Estimate ratio of the total income received by the highest earners to the total income received by lowest earners, defaulting to 20
svyqsr(formula, design, ...)
# S3 method for survey.design
svyqsr(
formula,
design,
alpha1 = 0.2,
alpha2 = (1 - alpha1),
na.rm = FALSE,
upper_quant = FALSE,
lower_quant = FALSE,
upper_tot = FALSE,
lower_tot = FALSE,
deff = FALSE,
linearized = FALSE,
influence = FALSE,
...
)
# S3 method for svyrep.design
svyqsr(
formula,
design,
alpha1 = 0.2,
alpha2 = (1 - alpha1),
na.rm = FALSE,
upper_quant = FALSE,
lower_quant = FALSE,
upper_tot = FALSE,
lower_tot = FALSE,
deff = FALSE,
linearized = FALSE,
return.replicates = FALSE,
...
)
# S3 method for DBIsvydesign
svyqsr(formula, design, ...)
a formula specifying the income variable
a design object of class survey.design
or class svyrep.design
from the survey
library.
future expansion
order of the lower quintile
order of the upper quintile
Should cases with missing values be dropped?
return the lower bound of highest earners
return the upper bound of lowest earners
return the highest earners total
return the lowest earners total
Return the design effect (see survey::svymean
)
Should a matrix of linearized variables be returned
Should a matrix of (weighted) influence functions be returned? (for compatibility with svyby
)
Return the replicate estimates?
Object of class "cvystat
", which are vectors with a "var
" attribute giving the variance and a "statistic
" attribute giving the name of the statistic.
you must run the convey_prep
function on your survey design object immediately after creating it with the svydesign
or svrepdesign
function.
Guillaume Osier (2009). Variance estimation for complex indicators of poverty and inequality. Journal of the European Survey Research Association, Vol.3, No.3, pp. 167-195, ISSN 1864-3361, URL https://ojs.ub.uni-konstanz.de/srm/article/view/369.
Jean-Claude Deville (1999). Variance estimation for complex statistics and estimators: linearization and residual techniques. Survey Methodology, 25, 193-203, URL https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X19990024882.
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 )
svyqsr( ~eqincome , design = des_eusilc, upper_tot = TRUE, lower_tot = TRUE )
#> qsr SE
#> eqincome 3.97 0.0426
# replicate-weighted design
des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" )
des_eusilc_rep <- convey_prep( des_eusilc_rep )
svyqsr( ~eqincome , design = des_eusilc_rep, upper_tot = TRUE, lower_tot = TRUE )
#> qsr SE
#> eqincome 3.97 0.0476
if (FALSE) {
# linearized design using a variable with missings
svyqsr( ~ db090 , design = des_eusilc )
svyqsr( ~ db090 , design = des_eusilc , na.rm = TRUE )
# replicate-weighted design using a variable with missings
svyqsr( ~ db090 , design = des_eusilc_rep )
svyqsr( ~ db090 , design = des_eusilc_rep , 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 )
svyqsr( ~ eqincome , design = dbd_eusilc )
dbRemoveTable( conn , 'eusilc' )
dbDisconnect( conn , shutdown = TRUE )
}