Identify for each coefficient of a model the corresponding variable

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

model_identify_variables(model)

# S3 method for default
model_identify_variables(model)

# S3 method for lavaan
model_identify_variables(model)

# S3 method for aov
model_identify_variables(model)

# S3 method for clm
model_identify_variables(model)

# S3 method for clmm
model_identify_variables(model)

# S3 method for gam
model_identify_variables(model)

# S3 method for model_fit
model_identify_variables(model)

# S3 method for logitr
model_identify_variables(model)

Arguments

model

a model object

Value

A tibble with four columns:

• term: coefficients of the model

• variable: the corresponding variable

• var_class: class of the variable (cf. stats::.MFclass())

• var_type: "continuous", "dichotomous" (categorical variable with 2 levels), "categorical" (categorical variable with 3 or more levels), "intercept" or "interaction"

• var_nlevels: number of original levels for categorical variables

See also

tidy_identify_variables()

Other model_helpers: model_compute_terms_contributions(), model_get_assign(), model_get_coefficients_type(), model_get_contrasts(), model_get_model(), model_get_model_frame(), model_get_model_matrix(), model_get_n(), model_get_nlevels(), model_get_offset(), model_get_pairwise_contrasts(), model_get_response(), model_get_response_variable(), model_get_terms(), model_get_weights(), model_get_xlevels(), model_list_contrasts(), model_list_higher_order_variables(), model_list_terms_levels(), model_list_variables()

Examples

Titanic %>%
  dplyr::as_tibble() %>%
  dplyr::mutate(Survived = factor(Survived, c("No", "Yes"))) %>%
  glm(
    Survived ~ Class + Age * Sex,
    data = ., weights = .$n,
    family = binomial
  ) %>%
  model_identify_variables()
#> # A tibble: 7 × 5
#>   term             variable var_class var_nlevels var_type   
#>   <chr>            <chr>    <chr>           <int> <chr>      
#> 1 (Intercept)      NA       NA                 NA intercept  
#> 2 Class2nd         Class    character           4 categorical
#> 3 Class3rd         Class    character           4 categorical
#> 4 ClassCrew        Class    character           4 categorical
#> 5 AgeChild         Age      character           2 dichotomous
#> 6 SexMale          Sex      character           2 dichotomous
#> 7 AgeChild:SexMale Age:Sex  NA                 NA interaction

iris %>%
  lm(
    Sepal.Length ~ poly(Sepal.Width, 2) + Species,
    data = .,
    contrasts = list(Species = contr.sum)
  ) %>%
  model_identify_variables()
#> # A tibble: 5 × 5
#>   term                  variable    var_class var_nlevels var_type   
#>   <chr>                 <chr>       <chr>           <int> <chr>      
#> 1 (Intercept)           NA          NA                 NA intercept  
#> 2 poly(Sepal.Width, 2)1 Sepal.Width nmatrix.2          NA continuous 
#> 3 poly(Sepal.Width, 2)2 Sepal.Width nmatrix.2          NA continuous 
#> 4 Species1              Species     factor              3 categorical
#> 5 Species2              Species     factor              3 categorical