model_identify_variables {broom.helpers}

R Documentation

Identify for each coefficient of a model the corresponding variable

Description

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

Usage

model_identify_variables(model)

## Default S3 method:
model_identify_variables(model)

## S3 method for class 'lavaan'
model_identify_variables(model)

## S3 method for class 'aov'
model_identify_variables(model)

## S3 method for class 'clm'
model_identify_variables(model)

## S3 method for class 'clmm'
model_identify_variables(model)

## S3 method for class 'gam'
model_identify_variables(model)

## S3 method for class 'model_fit'
model_identify_variables(model)

## S3 method for class '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_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_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()

iris %>%
  lm(
    Sepal.Length ~ poly(Sepal.Width, 2) + Species,
    data = .,
    contrasts = list(Species = contr.sum)
  ) %>%
  model_identify_variables()

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