## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment  = "#>",
  fig.width  = 7,
  fig.height = 4
)

## ----logistic-----------------------------------------------------------------
library(ggadjustedforest)

data(mtcars)
mtcars$am <- as.integer(mtcars$am)   # binary outcome: automatic (0) vs manual (1)

result <- gg_adjusted_forest(
  data       = mtcars,
  outcome    = "am",
  exposure   = "hp",
  covariates = c("wt", "cyl"),
  model_type = "logistic",
  title      = "Effect of Horsepower on Transmission Type"
)
result$table

## ----plot-logistic------------------------------------------------------------
result$plot

## ----cumulative---------------------------------------------------------------
result_cum <- gg_adjusted_forest(
  data       = mtcars,
  outcome    = "am",
  exposure   = "hp",
  covariates = c("wt", "cyl", "disp"),
  cumulative = TRUE,
  title      = "Cumulative adjustment: hp on transmission"
)
result_cum$formatted_table[, c("model", "formatted", "p.value")]

## ----cumulative-labels--------------------------------------------------------
labels <- c(
  "Unadjusted"         = "Crude",
  "+ wt"               = "Adjusted for weight",
  "+ wt + cyl"         = "Adjusted for weight + cylinders",
  "+ wt + cyl + disp"  = "Fully adjusted"
)

result_cum2 <- gg_adjusted_forest(
  data              = mtcars,
  outcome           = "am",
  exposure          = "hp",
  covariates        = c("wt", "cyl", "disp"),
  cumulative        = TRUE,
  cumulative_labels = labels,
  title             = "Cumulative adjustment with custom labels"
)
result_cum2$plot

## ----cox----------------------------------------------------------------------
lung <- survival::lung
lung$status01 <- as.integer(lung$status == 2)
lung <- stats::na.omit(lung[, c("time", "status01", "age", "sex", "ph.ecog")])

result_cox <- gg_adjusted_forest(
  data       = lung,
  outcome    = "status01",
  exposure   = "age",
  covariates = c("sex", "ph.ecog"),
  model_type = "coxph",
  time_var   = "time",
  event_var  = "status01",
  title      = "Effect of Age on Survival (lung cancer)"
)
result_cox$plot

## ----custom-------------------------------------------------------------------
gg_adjusted_forest(
  data           = mtcars,
  outcome        = "am",
  exposure       = "hp",
  covariates     = "wt",
  model_type     = "logistic",
  color          = "#2166ac",
  point_size     = 5,
  point_shape    = 18,          # diamond
  vline_color    = "firebrick",
  vline_linetype = "dotted",
  x_breaks       = c(0.9, 1.0, 1.1),
  title          = "Custom aesthetics"
)$plot

## ----linear-------------------------------------------------------------------
gg_adjusted_forest(
  data       = mtcars,
  outcome    = "mpg",
  exposure   = "hp",
  covariates = c("wt", "cyl"),
  model_type = "linear",
  title      = "Effect of Horsepower on Fuel Efficiency"
)$plot

## ----table-only---------------------------------------------------------------
forest_table(
  data       = mtcars,
  outcome    = "am",
  exposure   = "hp",
  covariates = c("wt", "cyl"),
  model_type = "logistic"
)

## ----multi-patchwork, warning=FALSE, fig.height=5, fig.width=10---------------
library(patchwork)

data(mtcars)
mtcars$am <- as.integer(mtcars$am)
mtcars$vs <- as.integer(mtcars$vs)

# Extract $plot — patchwork composes ggplot2 objects directly
p_am <- gg_adjusted_forest(
  data = mtcars, outcome = "am", exposure = "hp",
  covariates = c("wt", "cyl"), model_type = "logistic",
  title = "Transmission", show_table = FALSE
)$plot

p_vs <- gg_adjusted_forest(
  data = mtcars, outcome = "vs", exposure = "hp",
  covariates = c("wt", "cyl"), model_type = "logistic",
  title = "Engine Shape", show_table = FALSE
)$plot

p_am / p_vs