context("svyjdiv output survey.design and svyrep.design")

skip_on_cran()

library(laeken)
library(survey)

data(api)
dstrat1 <- convey_prep(svydesign(id =  ~ 1, data = apistrat))
test_that("svyjdiv works on unweighted designs", {
  svyjdiv(~ api00, design = dstrat1)
})


data(eusilc)
names(eusilc) <- tolower(names(eusilc))
des_eusilc <-
  svydesign(
    ids = ~ rb030,
    strata =  ~ db040,
    weights = ~ rb050,
    data = eusilc
  )
des_eusilc <- convey_prep(des_eusilc)
des_eusilc_rep_save <-
  des_eusilc_rep <-
  as.svrepdesign(des_eusilc, type = "bootstrap" , replicates = 10)
des_eusilc_rep <- convey_prep(des_eusilc_rep)
des_eusilc <- subset(des_eusilc , eqincome > 0)
des_eusilc_rep <- subset(des_eusilc_rep , eqincome > 0)


# estimates
a1 <- svyjdiv(~ eqincome, design = des_eusilc)
a2 <-
  svyby(~ eqincome,
        by = ~ hsize,
        design = des_eusilc,
        FUN = svyjdiv)
b1 <- svyjdiv(~ eqincome, design = des_eusilc_rep)
b2 <-
  svyby(~ eqincome,
        by = ~ hsize,
        design = des_eusilc_rep,
        FUN = svyjdiv)

se_dif1 <- abs(SE(a1) - SE(b1))
se_diff2 <- max(abs(SE(a2) - SE(b2)))

test_that("output svyjdiv", {
  expect_is(coef(a1), "numeric")
  expect_is(coef(a2), "numeric")
  expect_is(coef(b1), "numeric")
  expect_is(coef(b2), "numeric")
  expect_equal(coef(a1), coef(b1))
  expect_equal(coef(a2), coef(b2))
  expect_lte(se_dif1, coef(a1) * 0.05) # the difference between CVs should be less than 5% of the coefficient, otherwise manually set it
  expect_lte(se_diff2, max(coef(a2)) * 0.1) # the difference between CVs should be less than 10% of the maximum coefficient, otherwise manually set it
  expect_is(SE(a1), "matrix")
  expect_is(SE(a2), "numeric")
  expect_is(SE(b1), "numeric")
  expect_is(SE(b2), "numeric")
  expect_lte(confint(a1)[1], coef(a1))
  expect_gte(confint(a1)[2], coef(a1))
  expect_lte(confint(b1)[, 1], coef(b1))
  expect_gte(confint(b1)[2], coef(b1))
  expect_equal(sum(confint(a2)[, 1] <= coef(a2)), length(coef(a2)))
  expect_equal(sum(confint(a2)[, 2] >= coef(a2)), length(coef(a2)))
  expect_equal(sum(confint(b2)[, 1] <= coef(b2)), length(coef(b2)))
  expect_equal(sum(confint(b2)[, 2] >= coef(b2)), length(coef(b2)))
})

# compare for theil-decomposability
g0 <- svygei( ~ eqincome , design = des_eusilc , epsilon = 0)
g1 <- svygei( ~ eqincome , design = des_eusilc , epsilon = 1)

test_that("jdiv qual sum of gei0 and gei1", {
  expect_equal(as.numeric(coef(a1)), as.numeric(coef(g0) + coef(g1))[1])
  expect_equal(as.numeric(coef(b1)), as.numeric(coef(g0) + coef(g1))[1])
})


test_that("database-backed designs", {
  # database-backed design
  library(RSQLite)
  library(DBI)
  dbfile <- tempfile()
  conn <- dbConnect(RSQLite::SQLite() , dbfile)
  dbWriteTable(conn , 'eusilc' , eusilc)

  dbd_eusilc <-
    svydesign(
      ids = ~ rb030 ,
      strata = ~ db040 ,
      weights = ~ rb050 ,
      data = "eusilc",
      dbname = dbfile,
      dbtype = "SQLite"
    )
  dbd_eusilc <- convey_prep(dbd_eusilc)
  dbd_eusilc <- subset(dbd_eusilc , eqincome > 0)


  # create a hacky database-backed svrepdesign object
  # mirroring des_eusilc_rep_save
  dbd_eusilc_rep <-
    svrepdesign(
      weights = ~ rb050,
      repweights = des_eusilc_rep_save$repweights ,
      scale = des_eusilc_rep_save$scale ,
      rscales = des_eusilc_rep_save$rscales ,
      type = "bootstrap" ,
      data = "eusilc" ,
      dbtype = "SQLite" ,
      dbname = dbfile ,
      combined.weights = FALSE
    )
  dbd_eusilc_rep <- convey_prep(dbd_eusilc_rep)
  dbd_eusilc_rep <- subset(dbd_eusilc_rep , eqincome > 0)

  # point estimates
  c1 <- svyjdiv( ~ eqincome , design = dbd_eusilc)
  c2 <-
    svyby(~ eqincome,
          by = ~ hsize,
          design = dbd_eusilc,
          FUN = svyjdiv)

  # compare subsetted objects to svyby objects
  sub_des <-
    svyjdiv( ~ eqincome , design = subset(des_eusilc , hsize == 1))
  sby_des <-
    svyby( ~ eqincome,
           by = ~ hsize,
           design = des_eusilc,
           FUN = svyjdiv)
  sub_rep <-
    svyjdiv( ~ eqincome , design = subset(des_eusilc_rep , hsize == 1))
  sby_rep <-
    svyby( ~ eqincome,
           by = ~ hsize,
           design = des_eusilc_rep,
           FUN = svyjdiv)

  # compare subsetted objects to svyby objects for resampling results
  sub_dbd <-
    svyjdiv( ~ eqincome , design = subset(dbd_eusilc , hsize == 1))
  sby_dbd <-
    svyby( ~ eqincome,
           by = ~ hsize,
           design = dbd_eusilc,
           FUN = svyjdiv)
  sub_dbr <-
    svyjdiv( ~ eqincome , design = subset(dbd_eusilc_rep , hsize == 1))
  sby_dbr <-
    svyby( ~ eqincome,
           by = ~ hsize,
           design = dbd_eusilc_rep,
           FUN = svyjdiv)

  dbRemoveTable(conn , 'eusilc')
  dbDisconnect(conn)

  expect_equal(coef(a1), coef(c1))
  expect_equal(coef(a2), coef(c2))
  expect_equal(SE(a1), SE(c1))
  expect_equal(SE(a2), SE(c2))

  expect_equal(as.numeric(coef(sub_des)), as.numeric(coef(sby_des))[1])
  expect_equal(as.numeric(coef(sub_rep)), as.numeric(coef(sby_rep))[1])
  expect_equal(as.numeric(SE(sub_des)), as.numeric(SE(sby_des))[1])
  expect_equal(as.numeric(SE(sub_rep)), as.numeric(SE(sby_rep))[1])

  # coefficients should match across svydesign & svrepdesign
  expect_equal(as.numeric(coef(sub_des)), as.numeric(coef(sby_rep))[1])

  # coefficients of variation should be within five percent
  cv_dif <- abs(cv(sub_des) - cv(sby_rep)[1])
  expect_lte(cv_dif, 5)

  expect_equal(coef(sub_des), coef(sub_dbd))
  expect_equal(coef(sub_rep), coef(sub_dbr))
  expect_equal(SE(sub_des), SE(sub_dbd))
  expect_equal(SE(sub_rep), SE(sub_dbr))

  # compare database-backed subsetted objects to database-backed svyby objects
  expect_equal(as.numeric(coef(sub_dbd)), as.numeric(coef(sby_dbd))[1])
  expect_equal(as.numeric(coef(sub_dbr)), as.numeric(coef(sby_dbr))[1])
  expect_equal(as.numeric(SE(sub_dbd)), as.numeric(SE(sby_dbd))[1])
  expect_equal(as.numeric(SE(sub_dbr)), as.numeric(SE(sby_dbr))[1])

})