Compute a matrix of terms contributions
Used for model_get_n(). For each row and term, equal 1 if this row should
be taken into account in the estimate of the number of observations,
0 otherwise.
model_compute_terms_contributions(model) ## Default S3 method: model_compute_terms_contributions(model)
model |
a model object |
This function does not cover lavaan models (NULL is returned).
Other model_helpers:
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_response(),
model_get_terms(),
model_get_weights(),
model_get_xlevels(),
model_identify_variables(),
model_list_contrasts(),
model_list_terms_levels(),
model_list_variables()
mod <- lm(Sepal.Length ~ Sepal.Width, iris)
mod %>% model_compute_terms_contributions()
mod <- lm(hp ~ mpg + factor(cyl) + disp:hp, mtcars)
mod %>% model_compute_terms_contributions()
mod <- glm(
response ~ stage * grade + trt,
gtsummary::trial,
family = binomial,
contrasts = list(
stage = contr.sum,
grade = contr.treatment(3, 2),
trt = "contr.SAS"
)
)
mod %>% model_compute_terms_contributions()
mod <- glm(
response ~ stage * trt,
gtsummary::trial,
family = binomial,
contrasts = list(stage = contr.poly)
)
mod %>% model_compute_terms_contributions()
mod <- glm(
Survived ~ Class * Age + Sex,
data = Titanic %>% as.data.frame(),
weights = Freq, family = binomial
)
mod %>% model_compute_terms_contributions()
d <- dplyr::as_tibble(Titanic) %>%
dplyr::group_by(Class, Sex, Age) %>%
dplyr::summarise(
n_survived = sum(n * (Survived == "Yes")),
n_dead = sum(n * (Survived == "No"))
)
mod <- glm(cbind(n_survived, n_dead) ~ Class * Age + Sex, data = d, family = binomial)
mod %>% model_compute_terms_contributions()Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.