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Attach a full model to the tibble of model terms

Source: R/tidy_and_attach.R
tidy_attach_model.Rd

To facilitate the use of broom helpers with pipe, it is recommended to attach the original model as an attribute to the tibble of model terms generated by broom::tidy().

Usage

tidy_attach_model(x, model, .attributes = NULL)

tidy_and_attach(
  model,
  tidy_fun = tidy_with_broom_or_parameters,
  conf.int = TRUE,
  conf.level = 0.95,
  exponentiate = FALSE,
  model_matrix_attr = TRUE,
  ...
)

tidy_get_model(x)

tidy_detach_model(x)

Arguments

x

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

model

(a model object, e.g. glm)
A model to be attached/tidied.

.attributes

(list)
Named list of additional attributes to be attached to x.

tidy_fun

(function)
Option to specify a custom tidier function.

conf.int

(logical)
Should confidence intervals be computed? (see broom::tidy())

conf.level

(numeric)
Level of confidence for confidence intervals (default: 95%).

exponentiate

(logical)
Whether or not to exponentiate the coefficient estimates. This is typical for logistic, Poisson and Cox models, but a bad idea if there is no log or logit link; defaults to FALSE.

model_matrix_attr

(logical)
Whether model frame and model matrix should be added as attributes of model (respectively named "model_frame" and "model_matrix") and passed through

...

Other arguments passed to tidy_fun().

Details

tidy_attach_model() attach the model to a tibble already generated while tidy_and_attach() will apply broom::tidy() and attach the model.

Use tidy_get_model() to get the model attached to the tibble and tidy_detach_model() to remove the attribute containing the model.

See also

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_disambiguate_terms(), tidy_group_by(), tidy_identify_variables(), tidy_plus_plus(), tidy_remove_intercept(), tidy_select_variables()

Examples

mod <- lm(Sepal.Length ~ Sepal.Width + Species, data = iris)
tt <- mod |>
  tidy_and_attach(conf.int = TRUE)
tt
#> # A tibble: 4 × 7
#>   term              estimate std.error statistic  p.value conf.low conf.high
#>   <chr>                <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
#> 1 (Intercept)          2.25      0.370      6.09 9.57e- 9    1.52       2.98
#> 2 Sepal.Width          0.804     0.106      7.56 4.19e-12    0.593      1.01
#> 3 Speciesversicolor    1.46      0.112     13.0  3.48e-26    1.24       1.68
#> 4 Speciesvirginica     1.95      0.100     19.5  2.09e-42    1.75       2.14
tidy_get_model(tt)
#> 
#> Call:
#> lm(formula = Sepal.Length ~ Sepal.Width + Species, data = iris)
#> 
#> Coefficients:
#>       (Intercept)        Sepal.Width  Speciesversicolor   Speciesvirginica  
#>            2.2514             0.8036             1.4587             1.9468  
#>