## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "figures/getting-started-"
)
set.seed(1)

## -----------------------------------------------------------------------------
library(ivcheck)

set.seed(1)
n <- 500
z <- sample(0:1, n, replace = TRUE)
d <- rbinom(n, 1, 0.3 + 0.4 * z)
y <- rnorm(n, mean = d)

result <- iv_kitagawa(y, d, z, n_boot = 500, parallel = FALSE)
print(result)

## -----------------------------------------------------------------------------
str(result, max.level = 1)

## -----------------------------------------------------------------------------
quantile(result$boot_stats, c(0.9, 0.95, 0.99))

## -----------------------------------------------------------------------------
set.seed(2)
n <- 1500
z <- sample(0:1, n, replace = TRUE)
d <- rbinom(n, 1, 0.3 + 0.4 * z)
# Direct Z effect on Y for treated units only: clean exclusion violation
y <- rnorm(n, mean = d + 2 * d * z)

result_v <- iv_kitagawa(y, d, z, n_boot = 500, parallel = FALSE)
print(result_v)

## -----------------------------------------------------------------------------
result_v$binding

## -----------------------------------------------------------------------------
set.seed(3)
n <- 800
x <- rnorm(n)
z <- rbinom(n, 1, plogis(x))
d <- rbinom(n, 1, plogis(-0.5 + 0.8 * z + 0.5 * x))
y <- rnorm(n, mean = d + 0.3 * x)

result_mw <- iv_mw(y, d, z, x = x, n_bins = 4, n_boot = 300, parallel = FALSE)
print(result_mw)

## -----------------------------------------------------------------------------
set.seed(1)
n <- 600
z <- sample(0:1, n, replace = TRUE)
d <- rbinom(n, 1, 0.3 + 0.4 * z)
y <- rnorm(n, mean = d)

check <- structure(list(
  table = data.frame(
    test = c("Kitagawa (2015)", "Mourifie-Wan (2017)"),
    statistic = c(0.3, 0.35),
    p_value = c(0.82, 0.78),
    verdict = c("pass", "pass")
  ),
  alpha = 0.05,
  overall = "cannot reject IV validity at 0.05."
), class = "iv_check")
print(check)