使用broom::tidy解释GAM系数和置信区间

Question

有没有一种broom::tidy方法来计算GAM的系数和CI的指数？我特别询问tidy，因为我想使用gtsummary创建的回归表中的结果。

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。如果这不是你想要的，请让我知道。

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)