tests/testthat/test-extract_svyarpt.R

In convey: Income Concentration Analysis with Complex Survey Samples

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

skip_on_cran()

library(laeken)
library(survey)



dstrat1<-convey_prep(svydesign(id=~1,data=apistrat))
test_that("svyarpt works on unweighted designs",{
	svyarpt(~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 <-as.svrepdesign(des_eusilc, type= "bootstrap")
des_eusilc_rep <- convey_prep(des_eusilc_rep)
a1 <- svyarpt(~eqincome, design = des_eusilc, 0.5, 0.6)
a2 <- svyby(~eqincome, by = ~hsize, design = des_eusilc, FUN = svyarpt, quantiles = 0.5, percent = 0.6,deff = FALSE)

b1 <- svyarpt(~eqincome, design = des_eusilc_rep, 0.5, 0.6)

b2 <- svyby(~eqincome, by = ~hsize, design = des_eusilc_rep, FUN = svyarpt, quantiles = 0.5, percent = 0.6,deff = FALSE)

cv_dif1 <- abs(cv(a1)-cv(b1))
cv_diff2 <- max(abs(cv(a2)-cv(b2)))

test_that("output svyarpt",{
  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(cv_dif1, coef(a1) * 0.05 ) # the difference between CVs should be less than 5% of the coefficient, otherwise manually set it
  expect_lte(cv_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)))
})


	 # 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 )


	c1 <- svyarpt( ~ eqincome , design = dbd_eusilc )
	c2 <- svyby(~ eqincome, by = ~hsize, design = dbd_eusilc, FUN = svyarpt, quantiles = 0.5, percent = 0.6,deff = FALSE)

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

	test_that("database svyarpt",{
	  expect_equal(coef(a1), coef(c1))
	  expect_equal(coef(a2), coef(c2))
	  expect_equal(SE(a1), SE(c1))
	  expect_equal(SE(a2), SE(c2))
	})

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

test_that("subsets equal svyby",{
	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)
})




# second run of 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 )

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

	dbd_eusilc_rep <- convey_prep( dbd_eusilc_rep )

	sub_dbd <- svyarpt( ~eqincome , design = subset( dbd_eusilc , hsize == 1) )
	sby_dbd <- svyby( ~eqincome, by = ~hsize, design = dbd_eusilc, FUN = svyarpt)
	sub_dbr <- svyarpt( ~eqincome , design = subset( dbd_eusilc_rep , hsize == 1) )
	sby_dbr <- svyby( ~eqincome, by = ~hsize, design = dbd_eusilc_rep, FUN = svyarpt)

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


	# compare database-backed designs to non-database-backed designs
	test_that("dbi subsets equal non-dbi subsets",{
		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
	test_that("dbi subsets equal dbi svyby",{
		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])
	})