Nothing
context("Gpg output survey.design and svyrep.design")
skip_on_cran()
library(laeken)
library(survey)
data(api)
dstrat1 <- convey_prep(svydesign(id = ~ 1, data = apistrat))
dstrat1 <-
update(dstrat1 , sex = ifelse(both == 'Yes' , 'male' , 'female'))
test_that("svygpg works on unweighted designs", {
svygpg( ~ api00, design = dstrat1, ~ sex)
})
data(ses)
names(ses) <- gsub("size" , "size_" , tolower(names(ses)))
des_ses <- svydesign(id = ~ 1,
weights = ~ weights,
data = ses)
des_ses <- convey_prep(des_ses)
des_ses_rep <- as.svrepdesign(des_ses, type = "bootstrap")
des_ses_rep <- convey_prep(des_ses_rep)
a1 <- svygpg( ~ earningshour, des_ses, ~ sex)
a2 <-
svyby(
~ earningshour,
by = ~ location,
design = des_ses,
FUN = svygpg,
sex = ~ sex,
deff = FALSE
)
b1 <- svygpg( ~ earningshour, design = des_ses_rep, ~ sex)
b2 <- svyby(
~ earningshour,
by = ~ location,
design = des_ses_rep,
FUN = svygpg,
sex = ~ sex,
deff = FALSE
)
cv_dif1 <- abs(cv(a1) - cv(b1))
pos_est <- coef(a2) > 0
cv_diff2 <-
max(abs(SE(a2)[pos_est] / coef(a2)[pos_est] - SE(b2)[pos_est] / coef(b2)[pos_est]))
test_that("output svygpg", {
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 , 'ses' , ses)
dbd_ses <-
svydesign(
id = ~ 1,
weights = ~ weights,
data = "ses",
dbname = dbfile,
dbtype = "SQLite"
)
dbd_ses <- convey_prep(dbd_ses)
c1 <- svygpg(formula = ~ earningshour,
design = dbd_ses,
sex = ~ sex)
c2 <-
svyby(
~ earningshour,
by = ~ location,
design = dbd_ses,
FUN = svygpg,
sex = ~ sex,
deff = FALSE
)
dbRemoveTable(conn , 'ses')
test_that("database svygpg", {
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 <-
svygpg(~ earningshour ,
sex = ~ sex,
design = subset(des_ses , location == "AT1"))
sby_des <-
svyby(
~ earningshour,
sex = ~ sex,
by = ~ location,
design = des_ses,
FUN = svygpg
)
sub_rep <-
svygpg(~ earningshour,
sex = ~ sex ,
design = subset(des_ses_rep , location == "AT1"))
sby_rep <-
svyby(
~ earningshour,
sex = ~ sex,
by = ~ location,
design = des_ses_rep,
FUN = svygpg
)
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 , 'ses' , ses)
dbd_ses <-
svydesign(
id = ~ 1,
weights = ~ weights,
data = "ses",
dbname = dbfile,
dbtype = "SQLite"
)
dbd_ses <- convey_prep(dbd_ses)
# create a hacky database-backed svrepdesign object
# mirroring des_ses_rep
dbd_ses_rep <-
svrepdesign(
weights = ~ weights,
repweights = des_ses_rep$repweights ,
scale = des_ses_rep$scale ,
rscales = des_ses_rep$rscales ,
type = "bootstrap" ,
data = "ses" ,
dbtype = "SQLite" ,
dbname = dbfile ,
combined.weights = FALSE
)
dbd_ses_rep <- convey_prep(dbd_ses_rep)
sub_dbd <-
svygpg(~ earningshour,
sex = ~ sex ,
design = subset(dbd_ses , location == "AT1"))
sby_dbd <-
svyby(
~ earningshour,
sex = ~ sex,
by = ~ location,
design = dbd_ses,
FUN = svygpg
)
sub_dbr <-
svygpg(~ earningshour,
sex = ~ sex ,
design = subset(dbd_ses_rep , location == "AT1"))
sby_dbr <-
svyby(
~ earningshour,
sex = ~ sex,
by = ~ location,
design = dbd_ses_rep,
FUN = svygpg
)
dbRemoveTable(conn , 'ses')
# 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])
})
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