Identify the variable corresponding to each model coefficient

tidy_identify_variables() will add to the tidy tibble three additional columns: variable, var_class, var_type and var_nlevels.

Usage

tidy_identify_variables(x, model = tidy_get_model(x), quiet = FALSE)

Arguments

x

(data.frame)
A tidy tibble as produced by tidy_*() functions.

model

(a model object, e.g. glm)
The corresponding model, if not attached to x.

quiet

(logical)
Whether broom.helpers should not return a message when requested output cannot be generated. Default is FALSE.

Details

It will also identify interaction terms and intercept(s).

var_type could be:

• "continuous",

• "dichotomous" (categorical variable with 2 levels),

• "categorical" (categorical variable with 3 levels or more),

• "intercept"

• "interaction"

• "ran_pars (random-effect parameters for mixed models)

• "ran_vals" (random-effect values for mixed models)

• "unknown" in the rare cases where tidy_identify_variables() will fail to identify the list of variables

For dichotomous and categorical variables, var_nlevels corresponds to the number of original levels in the corresponding variables.

For fixest models, a new column instrumental is added to indicate instrumental variables.

See also

model_identify_variables()

Other tidy_helpers: tidy_add_coefficients_type(), tidy_add_contrasts(), tidy_add_estimate_to_reference_rows(), tidy_add_header_rows(), tidy_add_n(), tidy_add_pairwise_contrasts(), tidy_add_reference_rows(), tidy_add_term_labels(), tidy_add_variable_labels(), tidy_attach_model(), tidy_disambiguate_terms(), tidy_group_by(), tidy_plus_plus(), tidy_remove_intercept(), tidy_select_variables()

Examples

df <- Titanic |>
  dplyr::as_tibble() |>
  dplyr::mutate(Survived = factor(Survived, c("No", "Yes")))
glm(
  Survived ~ Class + Age * Sex,
  data = df,
  weights = df$n,
  family = binomial
) |>
  tidy_and_attach() |>
  tidy_identify_variables()
#> # A tibble: 7 × 11
#>   term      variable var_class var_type var_nlevels estimate std.error statistic
#>   <chr>     <chr>    <chr>     <chr>          <int>    <dbl>     <dbl>     <dbl>
#> 1 (Interce… (Interc… NA        interce…          NA    2.18      0.176    12.4  
#> 2 Class2nd  Class    character categor…           4   -1.03      0.200    -5.17 
#> 3 Class3rd  Class    character categor…           4   -1.81      0.176   -10.3  
#> 4 ClassCrew Class    character categor…           4   -0.803     0.160    -5.03 
#> 5 AgeChild  Age      character dichoto…           2   -0.110     0.335    -0.328
#> 6 SexMale   Sex      character dichoto…           2   -2.62      0.151   -17.3  
#> 7 AgeChild… Age:Sex  NA        interac…          NA    1.90      0.433     4.39 
#> # ℹ 3 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>

lm(
  Sepal.Length ~ poly(Sepal.Width, 2) + Species,
  data = iris,
  contrasts = list(Species = contr.sum)
) |>
  tidy_and_attach(conf.int = TRUE) |>
  tidy_identify_variables()
#> # A tibble: 5 × 11
#>   term      variable var_class var_type var_nlevels estimate std.error statistic
#>   <chr>     <chr>    <chr>     <chr>          <int>    <dbl>     <dbl>     <dbl>
#> 1 (Interce… (Interc… NA        interce…          NA   5.84      0.0359   163.   
#> 2 poly(Sep… Sepal.W… nmatrix.2 continu…          NA   4.27      0.568      7.52 
#> 3 poly(Sep… Sepal.W… nmatrix.2 continu…          NA  -0.0720    0.447     -0.161
#> 4 Species1  Species  factor    categor…           3  -1.13      0.0647   -17.5  
#> 5 Species2  Species  factor    categor…           3   0.324     0.0593     5.46 
#> # ℹ 3 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>