Exponentiating GAM coefficients and confidence intervals using broom::tidy

Question

I am running generalized additive models with mgcv::gam and am trying to organize my results using broom:tidy , but tidy apparently does not exponentiate coefficients or confidence intervals for GAMs, though it does for regular glm models. Is there a broom::tidy method for exponentiating coefficients and CIs from GAMs? I ask specifically about tidy because I would like to use the results in regression tables created by gtsummary .

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

You can directly use tbl_regression() to exponentiate your results. If this is not what you are after please let me know.

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)