The tidycat package: expand broom::tidy() output for categorical parameter estimates
Introduction
The tidycat package includes the tidy_categorical() function to expand broom::tidy() outputs for categorical parameter estimates.
Installation
You can install the released version of tidycat from CRAN with:
install.packages("tidycat")And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("guyabel/tidycat")Additional columns for categorical parameter estimates
The tidy() function in the broom package takes the messy output of built-in functions in R, such as lm(), and turns them into tidy data frames.
library(dplyr)
library(broom)
m0 <- esoph %>%
mutate_if(is.factor, ~factor(., ordered = FALSE)) %>%
glm(cbind(ncases, ncontrols) ~ agegp + tobgp + alcgp, data = ., family = binomial())
# tidy
tidy(m0)
#> # A tibble: 12 x 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) -5.91 1.03 -5.74 9.61e- 9
#> 2 agegp35-44 1.61 1.07 1.51 1.32e- 1
#> 3 agegp45-54 2.98 1.02 2.90 3.68e- 3
#> 4 agegp55-64 3.36 1.02 3.29 9.91e- 4
#> 5 agegp65-74 3.73 1.03 3.64 2.78e- 4
#> 6 agegp75+ 3.68 1.06 3.46 5.43e- 4
#> 7 tobgp10-19 0.341 0.205 1.66 9.72e- 2
#> 8 tobgp20-29 0.396 0.246 1.61 1.07e- 1
#> 9 tobgp30+ 0.868 0.277 3.14 1.70e- 3
#> 10 alcgp40-79 1.12 0.238 4.70 2.55e- 6
#> 11 alcgp80-119 1.45 0.263 5.51 3.68e- 8
#> 12 alcgp120+ 2.12 0.288 7.36 1.90e-13Note: Currently ordered factor not supported in tidycat, hence their removal in mutate_if() above
The tidy_categorical() function adds further columns (variable, level and effect) to the broom::tidy() output to help manage categorical variables
library(tidycat)
m0 %>%
tidy() %>%
tidy_categorical(m = m0, include_reference = FALSE)
#> # A tibble: 12 x 8
#> term estimate std.error statistic p.value variable level effect
#> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <fct> <chr>
#> 1 (Intercep~ -5.91 1.03 -5.74 9.61e- 9 (Intercept) (Interce~ main
#> 2 agegp35-44 1.61 1.07 1.51 1.32e- 1 agegp 35-44 main
#> 3 agegp45-54 2.98 1.02 2.90 3.68e- 3 agegp 45-54 main
#> 4 agegp55-64 3.36 1.02 3.29 9.91e- 4 agegp 55-64 main
#> 5 agegp65-74 3.73 1.03 3.64 2.78e- 4 agegp 65-74 main
#> 6 agegp75+ 3.68 1.06 3.46 5.43e- 4 agegp 75+ main
#> 7 tobgp10-19 0.341 0.205 1.66 9.72e- 2 tobgp 10-19 main
#> 8 tobgp20-29 0.396 0.246 1.61 1.07e- 1 tobgp 20-29 main
#> 9 tobgp30+ 0.868 0.277 3.14 1.70e- 3 tobgp 30+ main
#> 10 alcgp40-79 1.12 0.238 4.70 2.55e- 6 alcgp 40-79 main
#> 11 alcgp80-1~ 1.45 0.263 5.51 3.68e- 8 alcgp 80-119 main
#> 12 alcgp120+ 2.12 0.288 7.36 1.90e-13 alcgp 120+ mainAdditional rows for reference categories
Include additional rows for reference category terms and a column to indicate their location by setting include_reference = TRUE (default). Setting exponentiate = TRUE ensures the parameter estimates in the reference group are set to one instead of zero (even odds in the logistic regression example below).
m0 %>%
tidy(exponentiate = TRUE) %>%
tidy_categorical(m = m0, exponentiate = TRUE, reference_label = "Baseline") %>%
select(-statistic, -p.value)
#> # A tibble: 15 x 7
#> term estimate std.error variable level effect reference
#> <chr> <dbl> <dbl> <chr> <fct> <chr> <chr>
#> 1 (Intercept) 0.00271 1.03 (Intercept) (Intercept) main Non-Baseline
#> 2 <NA> 1 1 agegp 25-34 main Baseline
#> 3 agegp35-44 5.00 1.07 agegp 35-44 main Non-Baseline
#> 4 agegp45-54 19.6 1.02 agegp 45-54 main Non-Baseline
#> 5 agegp55-64 28.7 1.02 agegp 55-64 main Non-Baseline
#> 6 agegp65-74 41.6 1.03 agegp 65-74 main Non-Baseline
#> 7 agegp75+ 39.7 1.06 agegp 75+ main Non-Baseline
#> 8 <NA> 1 1 tobgp 0-9g/day main Baseline
#> 9 tobgp10-19 1.41 0.205 tobgp 10-19 main Non-Baseline
#> 10 tobgp20-29 1.49 0.246 tobgp 20-29 main Non-Baseline
#> 11 tobgp30+ 2.38 0.277 tobgp 30+ main Non-Baseline
#> 12 <NA> 1 1 alcgp 0-39g/day main Baseline
#> 13 alcgp40-79 3.07 0.238 alcgp 40-79 main Non-Baseline
#> 14 alcgp80-119 4.25 0.263 alcgp 80-119 main Non-Baseline
#> 15 alcgp120+ 8.29 0.288 alcgp 120+ main Non-BaselineStandard coefficient plots
The results from broom::tidy() can be used to quickly plot estimated coefficients and their confidence intervals.
# store parameter estimates and confidence intervals (except for the intercept)
d0 <- m0 %>%
tidy(conf.int = TRUE) %>%
slice(-1)
d0
#> # A tibble: 11 x 7
#> term estimate std.error statistic p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 agegp35-44 1.61 1.07 1.51 1.32e- 1 -0.100 4.54
#> 2 agegp45-54 2.98 1.02 2.90 3.68e- 3 1.41 5.86
#> 3 agegp55-64 3.36 1.02 3.29 9.91e- 4 1.81 6.24
#> 4 agegp65-74 3.73 1.03 3.64 2.78e- 4 2.16 6.62
#> 5 agegp75+ 3.68 1.06 3.46 5.43e- 4 1.99 6.61
#> 6 tobgp10-19 0.341 0.205 1.66 9.72e- 2 -0.0644 0.742
#> 7 tobgp20-29 0.396 0.246 1.61 1.07e- 1 -0.0935 0.872
#> 8 tobgp30+ 0.868 0.277 3.14 1.70e- 3 0.319 1.41
#> 9 alcgp40-79 1.12 0.238 4.70 2.55e- 6 0.665 1.60
#> 10 alcgp80-119 1.45 0.263 5.51 3.68e- 8 0.939 1.97
#> 11 alcgp120+ 2.12 0.288 7.36 1.90e-13 1.56 2.69
library(ggplot2)
library(tidyr)
ggplot(data = d0,
mapping = aes(x = term, y = estimate, ymin = conf.low, ymax = conf.high)) +
coord_flip() +
geom_hline(yintercept = 0, linetype = "dashed") +
geom_pointrange()
Enhanced coefficient plots
The additional columns from tidy_categorical() can be used to group together terms from the same categorical variable by setting colour = variable
d0 <- m0 %>%
tidy(conf.int = TRUE) %>%
tidy_categorical(m = m0, include_reference = FALSE) %>%
slice(-1)
d0 %>%
select(-(3:5))
#> # A tibble: 11 x 7
#> term estimate conf.low conf.high variable level effect
#> <chr> <dbl> <dbl> <dbl> <chr> <fct> <chr>
#> 1 agegp35-44 1.61 -0.100 4.54 agegp 35-44 main
#> 2 agegp45-54 2.98 1.41 5.86 agegp 45-54 main
#> 3 agegp55-64 3.36 1.81 6.24 agegp 55-64 main
#> 4 agegp65-74 3.73 2.16 6.62 agegp 65-74 main
#> 5 agegp75+ 3.68 1.99 6.61 agegp 75+ main
#> 6 tobgp10-19 0.341 -0.0644 0.742 tobgp 10-19 main
#> 7 tobgp20-29 0.396 -0.0935 0.872 tobgp 20-29 main
#> 8 tobgp30+ 0.868 0.319 1.41 tobgp 30+ main
#> 9 alcgp40-79 1.12 0.665 1.60 alcgp 40-79 main
#> 10 alcgp80-119 1.45 0.939 1.97 alcgp 80-119 main
#> 11 alcgp120+ 2.12 1.56 2.69 alcgp 120+ main
ggplot(data = d0,
mapping = aes(x = term, y = estimate, ymin = conf.low, ymax = conf.high,
colour = variable)) +
coord_flip() +
geom_hline(yintercept = 0, linetype = "dashed") +
geom_pointrange()
The additional rows from tidy_categorical() can be used to include the reference categories in a coefficient plot, allowing the reader to better grasp the meaning of the parameter estimates in each categorical variable. Using ggforce::facet_col() the terms of each variable can be separated to further improve the presentation of the coefficient plot.
d0 <- m0 %>%
tidy(conf.int = TRUE) %>%
tidy_categorical(m = m0) %>%
slice(-1)
d0 %>%
select(-(3:5))
#> # A tibble: 14 x 8
#> term estimate conf.low conf.high variable level effect reference
#> <chr> <dbl> <dbl> <dbl> <chr> <fct> <chr> <chr>
#> 1 <NA> 0 0 0 agegp 25-34 main Baseline Categ~
#> 2 agegp35-~ 1.61 -0.100 4.54 agegp 35-44 main Non-Baseline C~
#> 3 agegp45-~ 2.98 1.41 5.86 agegp 45-54 main Non-Baseline C~
#> 4 agegp55-~ 3.36 1.81 6.24 agegp 55-64 main Non-Baseline C~
#> 5 agegp65-~ 3.73 2.16 6.62 agegp 65-74 main Non-Baseline C~
#> 6 agegp75+ 3.68 1.99 6.61 agegp 75+ main Non-Baseline C~
#> 7 <NA> 0 0 0 tobgp 0-9g/d~ main Baseline Categ~
#> 8 tobgp10-~ 0.341 -0.0644 0.742 tobgp 10-19 main Non-Baseline C~
#> 9 tobgp20-~ 0.396 -0.0935 0.872 tobgp 20-29 main Non-Baseline C~
#> 10 tobgp30+ 0.868 0.319 1.41 tobgp 30+ main Non-Baseline C~
#> 11 <NA> 0 0 0 alcgp 0-39g/~ main Baseline Categ~
#> 12 alcgp40-~ 1.12 0.665 1.60 alcgp 40-79 main Non-Baseline C~
#> 13 alcgp80-~ 1.45 0.939 1.97 alcgp 80-119 main Non-Baseline C~
#> 14 alcgp120+ 2.12 1.56 2.69 alcgp 120+ main Non-Baseline C~
library(ggforce)
ggplot(data = d0,
mapping = aes(x = level, y = estimate, colour = reference,
ymin = conf.low, ymax = conf.high)) +
facet_col(facets = vars(variable), scales = "free_y", space = "free") +
coord_flip() +
geom_hline(yintercept = 0, linetype = "dashed") +
geom_pointrange()
Note the switch of the x aesthetic to the level column rather than term.
Alternatively, horizontal plots can be obtained using ggforce::facet_row() and loosing coord_flip();
ggplot(data = d0,
mapping = aes(x = level, y = estimate,
ymin = conf.low, ymax = conf.high,
colour = reference)) +
facet_row(facets = vars(variable), scales = "free_x", space = "free") +
geom_hline(yintercept = 0, linetype = "dashed") +
geom_pointrange() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Interactions
Models with interactions can also be handled in tidy_categorical(). Using the mtcars data we can create three types of interactions (between two numeric variables, between a numeric variable and categorical variable and between two categorical variables)
m1 <- mtcars %>%
mutate(engine = recode_factor(vs, `0` = "straight", `1` = "V-shaped"),
transmission = recode_factor(am, `0` = "automatic", `1` = "manual")) %>%
lm(mpg ~ as.factor(cyl) + wt * hp + wt * transmission + engine * transmission , data = .)
tidy(m1)
#> # A tibble: 10 x 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 35.5 12.3 2.89 0.00843
#> 2 as.factor(cyl)6 -1.03 1.76 -0.585 0.565
#> 3 as.factor(cyl)8 2.01 4.09 0.492 0.628
#> 4 wt -4.65 3.55 -1.31 0.203
#> 5 hp -0.0731 0.0577 -1.27 0.218
#> 6 transmissionmanual 10.7 10.0 1.07 0.296
#> 7 engineV-shaped 3.74 3.21 1.16 0.257
#> 8 wt:hp 0.0134 0.0162 0.828 0.416
#> 9 wt:transmissionmanual -2.63 2.83 -0.930 0.362
#> 10 transmissionmanual:engineV-shaped -3.16 3.76 -0.842 0.409Setting n_level = TRUE creates an additional column to monitor the number of observations in each of level of the categorical variables, including interaction terms in the model:
d1 <- m1 %>%
tidy(conf.int = TRUE) %>%
tidy_categorical(m = m1, n_level = TRUE) %>%
slice(-1)
d1 %>%
select(-(2:7))
#> # A tibble: 16 x 6
#> term variable level effect reference n_level
#> <chr> <chr> <fct> <chr> <chr> <dbl>
#> 1 <NA> as.factor(cyl) 4 main Baseline Cate~ 11
#> 2 as.factor(cyl)6 as.factor(cyl) 6 main Non-Baseline ~ 7
#> 3 as.factor(cyl)8 as.factor(cyl) 8 main Non-Baseline ~ 14
#> 4 wt wt wt main Non-Baseline ~ NA
#> 5 hp hp hp main Non-Baseline ~ NA
#> 6 <NA> transmission automatic main Baseline Cate~ 19
#> 7 transmissionmanual transmission manual main Non-Baseline ~ 13
#> 8 <NA> engine straight main Baseline Cate~ 18
#> 9 engineV-shaped engine V-shaped main Non-Baseline ~ 14
#> 10 wt:hp wt:hp wt:hp interac~ Non-Baseline ~ NA
#> 11 <NA> wt:transmissi~ automatic interac~ Baseline Cate~ 19
#> 12 wt:transmissionma~ wt:transmissi~ manual interac~ Non-Baseline ~ 13
#> 13 <NA> transmission:~ automatic:~ interac~ Baseline Cate~ 25
#> 14 <NA> transmission:~ manual:str~ interac~ Non-Baseline ~ 0
#> 15 <NA> transmission:~ automatic:~ interac~ Non-Baseline ~ 0
#> 16 transmissionmanua~ transmission:~ manual:V-s~ interac~ Non-Baseline ~ 7We can use similar plotting code as above to plot the interactions:
ggplot(data = d1,
mapping = aes(x = level, y = estimate, colour = reference,
ymin = conf.low, ymax = conf.high)) +
facet_col(facets = "variable", scales = "free_y", space = "free") +
coord_flip() +
geom_hline(yintercept = 0, linetype = "dashed") +
geom_pointrange()