context("Test output")

test_that("Check if output is printed", {

  # Let us generate some panel data
  #-----------------------------------------------------------------------------
  # DGP 1 used by Sant'Anna and Zhao (2020) (Panel data case)
  # Sample size
  n <- 500
  # pscore index (strength of common support)
  Xsi.ps <- .75
  # Researcher always observes Z
  #-----------------------------------------------------------------------------
  # Mean and Std deviation of Z's without truncation
  mean.z1 <- exp(0.25/2)
  sd.z1 <- sqrt((exp(0.25) - 1) * exp(0.25))
  mean.z2 <- 10
  sd.z2 <- 0.54164
  mean.z3 <- 0.21887
  sd.z3 <-   0.04453
  mean.z4 <- 402
  sd.z4 <-  56.63891
  #-----------------------------------------------------------------------------
  set.seed(1234)
  # Gen covariates
  x1 <- stats::rnorm(n, mean = 0, sd = 1)
  x2 <- stats::rnorm(n, mean = 0, sd = 1)
  x3 <- stats::rnorm(n, mean = 0, sd = 1)
  x4 <- stats::rnorm(n, mean = 0, sd = 1)

  z1 <- exp(x1/2)
  z2 <- x2/(1 + exp(x1)) + 10
  z3 <- (x1 * x3/25 + 0.6)^3
  z4 <- (x1 + x4 + 20)^2

  z1 <- (z1 - mean.z1)/sd.z1
  z2 <- (z2 - mean.z2)/sd.z2
  z3 <- (z3 - mean.z3)/sd.z3
  z4 <- (z4 - mean.z4)/sd.z4

  x <- cbind(x1, x2, x3, x4)
  z <- cbind(z1, z2, z3, z4)
  #-----------------------------------------------------------------------------
  # Gen treatment groups
  # Propensity score
  pi <- stats::plogis(Xsi.ps * (- z1 + 0.5 * z2 - 0.25 * z3 - 0.1 * z4))
  d  <- as.numeric(runif(n) <= pi)
  #-----------------------------------------------------------------------------
  # Generate aux indexes for the potential outcomes
  index.lin <- 210 + 27.4*z1 + 13.7*(z2 + z3 + z4)
  index.unobs.het <- d * (index.lin)
  index.att <- 0

  #This is the key for consistency of outcome regression
  index.trend <- 210 + 27.4*z1 + 13.7*(z2 + z3 + z4)

  #v is the unobserved heterogeneity
  v <- stats::rnorm(n, mean = index.unobs.het, sd = 1)

  #Gen realized outcome at time 0
  y0 <- index.lin + v + stats::rnorm(n)

  # gen outcomes at time 1
  # First let's generate potential outcomes: y_1_potential
  y10 <- index.lin + v + stats::rnorm(n, mean = 0, sd = 1) +#This is the baseline
    index.trend #this is for the trend based on X

  y11 <- index.lin + v + stats::rnorm(n, mean = 0, sd = 1) +#This is the baseline
    index.trend + #this is for the trend based on X
    index.att # This is the treatment effects

  # Gen realized outcome at time 1
  y1 <- d * y11 + (1 - d) * y10
  #-----------------------------------------------------------------------------
  #Gen id
  id <- 1:n
  #-----------------------------------------------------------------------------
  # Put in a "wide" data frame
  dta_wide <- as.data.frame(cbind(id = id, y1 = y1, y0 = y0, d = d,
                                  x1 = z1, x2= z2, x3 = z3, x4 = z4))
  # Make "long" data
  dta_long <- as.data.frame(cbind(id = id, y = y1, d = d, post = T,
                                  x1 = z1, x2= z2, x3 = z3, x4 = z4))
  dta_long <- data.frame(rbind(dta_long,cbind(id = id, y = y0, d = d, post = F,
                                              x1 = z1, x2= z2, x3 = z3, x4 = z4)))
  dta_long <- dta_long[order(dta_long$id),]

  #-----------------------------------------------------------------------------
  #-----------------------------------------------------------------------------
  #-----------------------------------------------------------------------------
  #Output

  drout_p <- drdid(yname="y",
                   tname = "post",
                   idname = "id",
                   dname = "d",
                   xformla= ~ x1 + x2 + x3 + x4,
                   data = dta_long,
                   panel=T,
                   boot = F)
  expect_output(summary(drout_p))
  expect_output(print(drout_p))

  drout_rc <- drdid(yname="y",
                    tname = "post",
                    idname = "id",
                    dname = "d",
                    xformla= ~ x1 + x2 + x3 + x4,
                    data = dta_long,
                    panel=F,
                    boot = F)
  expect_output(summary(drout_rc))
  expect_output(print(drout_rc))

  drout_p_b <- drdid(yname="y",
                     tname = "post",
                     idname = "id",
                     dname = "d",
                     xformla= ~ x1 + x2 + x3 + x4,
                     data = dta_long,
                     panel=T,
                     boot = T)
  expect_output(summary(drout_p_b))
  expect_output(print(drout_p_b))


  drout_p_b2 <- drdid(yname="y",
                      tname = "post",
                      idname = "id",
                      dname = "d",
                      xformla= ~ x1 + x2 + x3 + x4,
                      estMethod = "trad",
                      data = dta_long,
                      panel=T,
                      boot = T)
  expect_output(summary(drout_p_b2))
  expect_output(print(drout_p_b2))


  orout <- ordid(yname="y",
                 tname = "post",
                 idname = "id",
                 dname = "d",
                 xformla= ~ x1 + x2 + x3 + x4,
                 data = dta_long,
                 panel=T,
                 boot = F)
  expect_output(summary(orout))
  expect_output(print(orout))

  ipwout <- ipwdid(yname="y",
                 tname = "post",
                 idname = "id",
                 dname = "d",
                 xformla= ~ x1 + x2 + x3 + x4,
                 data = dta_long,
                 panel=T,
                 boot = F)
  expect_output(summary(ipwout))
  expect_output(print(ipwout))

  ipwout2 <- ipwdid(yname="y",
                   tname = "post",
                   idname = "id",
                   dname = "d",
                   xformla= ~ x1 + x2 + x3 + x4,
                   data = dta_long,
                   normalized = F,
                   panel=T,
                   boot = F)
  expect_output(summary(ipwout2))
  expect_output(print(ipwout2))

  dr_trad1.did_rc2 <- drdid_rc1(dta_long$y,
                                dta_long$post,
                                dta_long$d,
                                dta_long[,5:8], boot = F)
  expect_output(summary(dr_trad1.did_rc2))
  expect_output(print(dr_trad1.did_rc2))

  dr_imp1.did_rc2 <- drdid_imp_rc1(dta_long$y,
                                   dta_long$post,
                                   dta_long$d,
                                   dta_long[,5:8], boot = F)

  expect_output(summary(dr_imp1.did_rc2))
  expect_output(print(dr_imp1.did_rc2))



  #-----------------------------------------------------------------------------

})