broomExtra: Enhancements for broom and easystats Package Families| Package | Status | Usage | GitHub | References |
|---|---|---|---|---|
|
|
|
||
|
||||
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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:
broombroom.mixedparametersperformanceSince 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):
tidymodels column-naming schema.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.
To get the latest, stable CRAN release:
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upgrade_dependencies = TRUE # updates any out of date dependencies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-
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
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
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 0.0882 0.313 -0.526 0.702 0.282 Inf
#> 2 Threshold2 1.24 0.0883 1.07 1.41 14.1 Inf
#> 3 Threshold3 2.42 0.0836 2.26 2.58 28.9 Inf
#> 4 thkspre 0.403 0.0429 0.319 0.487 9.39 Inf
#> 5 cc 0.924 0.371 0.196 1.65 2.49 Inf
#> 6 tv 0.275 0.315 -0.342 0.893 0.873 Inf
#> 7 cctv -0.466 0.406 -1.26 0.330 -1.15 Inf
#> 8 Random.(Intercept) 0.0735 0.0495 -0.0235 0.170 1.49 Inf
#> p.value effects
#> <dbl> <chr>
#> 1 0.778 fixed
#> 2 0 fixed
#> 3 0 fixed
#> 4 0 fixed
#> 5 0.0128 fixed
#> 6 0.383 fixed
#> 7 0.251 fixed
#> 8 0.137 randomWhile 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
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 visual =~ x1 1 0 1 1 0.
#> 2 visual =~ x2 0.554 0.0997 0.358 0.749 2.80e- 8
#> 3 visual =~ x3 0.729 0.109 0.516 0.943 2.31e-11
#> 4 textual =~ x4 1 0 1 1 0.
#> 5 textual =~ x5 1.11 0.0654 0.985 1.24 0.
#> 6 textual =~ x6 0.926 0.0554 0.817 1.03 0.
#> 7 speed =~ x7 1 0 1 1 0.
#> 8 speed =~ x8 1.18 0.165 0.857 1.50 8.56e-13
#> 9 speed =~ x9 1.08 0.151 0.785 1.38 8.40e-13
#> 10 visual ~~ textual 0.408 0.0735 0.264 0.552 2.82e- 8
#> 11 visual ~~ speed 0.262 0.0563 0.152 0.373 3.17e- 6
#> 12 textual ~~ speed 0.173 0.0493 0.0768 0.270 4.35e- 4
#> type
#> <chr>
#> 1 Loading
#> 2 Loading
#> 3 Loading
#> 4 Loading
#> 5 Loading
#> 6 Loading
#> 7 Loading
#> 8 Loading
#> 9 Loading
#> 10 Correlation
#> 11 Correlation
#> 12 CorrelationAdditionally, 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
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 (Intercept) -41.0 9.81 -61.7 -20.3 -4.18 17
#> 2 Air.Flow 0.829 0.111 0.595 1.06 7.46 17
#> 3 Water.Temp 0.926 0.303 0.286 1.57 3.05 17
#> 4 Acid.Conc. -0.128 0.129 -0.400 0.144 -0.992 17
#> p.value
#> <dbl>
#> 1 0.000624
#> 2 0.000000933
#> 3 0.00720
#> 4 0.335glance_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
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0.799 0.777 0.268 7 1.50 0.0406
#> dw.transformed p.value.transformed nobs
#> <dbl> <dbl> <int>
#> 1 2.06 0.521 32Additionally, 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
#> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <int> <int> <dbl>
#> 1 43.2 31 -7.37 20.7 25.1 14.7 29 32 0.705
#> rmse logloss score.log score.spherical pcp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0.678 0.230 -19.0 0.116 0.858Currently, 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.
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")))
#> NULLGetting 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.
#> 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 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))
#> NULLGetting 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
#> .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 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)))
#> NULLgrouped_ variants of genericsgrouped 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
#> <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 rowsgrouped_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. 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)
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. 10833grouped_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 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 rowsAs the code stands right now, here is the code coverage for all primary functions involved: https://codecov.io/gh/IndrajeetPatil/broomExtra/tree/master/R
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.
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. 😄