使用 broom::tidy 为 GAM 系数和置信区间取幂

Question

我正在使用mgcv::gam运行广义加法模型，并尝试使用broom:tidy组织我的结果，但tidy显然不会对 GAM 的系数或置信区间进行指数化，尽管它适用于常规glm模型。 是否有broom::tidy方法对 GAM 中的系数和 CI 求幂？ 我特别询问tidy因为我想在gtsummary创建的回归表中使用结果。

library(tidyverse)
library(magrittr)
library(mgcv)
library(parameters)
library(gtsummary)
library(broom)

# sample data

id <- 1:2000
gender <- sample(0:1, 2000, replace = T)
age <- sample(17:64, 2000, replace = T)
race <- sample(0:1, 2000, replace = T)
health_score <- sample(0:25, 2000, replace = T)
dead <- sample(0:1, 2000, replace = T)
days_enrolled <- sample(30:3000, 2000, replace = T)

df <- data.frame(id, gender, age, race, health_score, dead, days_enrolled)

# model

model <- gam(dead ~ gender + s(age) + race + s(health_score) + offset(log(days_enrolled)),
            data = df, method = "REML", family = nb())

# both give the same output:

tidy(model, parametric = T, conf.int = T)
tidy(model, parametric = T, conf.int = T, exponentiate = T)

Answer 1

您可以直接使用tbl_regression()对结果求幂。 如果这不是你所追求的，请告诉我。

library(tidyverse)
library(mgcv)
library(parameters)
library(gtsummary)
library(broom)

# sample data

id <- 1:2000
gender <- sample(0:1, 2000, replace = T)
age <- sample(17:64, 2000, replace = T)
race <- sample(0:1, 2000, replace = T)
health_score <- sample(0:25, 2000, replace = T)
dead <- sample(0:1, 2000, replace = T)
days_enrolled <- sample(30:3000, 2000, replace = T)

df <- data.frame(id, gender, age, race, health_score, dead, days_enrolled)

# model

model <- gam(dead ~ gender + s(age) + race + s(health_score) + offset(log(days_enrolled)),
             data = df, method = "REML", family = nb())


tbl_regression(model, exponentiate = TRUE)