set.seed(1234)
# library(nnet)
# 0 confounders

nobias_model <- glm(Y ~ X + U,
                    family = binomial(link = "logit"),
                    data = df_uc_emc_sel_source)

# xu_model <- multinom(
#   paste(X, U) ~ Xstar + Y,
#   data = df_uc_emc_sel_source
# )

s_model <- glm(S ~ Xstar + Y,
               family = binomial(link = "logit"),
               data = df_uc_emc_sel_source)

single_run <- adjust_multinom_uc_emc_sel(
  df_uc_emc_sel,
  exposure = "Xstar",
  outcome = "Y",
  x1u0_model_coefs = c(-1.92, 1.62, 0.70),
  x0u1_model_coefs = c(-0.30, -0.01, 0.69),
  x1u1_model_coefs = c(-1.64, 1.62, 1.35),
  s_model_coefs = c(
    s_model$coef[1],
    s_model$coef[2],
    s_model$coef[3]
  )
)

n <- 100000
nreps <- 10
est <- vector()
for (i in 1:nreps) {
  bdf <- df_uc_emc_sel[sample(seq_len(n), n, replace = TRUE), ]
  results <- adjust_multinom_uc_emc_sel(
    bdf,
    exposure = "Xstar",
    outcome = "Y",
    x1u0_model_coefs = c(-1.92, 1.62, 0.70),
    x0u1_model_coefs = c(-0.30, -0.01, 0.69),
    x1u1_model_coefs = c(-1.64, 1.62, 1.35),
    s_model_coefs = c(
      s_model$coef[1],
      s_model$coef[2],
      s_model$coef[3]
    )
  )
  est[i] <- results$estimate
}

or_true <- exp(summary(nobias_model)$coef[2, 1])
or_adjusted <- median(est)

test_that("0 confounders: odds ratio and confidence interval output", {
  expect_gt(or_adjusted, or_true - 0.1)
  expect_lt(or_adjusted, or_true + 0.1)
  expect_vector(
    single_run$ci,
    ptype = double(),
    size = 2
  )
})

# 1 confounder

nobias_model <- glm(Y ~ X + C1 + U,
                    family = binomial(link = "logit"),
                    data = df_uc_emc_sel_source)

# xu_model <- multinom(
#   paste(X, U) ~ Xstar + Y + C1,
#   data = df_uc_emc_sel_source
# )

s_model <- glm(S ~ Xstar + Y + C1,
               family = binomial(link = "logit"),
               data = df_uc_emc_sel_source)

single_run <- adjust_multinom_uc_emc_sel(
  df_uc_emc_sel,
  exposure = "Xstar",
  outcome = "Y",
  confounders = "C1",
  x1u0_model_coefs = c(-2.10, 1.62, 0.67, 0.35),
  x0u1_model_coefs = c(-0.26, -0.01, 0.70, -0.08),
  x1u1_model_coefs = c(-1.75, 1.62, 1.33, 0.24),
  s_model_coefs = c(
    s_model$coef[1],
    s_model$coef[2],
    s_model$coef[3],
    s_model$coef[4]
  )
)

n <- 100000
nreps <- 10
est <- vector()
for (i in 1:nreps) {
  bdf <- df_uc_emc_sel[sample(seq_len(n), n, replace = TRUE), ]
  results <- adjust_multinom_uc_emc_sel(
    bdf,
    exposure = "Xstar",
    outcome = "Y",
    confounders = "C1",
    x1u0_model_coefs = c(-2.10, 1.62, 0.67, 0.35),
    x0u1_model_coefs = c(-0.26, -0.01, 0.70, -0.08),
    x1u1_model_coefs = c(-1.75, 1.62, 1.33, 0.24),
    s_model_coefs = c(
      s_model$coef[1],
      s_model$coef[2],
      s_model$coef[3],
      s_model$coef[4]
    )
  )
  est[i] <- results$estimate
}

or_true <- exp(summary(nobias_model)$coef[2, 1])
or_adjusted <- median(est)

test_that("1 confounder: odds ratio and confidence interval output", {
  expect_gt(or_adjusted, or_true - 0.1)
  expect_lt(or_adjusted, or_true + 0.1)
  expect_vector(
    single_run$ci,
    ptype = double(),
    size = 2
  )
})

# 2 confounders

nobias_model <- glm(Y ~ X + C1 + C2 + U,
                    family = binomial(link = "logit"),
                    data = df_uc_emc_sel_source)

# xu_model <- multinom(
#   paste(X, U) ~ Xstar + Y + C1 + C2,
#   data = df_uc_emc_sel_source
# )

s_model <- glm(S ~ Xstar + Y + C1 + C2,
               family = binomial(link = "logit"),
               data = df_uc_emc_sel_source)

single_run <- adjust_multinom_uc_emc_sel(
  df_uc_emc_sel,
  exposure = "Xstar",
  outcome = "Y",
  confounders = c("C1", "C2"),
  x1u0_model_coefs = c(-2.05, 1.62, 0.64, 0.35, -0.26),
  x0u1_model_coefs = c(-0.28, -0.01, 0.71, -0.08, 0.07),
  x1u1_model_coefs = c(-1.74, 1.62, 1.32, 0.24, -0.04),
  s_model_coefs = c(
    s_model$coef[1],
    s_model$coef[2],
    s_model$coef[3],
    s_model$coef[4],
    s_model$coef[5]
  )
)

n <- 100000
nreps <- 10
est <- vector()
for (i in 1:nreps) {
  bdf <- df_uc_emc_sel[sample(seq_len(n), n, replace = TRUE), ]
  results <- adjust_multinom_uc_emc_sel(
    bdf,
    exposure = "Xstar",
    outcome = "Y",
    confounders = c("C1", "C2"),
    x1u0_model_coefs = c(-2.05, 1.62, 0.64, 0.35, -0.26),
    x0u1_model_coefs = c(-0.28, -0.01, 0.71, -0.08, 0.07),
    x1u1_model_coefs = c(-1.74, 1.62, 1.32, 0.24, -0.04),
    s_model_coefs = c(
      s_model$coef[1],
      s_model$coef[2],
      s_model$coef[3],
      s_model$coef[4],
      s_model$coef[5]
    )
  )
  est[i] <- results$estimate
}

or_true <- exp(summary(nobias_model)$coef[2, 1])
or_adjusted <- median(est)

test_that("2 confounders: odds ratio and confidence interval output", {
  expect_gt(or_adjusted, or_true - 0.1)
  expect_lt(or_adjusted, or_true + 0.1)
  expect_vector(
    single_run$ci,
    ptype = double(),
    size = 2
  )
})

# 3 confounders

nobias_model <- glm(Y ~ X + C1 + C2 + C3 + U,
                    family = binomial(link = "logit"),
                    data = df_uc_emc_sel_source)

# xu_model <- multinom(
#   paste(X, U) ~ Xstar + Y + C1 + C2 + C3,
#   data = df_uc_emc_sel_source
# )

s_model <- glm(S ~ Xstar + Y + C1 + C2 + C3,
               family = binomial(link = "logit"),
               data = df_uc_emc_sel_source)

single_run <- adjust_multinom_uc_emc_sel(
  df_uc_emc_sel,
  exposure = "Xstar",
  outcome = "Y",
  confounders = c("C1", "C2", "C3"),
  x1u0_model_coefs = c(-2.78, 1.62, 0.61, 0.36, -0.27, 0.88),
  x0u1_model_coefs = c(-0.17, -0.01, 0.71, -0.08, 0.07, -0.15),
  x1u1_model_coefs = c(-2.36, 1.62, 1.29, 0.25, -0.06, 0.74),
  s_model_coefs = c(
    s_model$coef[1],
    s_model$coef[2],
    s_model$coef[3],
    s_model$coef[4],
    s_model$coef[5],
    s_model$coef[6]
  )
)

n <- 100000
nreps <- 10
est <- vector()
for (i in 1:nreps) {
  bdf <- df_uc_emc_sel[sample(seq_len(n), n, replace = TRUE), ]
  results <- adjust_multinom_uc_emc_sel(
    bdf,
    exposure = "Xstar",
    outcome = "Y",
    confounders = c("C1", "C2", "C3"),
    x1u0_model_coefs = c(-2.78, 1.62, 0.61, 0.36, -0.27, 0.88),
    x0u1_model_coefs = c(-0.17, -0.01, 0.71, -0.08, 0.07, -0.15),
    x1u1_model_coefs = c(-2.36, 1.62, 1.29, 0.25, -0.06, 0.74),
    s_model_coefs = c(
      s_model$coef[1],
      s_model$coef[2],
      s_model$coef[3],
      s_model$coef[4],
      s_model$coef[5],
      s_model$coef[6]
    )
  )
  est[i] <- results$estimate
}

or_true <- exp(summary(nobias_model)$coef[2, 1])
or_adjusted <- median(est)

test_that("3 confounders: odds ratio and confidence interval output", {
  expect_gt(or_adjusted, or_true - 0.1)
  expect_lt(or_adjusted, or_true + 0.1)
  expect_vector(
    single_run$ci,
    ptype = double(),
    size = 2
  )
})