For binomial and multinomial logistic models, will also return the number of events.
Usage
model_get_n(model)
# Default S3 method
model_get_n(model)
# S3 method for class 'glm'
model_get_n(model)
# S3 method for class 'glmerMod'
model_get_n(model)
# S3 method for class 'multinom'
model_get_n(model)
# S3 method for class 'LORgee'
model_get_n(model)
# S3 method for class 'coxph'
model_get_n(model)
# S3 method for class 'survreg'
model_get_n(model)
# S3 method for class 'model_fit'
model_get_n(model)
# S3 method for class 'tidycrr'
model_get_n(model)Details
For Poisson models, will return the number of events and exposure time
(defined with stats::offset()).
For Cox models (survival::coxph()), will return the number of events,
exposure time and the number of individuals.
For competing risk regression models (tidycmprsk::crr()), n_event takes
into account only the event of interest defined by failcode.
See tidy_add_n() for more details.
The total number of observations (N_obs), of individuals (N_ind), of
events (N_event) and of exposure time (Exposure) are stored as attributes
of the returned tibble.
This function does not cover lavaan models (NULL is returned).
See also
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_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_identify_variables(),
model_list_contrasts(),
model_list_higher_order_variables(),
model_list_terms_levels(),
model_list_variables()
Examples
lm(hp ~ mpg + factor(cyl) + disp:hp, mtcars) |>
model_get_n()
#> # A tibble: 6 × 2
#> term n_obs
#> <chr> <dbl>
#> 1 (Intercept) 32
#> 2 mpg 32
#> 3 factor(cyl)6 7
#> 4 factor(cyl)8 14
#> 5 hp:disp 32
#> 6 factor(cyl)4 11
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_get_n()
#> # A tibble: 16 × 3
#> term n_obs n_event
#> <chr> <dbl> <dbl>
#> 1 (Intercept) 193 61
#> 2 stage1 52 18
#> 3 stage2 52 13
#> 4 stage3 40 15
#> 5 grade1 67 21
#> 6 grade3 63 21
#> 7 trtDrug A 95 28
#> 8 stage1:grade1 17 6
#> 9 stage2:grade1 17 4
#> 10 stage3:grade1 18 7
#> 11 stage1:grade3 13 6
#> 12 stage2:grade3 19 5
#> 13 stage3:grade3 13 4
#> 14 stage4 49 15
#> 15 grade2 63 19
#> 16 trtDrug B 98 33
if (FALSE) { # \dontrun{
mod <- glm(
Survived ~ Class * Age + Sex,
data = Titanic |> as.data.frame(),
weights = Freq, family = binomial
)
mod |> model_get_n()
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_get_n()
mod <- glm(response ~ age + grade * trt, gtsummary::trial, family = poisson)
mod |> model_get_n()
mod <- glm(
response ~ trt * grade + offset(ttdeath),
gtsummary::trial,
family = poisson
)
mod |> model_get_n()
dont
df <- survival::lung |> dplyr::mutate(sex = factor(sex))
mod <- survival::coxph(survival::Surv(time, status) ~ ph.ecog + age + sex, data = df)
mod |> model_get_n()
mod <- lme4::lmer(Reaction ~ Days + (Days | Subject), lme4::sleepstudy)
mod |> model_get_n()
mod <- lme4::glmer(response ~ trt * grade + (1 | stage),
family = binomial, data = gtsummary::trial
)
mod |> model_get_n()
mod <- lme4::glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
family = binomial, data = lme4::cbpp
)
mod |> model_get_n()
} # }