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README.md
In broom.helpers: Helpers for Model Coefficients Tibbles
broom.helpers 
The broom.helpers package provides suite of functions to work with
regression model broom::tidy() tibbles.
The suite includes functions to group regression model terms by
variable, insert reference and header rows for categorical variables,
add variable labels, and more.
broom.helpers is used, in particular, by gtsummary::tbl_regression()
for producing nice formatted tables of model
coefficients
and by ggstats::ggcoef_model() for plotting model
coefficients.
Installation & Documentation
To install stable version:
install.packages("broom.helpers")
Documentation of stable version:
https://larmarange.github.io/broom.helpers/
To install development version:
remotes::install_github("larmarange/broom.helpers")
Documentation of development version:
https://larmarange.github.io/broom.helpers/dev/
Examples
all-in-one wrapper
mod1 <- lm(Sepal.Length ~ Sepal.Width + Species, data = iris)
library(broom.helpers)
ex1 <- mod1 %>% tidy_plus_plus()
ex1
#> # A tibble: 4 × 17
#> term varia…¹ var_l…² var_c…³ var_t…⁴ var_n…⁵ contr…⁶ contr…⁷ refer…⁸ label
#> <chr> <chr> <chr> <chr> <chr> <int> <chr> <chr> <lgl> <chr>
#> 1 Sepal.W… Sepal.… Sepal.… numeric contin… NA <NA> <NA> NA Sepa…
#> 2 Species… Species Species factor catego… 3 contr.… treatm… TRUE seto…
#> 3 Species… Species Species factor catego… 3 contr.… treatm… FALSE vers…
#> 4 Species… Species Species factor catego… 3 contr.… treatm… FALSE virg…
#> # … with 7 more variables: n_obs <dbl>, estimate <dbl>, std.error <dbl>,
#> # statistic <dbl>, p.value <dbl>, conf.low <dbl>, conf.high <dbl>, and
#> # abbreviated variable names ¹variable, ²var_label, ³var_class, ⁴var_type,
#> # ⁵var_nlevels, ⁶contrasts, ⁷contrasts_type, ⁸reference_row
dplyr::glimpse(ex1)
#> Rows: 4
#> Columns: 17
#> $ term <chr> "Sepal.Width", "Speciessetosa", "Speciesversicolor", "S…
#> $ variable <chr> "Sepal.Width", "Species", "Species", "Species"
#> $ var_label <chr> "Sepal.Width", "Species", "Species", "Species"
#> $ var_class <chr> "numeric", "factor", "factor", "factor"
#> $ var_type <chr> "continuous", "categorical", "categorical", "categorica…
#> $ var_nlevels <int> NA, 3, 3, 3
#> $ contrasts <chr> NA, "contr.treatment", "contr.treatment", "contr.treatm…
#> $ contrasts_type <chr> NA, "treatment", "treatment", "treatment"
#> $ reference_row <lgl> NA, TRUE, FALSE, FALSE
#> $ label <chr> "Sepal.Width", "setosa", "versicolor", "virginica"
#> $ n_obs <dbl> 150, 50, 50, 50
#> $ estimate <dbl> 0.8035609, 0.0000000, 1.4587431, 1.9468166
#> $ std.error <dbl> 0.1063390, NA, 0.1121079, 0.1000150
#> $ statistic <dbl> 7.556598, NA, 13.011954, 19.465255
#> $ p.value <dbl> 4.187340e-12, NA, 3.478232e-26, 2.094475e-42
#> $ conf.low <dbl> 0.5933983, NA, 1.2371791, 1.7491525
#> $ conf.high <dbl> 1.013723, NA, 1.680307, 2.144481
mod2 <- glm(
response ~ poly(age, 3) + stage + grade * trt,
na.omit(gtsummary::trial),
family = binomial,
contrasts = list(
stage = contr.treatment(4, base = 3),
grade = contr.sum
)
)
ex2 <- mod2 %>%
tidy_plus_plus(
exponentiate = TRUE,
variable_labels = c(age = "Age (in years)"),
add_header_rows = TRUE,
show_single_row = "trt"
)
ex2
#> # A tibble: 17 × 19
#> term varia…¹ var_l…² var_c…³ var_t…⁴ var_n…⁵ heade…⁶ contr…⁷ contr…⁸ refer…⁹
#> <chr> <chr> <chr> <chr> <chr> <int> <lgl> <chr> <chr> <lgl>
#> 1 <NA> age Age (i… nmatri… contin… NA TRUE <NA> <NA> NA
#> 2 poly… age Age (i… nmatri… contin… NA FALSE <NA> <NA> NA
#> 3 poly… age Age (i… nmatri… contin… NA FALSE <NA> <NA> NA
#> 4 poly… age Age (i… nmatri… contin… NA FALSE <NA> <NA> NA
#> 5 <NA> stage T Stage factor catego… 4 TRUE contr.… treatm… NA
#> 6 stag… stage T Stage factor catego… 4 FALSE contr.… treatm… FALSE
#> 7 stag… stage T Stage factor catego… 4 FALSE contr.… treatm… FALSE
#> 8 stag… stage T Stage factor catego… 4 FALSE contr.… treatm… TRUE
#> 9 stag… stage T Stage factor catego… 4 FALSE contr.… treatm… FALSE
#> 10 <NA> grade Grade factor catego… 3 TRUE contr.… sum NA
#> 11 grad… grade Grade factor catego… 3 FALSE contr.… sum FALSE
#> 12 grad… grade Grade factor catego… 3 FALSE contr.… sum FALSE
#> 13 grad… grade Grade factor catego… 3 FALSE contr.… sum TRUE
#> 14 trtD… trt Chemot… charac… dichot… 2 NA contr.… treatm… FALSE
#> 15 <NA> grade:… Grade … <NA> intera… NA TRUE <NA> <NA> NA
#> 16 grad… grade:… Grade … <NA> intera… NA FALSE <NA> <NA> NA
#> 17 grad… grade:… Grade … <NA> intera… NA FALSE <NA> <NA> NA
#> # … with 9 more variables: label <chr>, n_obs <dbl>, n_event <dbl>,
#> # estimate <dbl>, std.error <dbl>, statistic <dbl>, p.value <dbl>,
#> # conf.low <dbl>, conf.high <dbl>, and abbreviated variable names ¹variable,
#> # ²var_label, ³var_class, ⁴var_type, ⁵var_nlevels, ⁶header_row, ⁷contrasts,
#> # ⁸contrasts_type, ⁹reference_row
dplyr::glimpse(ex2)
#> Rows: 17
#> Columns: 19
#> $ term <chr> NA, "poly(age, 3)1", "poly(age, 3)2", "poly(age, 3)3", …
#> $ variable <chr> "age", "age", "age", "age", "stage", "stage", "stage", …
#> $ var_label <chr> "Age (in years)", "Age (in years)", "Age (in years)", "…
#> $ var_class <chr> "nmatrix.3", "nmatrix.3", "nmatrix.3", "nmatrix.3", "fa…
#> $ var_type <chr> "continuous", "continuous", "continuous", "continuous",…
#> $ var_nlevels <int> NA, NA, NA, NA, 4, 4, 4, 4, 4, 3, 3, 3, 3, 2, NA, NA, NA
#> $ header_row <lgl> TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, F…
#> $ contrasts <chr> NA, NA, NA, NA, "contr.treatment(base=3)", "contr.treat…
#> $ contrasts_type <chr> NA, NA, NA, NA, "treatment", "treatment", "treatment", …
#> $ reference_row <lgl> NA, NA, NA, NA, NA, FALSE, FALSE, TRUE, FALSE, NA, FALS…
#> $ label <chr> "Age (in years)", "Age (in years)", "Age (in years)²", …
#> $ n_obs <dbl> NA, 92, 56, 80, NA, 46, 50, 35, 42, NA, 63, 53, 57, 90,…
#> $ n_event <dbl> NA, 31, 17, 22, NA, 17, 12, 13, 12, NA, 20, 16, 18, 30,…
#> $ estimate <dbl> NA, 20.2416394, 1.2337899, 0.4931553, NA, 1.0047885, 0.…
#> $ std.error <dbl> NA, 2.3254455, 2.3512842, 2.3936657, NA, 0.4959893, 0.5…
#> $ statistic <dbl> NA, 1.29340459, 0.08935144, -0.29533409, NA, 0.00963137…
#> $ p.value <dbl> NA, 0.1958712, 0.9288026, 0.7677387, NA, 0.9923154, 0.1…
#> $ conf.low <dbl> NA, 0.225454425, 0.007493208, 0.004745694, NA, 0.379776…
#> $ conf.high <dbl> NA, 2315.587655, 100.318341, 74.226179, NA, 2.683385, 1…
fine control
ex3 <- mod1 %>%
# perform initial tidying of model
tidy_and_attach() %>%
# add reference row
tidy_add_reference_rows() %>%
# add term labels
tidy_add_term_labels() %>%
# remove intercept
tidy_remove_intercept
ex3
#> # A tibble: 4 × 16
#> term varia…¹ var_l…² var_c…³ var_t…⁴ var_n…⁵ contr…⁶ contr…⁷ refer…⁸ label
#> <chr> <chr> <chr> <chr> <chr> <int> <chr> <chr> <lgl> <chr>
#> 1 Sepal.W… Sepal.… Sepal.… numeric contin… NA <NA> <NA> NA Sepa…
#> 2 Species… Species Species factor catego… 3 contr.… treatm… TRUE seto…
#> 3 Species… Species Species factor catego… 3 contr.… treatm… FALSE vers…
#> 4 Species… Species Species factor catego… 3 contr.… treatm… FALSE virg…
#> # … with 6 more variables: estimate <dbl>, std.error <dbl>, statistic <dbl>,
#> # p.value <dbl>, conf.low <dbl>, conf.high <dbl>, and abbreviated variable
#> # names ¹variable, ²var_label, ³var_class, ⁴var_type, ⁵var_nlevels,
#> # ⁶contrasts, ⁷contrasts_type, ⁸reference_row
dplyr::glimpse(ex3)
#> Rows: 4
#> Columns: 16
#> $ term <chr> "Sepal.Width", "Speciessetosa", "Speciesversicolor", "S…
#> $ variable <chr> "Sepal.Width", "Species", "Species", "Species"
#> $ var_label <chr> "Sepal.Width", "Species", "Species", "Species"
#> $ var_class <chr> "numeric", "factor", "factor", "factor"
#> $ var_type <chr> "continuous", "categorical", "categorical", "categorica…
#> $ var_nlevels <int> NA, 3, 3, 3
#> $ contrasts <chr> NA, "contr.treatment", "contr.treatment", "contr.treatm…
#> $ contrasts_type <chr> NA, "treatment", "treatment", "treatment"
#> $ reference_row <lgl> NA, TRUE, FALSE, FALSE
#> $ label <chr> "Sepal.Width", "setosa", "versicolor", "virginica"
#> $ estimate <dbl> 0.8035609, NA, 1.4587431, 1.9468166
#> $ std.error <dbl> 0.1063390, NA, 0.1121079, 0.1000150
#> $ statistic <dbl> 7.556598, NA, 13.011954, 19.465255
#> $ p.value <dbl> 4.187340e-12, NA, 3.478232e-26, 2.094475e-42
#> $ conf.low <dbl> 0.5933983, NA, 1.2371791, 1.7491525
#> $ conf.high <dbl> 1.013723, NA, 1.680307, 2.144481
ex4 <- mod2 %>%
# perform initial tidying of model
tidy_and_attach(exponentiate = TRUE) %>%
# add variable labels, including a custom value for age
tidy_add_variable_labels(labels = c(age = "Age in years")) %>%
# add reference rows for categorical variables
tidy_add_reference_rows() %>%
# add a, estimate value of reference terms
tidy_add_estimate_to_reference_rows(exponentiate = TRUE) %>%
# add header rows for categorical variables
tidy_add_header_rows()
ex4
#> # A tibble: 20 × 17
#> term varia…¹ var_l…² var_c…³ var_t…⁴ var_n…⁵ heade…⁶ contr…⁷ contr…⁸ refer…⁹
#> <chr> <chr> <chr> <chr> <chr> <int> <lgl> <chr> <chr> <lgl>
#> 1 (Int… (Inter… (Inter… <NA> interc… NA NA <NA> <NA> NA
#> 2 <NA> age Age in… nmatri… contin… NA TRUE <NA> <NA> NA
#> 3 poly… age Age in… nmatri… contin… NA FALSE <NA> <NA> NA
#> 4 poly… age Age in… nmatri… contin… NA FALSE <NA> <NA> NA
#> 5 poly… age Age in… nmatri… contin… NA FALSE <NA> <NA> NA
#> 6 <NA> stage T Stage factor catego… 4 TRUE contr.… treatm… NA
#> 7 stag… stage T Stage factor catego… 4 FALSE contr.… treatm… FALSE
#> 8 stag… stage T Stage factor catego… 4 FALSE contr.… treatm… FALSE
#> 9 stag… stage T Stage factor catego… 4 FALSE contr.… treatm… TRUE
#> 10 stag… stage T Stage factor catego… 4 FALSE contr.… treatm… FALSE
#> 11 <NA> grade Grade factor catego… 3 TRUE contr.… sum NA
#> 12 grad… grade Grade factor catego… 3 FALSE contr.… sum FALSE
#> 13 grad… grade Grade factor catego… 3 FALSE contr.… sum FALSE
#> 14 grad… grade Grade factor catego… 3 FALSE contr.… sum TRUE
#> 15 <NA> trt Chemot… charac… dichot… 2 TRUE contr.… treatm… NA
#> 16 trtD… trt Chemot… charac… dichot… 2 FALSE contr.… treatm… TRUE
#> 17 trtD… trt Chemot… charac… dichot… 2 FALSE contr.… treatm… FALSE
#> 18 <NA> grade:… Grade … <NA> intera… NA TRUE <NA> <NA> NA
#> 19 grad… grade:… Grade … <NA> intera… NA FALSE <NA> <NA> NA
#> 20 grad… grade:… Grade … <NA> intera… NA FALSE <NA> <NA> NA
#> # … with 7 more variables: label <chr>, estimate <dbl>, std.error <dbl>,
#> # statistic <dbl>, p.value <dbl>, conf.low <dbl>, conf.high <dbl>, and
#> # abbreviated variable names ¹variable, ²var_label, ³var_class, ⁴var_type,
#> # ⁵var_nlevels, ⁶header_row, ⁷contrasts, ⁸contrasts_type, ⁹reference_row
dplyr::glimpse(ex4)
#> Rows: 20
#> Columns: 17
#> $ term <chr> "(Intercept)", NA, "poly(age, 3)1", "poly(age, 3)2", "p…
#> $ variable <chr> "(Intercept)", "age", "age", "age", "age", "stage", "st…
#> $ var_label <chr> "(Intercept)", "Age in years", "Age in years", "Age in …
#> $ var_class <chr> NA, "nmatrix.3", "nmatrix.3", "nmatrix.3", "nmatrix.3",…
#> $ var_type <chr> "intercept", "continuous", "continuous", "continuous", …
#> $ var_nlevels <int> NA, NA, NA, NA, NA, 4, 4, 4, 4, 4, 3, 3, 3, 3, 2, 2, 2,…
#> $ header_row <lgl> NA, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALS…
#> $ contrasts <chr> NA, NA, NA, NA, NA, "contr.treatment(base=3)", "contr.t…
#> $ contrasts_type <chr> NA, NA, NA, NA, NA, "treatment", "treatment", "treatmen…
#> $ reference_row <lgl> NA, NA, NA, NA, NA, NA, FALSE, FALSE, TRUE, FALSE, NA, …
#> $ label <chr> "(Intercept)", "Age in years", "Age in years", "Age in …
#> $ estimate <dbl> 0.5266376, NA, 20.2416394, 1.2337899, 0.4931553, NA, 1.…
#> $ std.error <dbl> 0.4130930, NA, 2.3254455, 2.3512842, 2.3936657, NA, 0.4…
#> $ statistic <dbl> -1.55229592, NA, 1.29340459, 0.08935144, -0.29533409, N…
#> $ p.value <dbl> 0.1205914, NA, 0.1958712, 0.9288026, 0.7677387, NA, 0.9…
#> $ conf.low <dbl> 0.227717775, NA, 0.225454425, 0.007493208, 0.004745694,…
#> $ conf.high <dbl> 1.164600, NA, 2315.587655, 100.318341, 74.226179, NA, 2…
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broom.helpers
The broom.helpers package provides suite of functions to work with
regression model broom::tidy() tibbles.
The suite includes functions to group regression model terms by variable, insert reference and header rows for categorical variables, add variable labels, and more.
broom.helpers is used, in particular, by gtsummary::tbl_regression()
for producing nice formatted tables of model
coefficients
and by ggstats::ggcoef_model() for plotting model
coefficients.
Installation & Documentation
To install stable version:
install.packages("broom.helpers")
Documentation of stable version: https://larmarange.github.io/broom.helpers/
To install development version:
remotes::install_github("larmarange/broom.helpers")
Documentation of development version: https://larmarange.github.io/broom.helpers/dev/
Examples
all-in-one wrapper
mod1 <- lm(Sepal.Length ~ Sepal.Width + Species, data = iris)
library(broom.helpers)
ex1 <- mod1 %>% tidy_plus_plus()
ex1
#> # A tibble: 4 × 17
#> term varia…¹ var_l…² var_c…³ var_t…⁴ var_n…⁵ contr…⁶ contr…⁷ refer…⁸ label
#> <chr> <chr> <chr> <chr> <chr> <int> <chr> <chr> <lgl> <chr>
#> 1 Sepal.W… Sepal.… Sepal.… numeric contin… NA <NA> <NA> NA Sepa…
#> 2 Species… Species Species factor catego… 3 contr.… treatm… TRUE seto…
#> 3 Species… Species Species factor catego… 3 contr.… treatm… FALSE vers…
#> 4 Species… Species Species factor catego… 3 contr.… treatm… FALSE virg…
#> # … with 7 more variables: n_obs <dbl>, estimate <dbl>, std.error <dbl>,
#> # statistic <dbl>, p.value <dbl>, conf.low <dbl>, conf.high <dbl>, and
#> # abbreviated variable names ¹variable, ²var_label, ³var_class, ⁴var_type,
#> # ⁵var_nlevels, ⁶contrasts, ⁷contrasts_type, ⁸reference_row
dplyr::glimpse(ex1)
#> Rows: 4
#> Columns: 17
#> $ term <chr> "Sepal.Width", "Speciessetosa", "Speciesversicolor", "S…
#> $ variable <chr> "Sepal.Width", "Species", "Species", "Species"
#> $ var_label <chr> "Sepal.Width", "Species", "Species", "Species"
#> $ var_class <chr> "numeric", "factor", "factor", "factor"
#> $ var_type <chr> "continuous", "categorical", "categorical", "categorica…
#> $ var_nlevels <int> NA, 3, 3, 3
#> $ contrasts <chr> NA, "contr.treatment", "contr.treatment", "contr.treatm…
#> $ contrasts_type <chr> NA, "treatment", "treatment", "treatment"
#> $ reference_row <lgl> NA, TRUE, FALSE, FALSE
#> $ label <chr> "Sepal.Width", "setosa", "versicolor", "virginica"
#> $ n_obs <dbl> 150, 50, 50, 50
#> $ estimate <dbl> 0.8035609, 0.0000000, 1.4587431, 1.9468166
#> $ std.error <dbl> 0.1063390, NA, 0.1121079, 0.1000150
#> $ statistic <dbl> 7.556598, NA, 13.011954, 19.465255
#> $ p.value <dbl> 4.187340e-12, NA, 3.478232e-26, 2.094475e-42
#> $ conf.low <dbl> 0.5933983, NA, 1.2371791, 1.7491525
#> $ conf.high <dbl> 1.013723, NA, 1.680307, 2.144481
mod2 <- glm(
response ~ poly(age, 3) + stage + grade * trt,
na.omit(gtsummary::trial),
family = binomial,
contrasts = list(
stage = contr.treatment(4, base = 3),
grade = contr.sum
)
)
ex2 <- mod2 %>%
tidy_plus_plus(
exponentiate = TRUE,
variable_labels = c(age = "Age (in years)"),
add_header_rows = TRUE,
show_single_row = "trt"
)
ex2
#> # A tibble: 17 × 19
#> term varia…¹ var_l…² var_c…³ var_t…⁴ var_n…⁵ heade…⁶ contr…⁷ contr…⁸ refer…⁹
#> <chr> <chr> <chr> <chr> <chr> <int> <lgl> <chr> <chr> <lgl>
#> 1 <NA> age Age (i… nmatri… contin… NA TRUE <NA> <NA> NA
#> 2 poly… age Age (i… nmatri… contin… NA FALSE <NA> <NA> NA
#> 3 poly… age Age (i… nmatri… contin… NA FALSE <NA> <NA> NA
#> 4 poly… age Age (i… nmatri… contin… NA FALSE <NA> <NA> NA
#> 5 <NA> stage T Stage factor catego… 4 TRUE contr.… treatm… NA
#> 6 stag… stage T Stage factor catego… 4 FALSE contr.… treatm… FALSE
#> 7 stag… stage T Stage factor catego… 4 FALSE contr.… treatm… FALSE
#> 8 stag… stage T Stage factor catego… 4 FALSE contr.… treatm… TRUE
#> 9 stag… stage T Stage factor catego… 4 FALSE contr.… treatm… FALSE
#> 10 <NA> grade Grade factor catego… 3 TRUE contr.… sum NA
#> 11 grad… grade Grade factor catego… 3 FALSE contr.… sum FALSE
#> 12 grad… grade Grade factor catego… 3 FALSE contr.… sum FALSE
#> 13 grad… grade Grade factor catego… 3 FALSE contr.… sum TRUE
#> 14 trtD… trt Chemot… charac… dichot… 2 NA contr.… treatm… FALSE
#> 15 <NA> grade:… Grade … <NA> intera… NA TRUE <NA> <NA> NA
#> 16 grad… grade:… Grade … <NA> intera… NA FALSE <NA> <NA> NA
#> 17 grad… grade:… Grade … <NA> intera… NA FALSE <NA> <NA> NA
#> # … with 9 more variables: label <chr>, n_obs <dbl>, n_event <dbl>,
#> # estimate <dbl>, std.error <dbl>, statistic <dbl>, p.value <dbl>,
#> # conf.low <dbl>, conf.high <dbl>, and abbreviated variable names ¹variable,
#> # ²var_label, ³var_class, ⁴var_type, ⁵var_nlevels, ⁶header_row, ⁷contrasts,
#> # ⁸contrasts_type, ⁹reference_row
dplyr::glimpse(ex2)
#> Rows: 17
#> Columns: 19
#> $ term <chr> NA, "poly(age, 3)1", "poly(age, 3)2", "poly(age, 3)3", …
#> $ variable <chr> "age", "age", "age", "age", "stage", "stage", "stage", …
#> $ var_label <chr> "Age (in years)", "Age (in years)", "Age (in years)", "…
#> $ var_class <chr> "nmatrix.3", "nmatrix.3", "nmatrix.3", "nmatrix.3", "fa…
#> $ var_type <chr> "continuous", "continuous", "continuous", "continuous",…
#> $ var_nlevels <int> NA, NA, NA, NA, 4, 4, 4, 4, 4, 3, 3, 3, 3, 2, NA, NA, NA
#> $ header_row <lgl> TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, F…
#> $ contrasts <chr> NA, NA, NA, NA, "contr.treatment(base=3)", "contr.treat…
#> $ contrasts_type <chr> NA, NA, NA, NA, "treatment", "treatment", "treatment", …
#> $ reference_row <lgl> NA, NA, NA, NA, NA, FALSE, FALSE, TRUE, FALSE, NA, FALS…
#> $ label <chr> "Age (in years)", "Age (in years)", "Age (in years)²", …
#> $ n_obs <dbl> NA, 92, 56, 80, NA, 46, 50, 35, 42, NA, 63, 53, 57, 90,…
#> $ n_event <dbl> NA, 31, 17, 22, NA, 17, 12, 13, 12, NA, 20, 16, 18, 30,…
#> $ estimate <dbl> NA, 20.2416394, 1.2337899, 0.4931553, NA, 1.0047885, 0.…
#> $ std.error <dbl> NA, 2.3254455, 2.3512842, 2.3936657, NA, 0.4959893, 0.5…
#> $ statistic <dbl> NA, 1.29340459, 0.08935144, -0.29533409, NA, 0.00963137…
#> $ p.value <dbl> NA, 0.1958712, 0.9288026, 0.7677387, NA, 0.9923154, 0.1…
#> $ conf.low <dbl> NA, 0.225454425, 0.007493208, 0.004745694, NA, 0.379776…
#> $ conf.high <dbl> NA, 2315.587655, 100.318341, 74.226179, NA, 2.683385, 1…
fine control
ex3 <- mod1 %>%
# perform initial tidying of model
tidy_and_attach() %>%
# add reference row
tidy_add_reference_rows() %>%
# add term labels
tidy_add_term_labels() %>%
# remove intercept
tidy_remove_intercept
ex3
#> # A tibble: 4 × 16
#> term varia…¹ var_l…² var_c…³ var_t…⁴ var_n…⁵ contr…⁶ contr…⁷ refer…⁸ label
#> <chr> <chr> <chr> <chr> <chr> <int> <chr> <chr> <lgl> <chr>
#> 1 Sepal.W… Sepal.… Sepal.… numeric contin… NA <NA> <NA> NA Sepa…
#> 2 Species… Species Species factor catego… 3 contr.… treatm… TRUE seto…
#> 3 Species… Species Species factor catego… 3 contr.… treatm… FALSE vers…
#> 4 Species… Species Species factor catego… 3 contr.… treatm… FALSE virg…
#> # … with 6 more variables: estimate <dbl>, std.error <dbl>, statistic <dbl>,
#> # p.value <dbl>, conf.low <dbl>, conf.high <dbl>, and abbreviated variable
#> # names ¹variable, ²var_label, ³var_class, ⁴var_type, ⁵var_nlevels,
#> # ⁶contrasts, ⁷contrasts_type, ⁸reference_row
dplyr::glimpse(ex3)
#> Rows: 4
#> Columns: 16
#> $ term <chr> "Sepal.Width", "Speciessetosa", "Speciesversicolor", "S…
#> $ variable <chr> "Sepal.Width", "Species", "Species", "Species"
#> $ var_label <chr> "Sepal.Width", "Species", "Species", "Species"
#> $ var_class <chr> "numeric", "factor", "factor", "factor"
#> $ var_type <chr> "continuous", "categorical", "categorical", "categorica…
#> $ var_nlevels <int> NA, 3, 3, 3
#> $ contrasts <chr> NA, "contr.treatment", "contr.treatment", "contr.treatm…
#> $ contrasts_type <chr> NA, "treatment", "treatment", "treatment"
#> $ reference_row <lgl> NA, TRUE, FALSE, FALSE
#> $ label <chr> "Sepal.Width", "setosa", "versicolor", "virginica"
#> $ estimate <dbl> 0.8035609, NA, 1.4587431, 1.9468166
#> $ std.error <dbl> 0.1063390, NA, 0.1121079, 0.1000150
#> $ statistic <dbl> 7.556598, NA, 13.011954, 19.465255
#> $ p.value <dbl> 4.187340e-12, NA, 3.478232e-26, 2.094475e-42
#> $ conf.low <dbl> 0.5933983, NA, 1.2371791, 1.7491525
#> $ conf.high <dbl> 1.013723, NA, 1.680307, 2.144481
ex4 <- mod2 %>%
# perform initial tidying of model
tidy_and_attach(exponentiate = TRUE) %>%
# add variable labels, including a custom value for age
tidy_add_variable_labels(labels = c(age = "Age in years")) %>%
# add reference rows for categorical variables
tidy_add_reference_rows() %>%
# add a, estimate value of reference terms
tidy_add_estimate_to_reference_rows(exponentiate = TRUE) %>%
# add header rows for categorical variables
tidy_add_header_rows()
ex4
#> # A tibble: 20 × 17
#> term varia…¹ var_l…² var_c…³ var_t…⁴ var_n…⁵ heade…⁶ contr…⁷ contr…⁸ refer…⁹
#> <chr> <chr> <chr> <chr> <chr> <int> <lgl> <chr> <chr> <lgl>
#> 1 (Int… (Inter… (Inter… <NA> interc… NA NA <NA> <NA> NA
#> 2 <NA> age Age in… nmatri… contin… NA TRUE <NA> <NA> NA
#> 3 poly… age Age in… nmatri… contin… NA FALSE <NA> <NA> NA
#> 4 poly… age Age in… nmatri… contin… NA FALSE <NA> <NA> NA
#> 5 poly… age Age in… nmatri… contin… NA FALSE <NA> <NA> NA
#> 6 <NA> stage T Stage factor catego… 4 TRUE contr.… treatm… NA
#> 7 stag… stage T Stage factor catego… 4 FALSE contr.… treatm… FALSE
#> 8 stag… stage T Stage factor catego… 4 FALSE contr.… treatm… FALSE
#> 9 stag… stage T Stage factor catego… 4 FALSE contr.… treatm… TRUE
#> 10 stag… stage T Stage factor catego… 4 FALSE contr.… treatm… FALSE
#> 11 <NA> grade Grade factor catego… 3 TRUE contr.… sum NA
#> 12 grad… grade Grade factor catego… 3 FALSE contr.… sum FALSE
#> 13 grad… grade Grade factor catego… 3 FALSE contr.… sum FALSE
#> 14 grad… grade Grade factor catego… 3 FALSE contr.… sum TRUE
#> 15 <NA> trt Chemot… charac… dichot… 2 TRUE contr.… treatm… NA
#> 16 trtD… trt Chemot… charac… dichot… 2 FALSE contr.… treatm… TRUE
#> 17 trtD… trt Chemot… charac… dichot… 2 FALSE contr.… treatm… FALSE
#> 18 <NA> grade:… Grade … <NA> intera… NA TRUE <NA> <NA> NA
#> 19 grad… grade:… Grade … <NA> intera… NA FALSE <NA> <NA> NA
#> 20 grad… grade:… Grade … <NA> intera… NA FALSE <NA> <NA> NA
#> # … with 7 more variables: label <chr>, estimate <dbl>, std.error <dbl>,
#> # statistic <dbl>, p.value <dbl>, conf.low <dbl>, conf.high <dbl>, and
#> # abbreviated variable names ¹variable, ²var_label, ³var_class, ⁴var_type,
#> # ⁵var_nlevels, ⁶header_row, ⁷contrasts, ⁸contrasts_type, ⁹reference_row
dplyr::glimpse(ex4)
#> Rows: 20
#> Columns: 17
#> $ term <chr> "(Intercept)", NA, "poly(age, 3)1", "poly(age, 3)2", "p…
#> $ variable <chr> "(Intercept)", "age", "age", "age", "age", "stage", "st…
#> $ var_label <chr> "(Intercept)", "Age in years", "Age in years", "Age in …
#> $ var_class <chr> NA, "nmatrix.3", "nmatrix.3", "nmatrix.3", "nmatrix.3",…
#> $ var_type <chr> "intercept", "continuous", "continuous", "continuous", …
#> $ var_nlevels <int> NA, NA, NA, NA, NA, 4, 4, 4, 4, 4, 3, 3, 3, 3, 2, 2, 2,…
#> $ header_row <lgl> NA, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALS…
#> $ contrasts <chr> NA, NA, NA, NA, NA, "contr.treatment(base=3)", "contr.t…
#> $ contrasts_type <chr> NA, NA, NA, NA, NA, "treatment", "treatment", "treatmen…
#> $ reference_row <lgl> NA, NA, NA, NA, NA, NA, FALSE, FALSE, TRUE, FALSE, NA, …
#> $ label <chr> "(Intercept)", "Age in years", "Age in years", "Age in …
#> $ estimate <dbl> 0.5266376, NA, 20.2416394, 1.2337899, 0.4931553, NA, 1.…
#> $ std.error <dbl> 0.4130930, NA, 2.3254455, 2.3512842, 2.3936657, NA, 0.4…
#> $ statistic <dbl> -1.55229592, NA, 1.29340459, 0.08935144, -0.29533409, N…
#> $ p.value <dbl> 0.1205914, NA, 0.1958712, 0.9288026, 0.7677387, NA, 0.9…
#> $ conf.low <dbl> 0.227717775, NA, 0.225454425, 0.007493208, 0.004745694,…
#> $ conf.high <dbl> 1.164600, NA, 2315.587655, 100.318341, 74.226179, NA, 2…
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