library(survey)
library(laeken)
data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) )
# linearized design
des_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 , weights = ~rb050 , data = eusilc )
des_eusilc <- convey_prep(des_eusilc)
# replicate-weighted design
des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" )
des_eusilc_rep <- convey_prep(des_eusilc_rep)
# subset all designs to positive income and non-missing records only
des_eusilc_pos_inc <- subset( des_eusilc , eqincome > 0 )
des_eusilc_rep_pos_inc <- subset( des_eusilc_rep , eqincome > 0 )
# linearized design
svyatk( ~eqincome , des_eusilc_pos_inc, epsilon = .5 )
svyatk( ~eqincome , des_eusilc_pos_inc )
svyatk( ~eqincome , des_eusilc_pos_inc, epsilon = 2 )
# replicate-weighted design
svyatk( ~eqincome , des_eusilc_rep_pos_inc, epsilon = .5 )
svyatk( ~eqincome , des_eusilc_rep_pos_inc )
svyatk( ~eqincome , des_eusilc_rep_pos_inc, epsilon = 2 )
# subsetting
svyatk( ~eqincome , subset(des_eusilc_pos_inc, db040 == "Styria"), epsilon = .5 )
svyatk( ~eqincome , subset(des_eusilc_pos_inc, db040 == "Styria") )
svyatk( ~eqincome , subset(des_eusilc_pos_inc, db040 == "Styria"), epsilon = 2 )
svyatk( ~eqincome , subset(des_eusilc_rep_pos_inc, db040 == "Styria"), epsilon = .5 )
svyatk( ~eqincome , subset(des_eusilc_rep_pos_inc, db040 == "Styria") )
svyatk( ~eqincome , subset(des_eusilc_rep_pos_inc, db040 == "Styria"), epsilon = 2 )
if (FALSE) {
# linearized design using a variable with missings (but subsetted to remove negatives)
svyatk( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n)), epsilon = .5 )
svyatk( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n)), epsilon = .5 , na.rm=TRUE )
# replicate-weighted design using a variable with missings (but subsetted to remove negatives)
svyatk( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n)), epsilon = .5 )
svyatk( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n)), epsilon = .5 , na.rm=TRUE )
# 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 )
# subset all designs to positive income and non-missing records only
dbd_eusilc_pos_inc <- subset( dbd_eusilc , eqincome > 0 )
# database-backed linearized design
svyatk( ~eqincome , dbd_eusilc_pos_inc, epsilon = .5 )
svyatk( ~eqincome , dbd_eusilc_pos_inc )
svyatk( ~eqincome , dbd_eusilc_pos_inc, epsilon = 2 )
svyatk( ~eqincome , subset(dbd_eusilc_pos_inc, db040 == "Styria"), epsilon = .5 )
svyatk( ~eqincome , subset(dbd_eusilc_pos_inc, db040 == "Styria") )
svyatk( ~eqincome , subset(dbd_eusilc_pos_inc, db040 == "Styria"), epsilon = 2 )
# database-backed linearized design using a variable with missings
# (but subsetted to remove negatives)
svyatk( ~py010n , subset(dbd_eusilc, py010n > 0 | is.na(py010n)), epsilon = .5 )
svyatk( ~py010n , subset(dbd_eusilc, py010n > 0 | is.na(py010n)), epsilon = .5 , na.rm=TRUE )
dbRemoveTable( conn , 'eusilc' )
dbDisconnect( conn , shutdown = TRUE )
}
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