Only for categorical variables with treatment, SAS or sum contrasts, and categorical variables with no contrast.
model_list_terms_levels(
model,
label_pattern = "{level}",
variable_labels = NULL
)
# S3 method for default
model_list_terms_levels(
model,
label_pattern = "{level}",
variable_labels = NULL
)
a model object
a glue pattern for term labels (see examples)
an optional named list or named vector of
custom variable labels passed to model_list_variables()
A tibble with ten columns:
variable
: variable
contrasts_type
: type of contrasts ("sum" or "treatment")
term
: term name
level
: term level
level_rank
: rank of the level
reference
: logical indicating which term is the reference level
reference_level
: level of the reference term
var_label
: variable label obtained with model_list_variables()
var_nlevels
: number of levels in this variable
dichotomous
: logical indicating if the variable is dichotomous
label
: term label (by default equal to term level)
The first nine columns can be used in label_pattern
.
Other model_helpers:
model_compute_terms_contributions()
,
model_get_assign()
,
model_get_coefficients_type()
,
model_get_contrasts()
,
model_get_model_frame()
,
model_get_model_matrix()
,
model_get_model()
,
model_get_nlevels()
,
model_get_n()
,
model_get_offset()
,
model_get_pairwise_contrasts()
,
model_get_response_variable()
,
model_get_response()
,
model_get_terms()
,
model_get_weights()
,
model_get_xlevels()
,
model_identify_variables()
,
model_list_contrasts()
,
model_list_higher_order_variables()
,
model_list_variables()
glm(
am ~ mpg + factor(cyl),
data = mtcars,
family = binomial,
contrasts = list(`factor(cyl)` = contr.sum)
) %>%
model_list_terms_levels()
#> # A tibble: 3 × 11
#> variable contrasts_type term level level_rank reference reference_level
#> <chr> <chr> <chr> <chr> <int> <lgl> <chr>
#> 1 factor(cyl) sum factor(… 4 1 FALSE 8
#> 2 factor(cyl) sum factor(… 6 2 FALSE 8
#> 3 factor(cyl) sum factor(… 8 3 TRUE 8
#> # ℹ 4 more variables: var_label <chr>, var_nlevels <int>, dichotomous <lgl>,
#> # label <glue>
df <- Titanic %>%
dplyr::as_tibble() %>%
dplyr::mutate(Survived = factor(Survived, c("No", "Yes")))
mod <- df %>%
glm(
Survived ~ Class + Age + Sex,
data = ., weights = .$n, family = binomial,
contrasts = list(Age = contr.sum, Class = "contr.helmert")
)
mod %>% model_list_terms_levels()
#> # A tibble: 4 × 11
#> variable contrasts_type term level level_rank reference reference_level
#> <chr> <chr> <chr> <chr> <int> <lgl> <chr>
#> 1 Age sum Age1 Adult 1 FALSE Child
#> 2 Age sum Age2 Child 2 TRUE Child
#> 3 Sex treatment SexFemale Female 1 TRUE Female
#> 4 Sex treatment SexMale Male 2 FALSE Female
#> # ℹ 4 more variables: var_label <chr>, var_nlevels <int>, dichotomous <lgl>,
#> # label <glue>
mod %>% model_list_terms_levels("{level} vs {reference_level}")
#> # A tibble: 4 × 11
#> variable contrasts_type term level level_rank reference reference_level
#> <chr> <chr> <chr> <chr> <int> <lgl> <chr>
#> 1 Age sum Age1 Adult 1 FALSE Child
#> 2 Age sum Age2 Child 2 TRUE Child
#> 3 Sex treatment SexFemale Female 1 TRUE Female
#> 4 Sex treatment SexMale Male 2 FALSE Female
#> # ℹ 4 more variables: var_label <chr>, var_nlevels <int>, dichotomous <lgl>,
#> # label <glue>
mod %>% model_list_terms_levels("{variable} [{level} - {reference_level}]")
#> # A tibble: 4 × 11
#> variable contrasts_type term level level_rank reference reference_level
#> <chr> <chr> <chr> <chr> <int> <lgl> <chr>
#> 1 Age sum Age1 Adult 1 FALSE Child
#> 2 Age sum Age2 Child 2 TRUE Child
#> 3 Sex treatment SexFemale Female 1 TRUE Female
#> 4 Sex treatment SexMale Male 2 FALSE Female
#> # ℹ 4 more variables: var_label <chr>, var_nlevels <int>, dichotomous <lgl>,
#> # label <glue>
mod %>% model_list_terms_levels(
"{ifelse(reference, level, paste(level, '-', reference_level))}"
)
#> # A tibble: 4 × 11
#> variable contrasts_type term level level_rank reference reference_level
#> <chr> <chr> <chr> <chr> <int> <lgl> <chr>
#> 1 Age sum Age1 Adult 1 FALSE Child
#> 2 Age sum Age2 Child 2 TRUE Child
#> 3 Sex treatment SexFemale Female 1 TRUE Female
#> 4 Sex treatment SexMale Male 2 FALSE Female
#> # ℹ 4 more variables: var_label <chr>, var_nlevels <int>, dichotomous <lgl>,
#> # label <glue>