tests/testthat/test-extract_svyjdiv-pos.R

In convey: Income Concentration Analysis with Complex Survey Samples

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])

})