA NA NA -1307.
#> 6 Fair I 0.915 0.914 1499. NA NA NA -698.
#> 7 Fair J 0.885 0.883 2189. NA NA NA -501.
#> 8 Good D 0.860 0.860 1729. NA NA NA -2981.
#> 9 Good E 0.870 0.870 1674. NA NA NA -4084.
#> 10 Good F 0.873 0.873 1677. NA NA NA -3997.
#> AIC BIC deviance df.residual nobs
#> <dbl> <dbl> <dbl> <int> <int>
#> 1 1524. 1529. 289568733. 84 85
#> 2 1749. 1754. 187724139. 100 101
#> 3 2816. 2822. 613473518. 155 156
#> 4 2893. 2899. 722351124. 158 159
#> 5 2618. 2624. 820050299. 141 142
#> 6 1400. 1405. 177605917. 79 80
#> 7 1005. 1009. 258660541. 54 55
#> 8 5966. 5974. 1001144317. 335 336
#> 9 8173. 8181. 1291712250. 461 462
#> 10 7998. 8006. 1267954026. 451 452
#> # … with 25 more rows
## linear mixed effects model (model summaries across grouping combinations)
grouped_glance(
data = sample_frac(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 × 8
#> cut nobs sigma logLik AIC BIC REMLcrit df.residual
#> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 Fair 811 1830. -7257. 14525. 14548. 14515. 806
#> 2 Good 2430 1373. -21027. 42064. 42093. 42054. 2425
#> 3 Very Good 5969 1362. -51577. 103165. 103198. 103155. 5964
#> 4 Premium 6922 1557. -60736. 121482. 121516. 121472. 6917
#> 5 Ideal 10838 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
grouped_augment(
data = ggplot2::diamonds,
grouping.vars = c(cut, color),
..f = stats::lm,
formula = price ~ carat - 1
)
#> # A tibble: 53,940 × 10
#> cut color price carat .fitted .resid .hat .sigma .cooksd .std.resid
#> <ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Fair D 2848 0.75 3795. -947. 0.00342 1822. 0.000933 -0.522
#> 2 Fair D 2858 0.71 3593. -735. 0.00306 1823. 0.000503 -0.405
#> 3 Fair D 2885 0.9 4554. -1669. 0.00492 1819. 0.00419 -0.920
#> 4 Fair D 2974 1 5060. -2086. 0.00607 1816. 0.00809 -1.15
#> 5 Fair D 3003 1.01 5111. -2108. 0.00620 1816. 0.00843 -1.16
#> 6 Fair D 3047 0.73 3694. -647. 0.00324 1823. 0.000412 -0.356
#> 7 Fair D 3077 0.71 3593. -516. 0.00306 1823. 0.000248 -0.284
#> 8 Fair D 3079 0.91 4605. -1526. 0.00503 1820. 0.00358 -0.841
#> 9 Fair D 3205 0.9 4554. -1349. 0.00492 1821. 0.00274 -0.744
#> 10 Fair D 3205 0.9 4554. -1349. 0.00492 1821. 0.00274 -0.744
#> # … with 53,930 more rows
## linear mixed-effects model
grouped_augment(
data = sample_frac(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 × 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 0.00837 3519. 7001. 0
#> 2 Fair 1881 0.65 F 2104. -223. 0.00225 0.0000346 1505. 2104. 0
#> 3 Fair 2376 1.2 G 5439. -3063. 0.00651 0.0191 2778. 5439. 0
#> 4 Fair 1323 0.5 D 1069. 254. 0.00281 0.0000565 1158. 1069. 0
#> 5 Fair 3282 0.92 F 3935. -653. 0.00338 0.000448 2130. 3935. 0
#> 6 Fair 2500 0.7 H 2259. 241. 0.00219 0.0000396 1621. 2259. 0
#> 7 Fair 13853 1.5 F 7868. 5985. 0.0149 0.170 3473. 7868. 0
#> 8 Fair 3869 1.01 H 4052. -183. 0.00287 0.0000297 2338. 4052. 0
#> 9 Fair 1811 0.7 H 2259. -448. 0.00219 0.000137 1621. 2259. 0
#> 10 Fair 2788 1.01 E 4406. -1618. 0.0135 0.0112 2338. 4406. 0
#> .sqrtXwt .sqrtrwt .weights .wtres
#> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 1 1817.
#> 2 1 1 1 -223.
#> 3 1 1 1 -3063.
#> 4 1 1 1 254.
#> 5 1 1 1 -653.
#> 6 1 1 1 241.
#> 7 1 1 1 5985.
#> 8 1 1 1 -183.
#> 9 1 1 1 -448.
#> 10 1 1 1 -1618.
#> # … with 26,960 more rows
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!
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{broomExtra}: Enhancements for {broom} and {easystats} Package Families
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.
It combines functionality from {broom} and {easystats} ecosystems,
and this package has the following advantages over the underlying
individual packages (see examples below for concrete instantiations of
these benefits):
- Covers more number of regression models than these individual packages.
- If possible to compute, the output tibbles contains a p-value column.
- More robust to extraneous input arguments that might sometimes cause problems for the underlying methods.
- Follows consistent
{tidymodels}column-naming schema. - Returns a more comprehensive model performance measure summary.
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
| Type | Source | Command |
|-------------|--------|--------------------------------------------------------|
| Release | CRAN | install.packages("broomExtra") |
| Development | GitHub | remotes::install_github("IndrajeetPatil/broomExtra") |
Lifecycle
| Function | Lifecycle |
|-----------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------|
| tidy_parameters | |
| glance_performance | |
| tidy, glance, augment | |
| grouped_tidy, grouped_glance, grouped_augment | |
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:
-
broom::tidy+parameters::model_parameters=broomExtra::tidy_parameters -
broom::glance+performance::model_performance=broomExtra::glance_performance
Benefits of using hybrid generics
-
The
tidy_parameterswill return a model summary either frombroomExtra::tidyorparameters::model_parameters, so you get best of the both worlds. -
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 aroundbroomExtra::tidyorparameters::model_parameters.
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)
library(broomExtra)
library(dplyr)
## 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 × 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 × 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 × 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 × 7
#> nobs sigma logLik AIC BIC REMLcrit df.residual
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 180 25.6 -872. 1756. 1775. 1744. 174
## linear model
lm.mod <- lm(Reaction ~ Days, sleepstudy)
broomExtra::glance(lm.mod)
#> # A tibble: 1 × 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.
#> deviance df.residual nobs
#> <dbl> <int> <int>
#> 1 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 × 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 × 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
#> .sqrtXwt .sqrtrwt .weights .wtres
#> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 1 -4.10
#> 2 1 1 1 -14.6
#> 3 1 1 1 -42.2
#> 4 1 1 1 8.78
#> 5 1 1 1 24.5
#> 6 1 1 1 62.7
#> 7 1 1 1 10.5
#> 8 1 1 1 -101.
#> 9 1 1 1 19.6
#> 10 1 1 1 35.7
#> # … with 170 more rows
## linear model
lm.mod <- lm(Reaction ~ Days, sleepstudy)
broomExtra::augment(lm.mod)
#> # A tibble: 180 × 8
#> Reaction Days .fitted .resid .hat .sigma .cooksd .std.resid
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 250. 0 251. -1.85 0.0192 47.8 0.0000149 -0.0390
#> 2 259. 1 262. -3.17 0.0138 47.8 0.0000313 -0.0669
#> 3 251. 2 272. -21.5 0.00976 47.8 0.00101 -0.454
#> 4 321. 3 283. 38.6 0.00707 47.8 0.00235 0.813
#> 5 357. 4 293. 63.6 0.00572 47.6 0.00514 1.34
#> 6 415. 5 304. 111. 0.00572 47.1 0.0157 2.33
#> 7 382. 6 314. 68.0 0.00707 47.6 0.00728 1.43
#> 8 290. 7 325. -34.5 0.00976 47.8 0.00261 -0.727
#> 9 431. 8 335. 95.4 0.0138 47.3 0.0284 2.01
#> 10 466. 9 346. 121. 0.0192 47.0 0.0639 2.56
#> # … 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 × 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)
library(broomExtra)
## linear model (tidy analysis across grouping combinations)
grouped_tidy(
data = sample_frac(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 × 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)
grouped_tidy(
data = sample_frac(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 × 9
#> cut effect group term estimate std.error statistic
#> <ord> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 Fair fixed <NA> carat 3800. 228. 16.7
#> 2 Fair ran_pars color sd__(Intercept) 2158. NA NA
#> 3 Fair ran_pars color cor__(Intercept).carat -0.975 NA NA
#> 4 Fair ran_pars color sd__carat 2545. NA NA
#> 5 Fair ran_pars Residual sd__Observation 1830. NA NA
#> 6 Good fixed <NA> carat 9217. 105. 87.6
#> 7 Good ran_pars color sd__(Intercept) 2686. NA NA
#> 8 Good ran_pars color cor__(Intercept).carat 0.998 NA NA
#> 9 Good ran_pars color sd__carat 1609. NA NA
#> 10 Good ran_pars Residual sd__Observation 1373. NA NA
#> conf.low conf.high
#> <dbl> <dbl>
#> 1 3212. 4387.
#> 2 NA NA
#> 3 NA NA
#> 4 NA NA
#> 5 NA NA
#> 6 8946. 9488.
#> 7 NA NA
#> 8 NA NA
#> 9 NA NA
#> 10 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)
grouped_glance(
data = sample_frac(ggplot2::diamonds, size = 0.5),
grouping.vars = c(cut, color),
formula = price ~ carat - 1,
..f = stats::lm,
na.action = na.omit
)
#> # A tibble: 35 × 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. NA NA NA -760.
#> 2 Fair E 0.876 0.875 1370. NA NA NA -872.
#> 3 Fair F 0.874 0.873 1989. NA NA NA -1406.
#> 4 Fair G 0.849 0.848 2138. NA NA NA -1444.
#> 5 Fair H 0.876 0.875 2412. NA NA NA -1307.
#> 6 Fair I 0.915 0.914 1499. NA NA NA -698.
#> 7 Fair J 0.885 0.883 2189. NA NA NA -501.
#> 8 Good D 0.860 0.860 1729. NA NA NA -2981.
#> 9 Good E 0.870 0.870 1674. NA NA NA -4084.
#> 10 Good F 0.873 0.873 1677. NA NA NA -3997.
#> AIC BIC deviance df.residual nobs
#> <dbl> <dbl> <dbl> <int> <int>
#> 1 1524. 1529. 289568733. 84 85
#> 2 1749. 1754. 187724139. 100 101
#> 3 2816. 2822. 613473518. 155 156
#> 4 2893. 2899. 722351124. 158 159
#> 5 2618. 2624. 820050299. 141 142
#> 6 1400. 1405. 177605917. 79 80
#> 7 1005. 1009. 258660541. 54 55
#> 8 5966. 5974. 1001144317. 335 336
#> 9 8173. 8181. 1291712250. 461 462
#> 10 7998. 8006. 1267954026. 451 452
#> # … with 25 more rows
## linear mixed effects model (model summaries across grouping combinations)
grouped_glance(
data = sample_frac(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 × 8
#> cut nobs sigma logLik AIC BIC REMLcrit df.residual
#> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 Fair 811 1830. -7257. 14525. 14548. 14515. 806
#> 2 Good 2430 1373. -21027. 42064. 42093. 42054. 2425
#> 3 Very Good 5969 1362. -51577. 103165. 103198. 103155. 5964
#> 4 Premium 6922 1557. -60736. 121482. 121516. 121472. 6917
#> 5 Ideal 10838 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
grouped_augment(
data = ggplot2::diamonds,
grouping.vars = c(cut, color),
..f = stats::lm,
formula = price ~ carat - 1
)
#> # A tibble: 53,940 × 10
#> cut color price carat .fitted .resid .hat .sigma .cooksd .std.resid
#> <ord> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Fair D 2848 0.75 3795. -947. 0.00342 1822. 0.000933 -0.522
#> 2 Fair D 2858 0.71 3593. -735. 0.00306 1823. 0.000503 -0.405
#> 3 Fair D 2885 0.9 4554. -1669. 0.00492 1819. 0.00419 -0.920
#> 4 Fair D 2974 1 5060. -2086. 0.00607 1816. 0.00809 -1.15
#> 5 Fair D 3003 1.01 5111. -2108. 0.00620 1816. 0.00843 -1.16
#> 6 Fair D 3047 0.73 3694. -647. 0.00324 1823. 0.000412 -0.356
#> 7 Fair D 3077 0.71 3593. -516. 0.00306 1823. 0.000248 -0.284
#> 8 Fair D 3079 0.91 4605. -1526. 0.00503 1820. 0.00358 -0.841
#> 9 Fair D 3205 0.9 4554. -1349. 0.00492 1821. 0.00274 -0.744
#> 10 Fair D 3205 0.9 4554. -1349. 0.00492 1821. 0.00274 -0.744
#> # … with 53,930 more rows
## linear mixed-effects model
grouped_augment(
data = sample_frac(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 × 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 0.00837 3519. 7001. 0
#> 2 Fair 1881 0.65 F 2104. -223. 0.00225 0.0000346 1505. 2104. 0
#> 3 Fair 2376 1.2 G 5439. -3063. 0.00651 0.0191 2778. 5439. 0
#> 4 Fair 1323 0.5 D 1069. 254. 0.00281 0.0000565 1158. 1069. 0
#> 5 Fair 3282 0.92 F 3935. -653. 0.00338 0.000448 2130. 3935. 0
#> 6 Fair 2500 0.7 H 2259. 241. 0.00219 0.0000396 1621. 2259. 0
#> 7 Fair 13853 1.5 F 7868. 5985. 0.0149 0.170 3473. 7868. 0
#> 8 Fair 3869 1.01 H 4052. -183. 0.00287 0.0000297 2338. 4052. 0
#> 9 Fair 1811 0.7 H 2259. -448. 0.00219 0.000137 1621. 2259. 0
#> 10 Fair 2788 1.01 E 4406. -1618. 0.0135 0.0112 2338. 4406. 0
#> .sqrtXwt .sqrtrwt .weights .wtres
#> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 1 1817.
#> 2 1 1 1 -223.
#> 3 1 1 1 -3063.
#> 4 1 1 1 254.
#> 5 1 1 1 -653.
#> 6 1 1 1 241.
#> 7 1 1 1 5985.
#> 8 1 1 1 -183.
#> 9 1 1 1 -448.
#> 10 1 1 1 -1618.
#> # … with 26,960 more rows
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!
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