## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment  = "#>",
  fig.width = 8,
  fig.height = 8,
  message = FALSE,
  warning = FALSE
)
library(ggplot2); library(dplyr); library(tidyr)
data.table::setDTthreads(1L)
options(dplyr.summarise.inform = FALSE, scipen = 999, digits = 5)
theme_set(theme_bw(base_size = 12))

## ----draw_data----------------------------------------------------------------
library(childpen)

data <- simulate_data(n_individuals = 5000, treatment_groups = 24:28)
data |> tibble()

## ----estimation---------------------------------------------------------------
res = multiple_treatment_group_analysis(data = data,
                                  treatment_groups = 24:25, # which treatment groups to run in the analysis
                                  periods_post = 2, # estimate results for post periods 0:2
                                  periods_pre = 2, # estimate pre-trend diagnostics, set to NULL to omit from estimation
                                  max_age = 40, # dont estimate results if age is above 40
                                  min_age = 20, # dont estimate results if age is below 20
                                  pre = 1, # use 1 period before treatment, can make further away if anticipation is concern
                                  verbose = FALSE # set to TRUE to output progress (i like to time loops) set to FALSE to omit messages
                                  )

## ----results_first------------------------------------------------------------
res |> tibble()

## -----------------------------------------------------------------------------
res |>
  filter(d == 24,
         a < d,
         estimand == "ATE",
         method == "DID_Female") |>
  mutate(control_offset = dp - d,
         control_offset = factor(control_offset)) |>
  ggplot(aes(x = event_time, y = est, ymin = ci_l, ymax = ci_h, color = control_offset, fill = control_offset)) +
  geom_ribbon(alpha = .15, color = NA) + geom_point() + geom_line() +
  scale_x_continuous(breaks = -3:-2) +
  facet_grid(cols = vars(control_offset))

## -----------------------------------------------------------------------------
res |>
  filter(d == 24,
         a < d,
         estimand == "ATE",
         method == "DID_Female") |>
  mutate(control_offset = dp - d, 
         control_offset = factor(control_offset)) |> 
  ggplot(aes(x = event_time, y = est, ymin = ci_l, ymax = ci_h, color = control_offset, fill = control_offset)) +
  geom_ribbon(alpha = .15, color = NA) + geom_point() + geom_line() + 
  scale_x_continuous(breaks = -3:-2)

## -----------------------------------------------------------------------------
res |> 
  filter(a < d, 
         estimand == "ATE",
         method == "DID_Female") |> 
  mutate(control_offset = dp - d, 
         control_offset = factor(control_offset)) |> 
  ggplot(aes(x = event_time, y = est, ymin = ci_l, ymax = ci_h, color = control_offset, fill = control_offset)) +
  geom_ribbon(alpha = .15, color = NA) + geom_point() + geom_line() + 
  scale_x_continuous(breaks = -3:-2) + 
  facet_grid(cols = vars(d))

## -----------------------------------------------------------------------------
res |> 
  filter(a < d, 
         estimand == "ATE" & (method == "DID_Female" | method == "DID_Male" | method == "TD") |
           estimand == "theta" & method == "NTD_Conv") |>
  mutate(control_offset = dp - d, 
         control_offset = factor(control_offset)) |> 
  ggplot(aes(x = event_time, y = est, ymin = ci_l, ymax = ci_h, color = control_offset, fill = control_offset)) +
  geom_ribbon(alpha = .15, color = NA) + geom_point() + geom_line() + 
  scale_x_continuous(breaks = -3:-2) + 
  facet_grid(cols = vars(d), rows = vars(method), scales = "free")