Additional columns on broom::tidy() 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-13
Note: 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 tidy output to help manage categorical variables
library(tidycat)
m0 %>%
tidy() %>%
tidy_categorical(m = m0)
#> # A tibble: 12 x 8
#> term estimate std.error statistic p.value variable level effect
#> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <fct> <chr>
#> 1 (Intercept) -5.91 1.03 -5.74 9.61e- 9 (Intercept) "" 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 alcgp80-119 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+" main
Include additional rows for reference category terms and column to indicate their location by setting exclude_reference = FALSE and reference_label to a suitable string. 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,
exclude_reference = FALSE, 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) "" 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-Baseline
Standard 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
Using the additional columns from tidy_categroical() the coefficient plot can be enhanced to include the reference category, 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, exclude_reference = FALSE, reference_label = "Baseline") %>%
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
#> 2 agegp35-44 1.61 -0.100 4.54 agegp 35-44 main Non-Baseli~
#> 3 agegp45-54 2.98 1.41 5.86 agegp 45-54 main Non-Baseli~
#> 4 agegp55-64 3.36 1.81 6.24 agegp 55-64 main Non-Baseli~
#> 5 agegp65-74 3.73 2.16 6.62 agegp 65-74 main Non-Baseli~
#> 6 agegp75+ 3.68 1.99 6.61 agegp 75+ main Non-Baseli~
#> 7 <NA> 0 0 0 tobgp 0-9g/day main Baseline
#> 8 tobgp10-19 0.341 -0.0644 0.742 tobgp 10-19 main Non-Baseli~
#> 9 tobgp20-29 0.396 -0.0935 0.872 tobgp 20-29 main Non-Baseli~
#> 10 tobgp30+ 0.868 0.319 1.41 tobgp 30+ main Non-Baseli~
#> 11 <NA> 0 0 0 alcgp 0-39g/day main Baseline
#> 12 alcgp40-79 1.12 0.665 1.60 alcgp 40-79 main Non-Baseli~
#> 13 alcgp80-119 1.45 0.939 1.97 alcgp 80-119 main Non-Baseli~
#> 14 alcgp120+ 2.12 1.56 2.69 alcgp 120+ main Non-Baseli~
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))
