Add an estimate value to references rows for categorical variables
For categorical variables with a treatment contrast
(stats::contr.treatment()) or a SAS contrast (stats::contr.SAS())
will add an estimate equal to 0 (or 1 if exponentiate = TRUE)
to the reference row.
tidy_add_estimate_to_reference_rows( x, exponentiate = attr(x, "exponentiate"), model = tidy_get_model(x), quiet = FALSE )
x |
a tidy tibble |
exponentiate |
logical indicating whether or not to exponentiate the
coefficient estimates. It should be consistent with the original call to
|
model |
the corresponding model, if not attached to |
quiet |
logical argument whether broom.helpers should not return a message when requested output cannot be generated. Default is FALSE |
For categorical variables with a sum contrast (stats::contr.sum()),
the estimate value of the reference row will be equal to the sum of
all other coefficients multiplied by -1 (eventually exponentiated if
exponentiate = TRUE), and obtained with emmeans::emmeans().
The emmeans package should therefore be installed.
For sum contrasts, the model coefficient corresponds
to the difference of each level with the grand mean.
For other variables, no change will be made.
If the reference_row column is not yet available in x,
tidy_add_reference_rows() will be automatically applied.
Other tidy_helpers:
tidy_add_coefficients_type(),
tidy_add_contrasts(),
tidy_add_header_rows(),
tidy_add_n(),
tidy_add_reference_rows(),
tidy_add_term_labels(),
tidy_add_variable_labels(),
tidy_attach_model(),
tidy_disambiguate_terms(),
tidy_identify_variables(),
tidy_plus_plus(),
tidy_remove_intercept(),
tidy_select_variables()
df <- Titanic %>%
dplyr::as_tibble() %>%
dplyr::mutate(dplyr::across(where(is.character), factor))
df %>%
glm(
Survived ~ Class + Age + Sex,
data = ., weights = .$n, family = binomial,
contrasts = list(Age = contr.sum, Class = "contr.SAS")
) %>%
tidy_and_attach(exponentiate = TRUE) %>%
tidy_add_reference_rows() %>%
tidy_add_estimate_to_reference_rows()
if (requireNamespace("gtsummary")) {
glm(
response ~ stage + grade * trt,
gtsummary::trial,
family = binomial,
contrasts = list(
stage = contr.treatment(4, base = 3),
grade = contr.treatment(3, base = 2),
trt = contr.treatment(2, base = 2)
)
) %>%
tidy_and_attach() %>%
tidy_add_reference_rows() %>%
tidy_add_estimate_to_reference_rows()
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