Estimate the ratio between the median income of people with age above 65 and the median income of people with age below 65.
svyrmir(formula, design, ...)
# S3 method for survey.design
svyrmir(
formula,
design,
age,
agelim = 65,
quantiles = 0.5,
na.rm = FALSE,
med_old = FALSE,
med_young = FALSE,
...
)
# S3 method for svyrep.design
svyrmir(
formula,
design,
age,
agelim = 65,
quantiles = 0.5,
na.rm = FALSE,
med_old = FALSE,
med_young = FALSE,
...
)
# S3 method for DBIsvydesign
svyrmir(formula, design, age, ...)
a formula specifying the income variable
a design object of class survey.design
or class svyrep.design
from the survey
library.
arguments passed on to `survey::oldsvyquantile`
formula defining the variable age
the age cutpoint, the default is 65
income quantile, usually .5 (median)
Should cases with missing values be dropped?
return the median income of people older than agelim
return the median income of people younger than agelim
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 ) )
# missing completely at random, missingness rate = .20
ind_miss <- rbinom(nrow(eusilc), 1, .20 )
eusilc$eqincome_miss <- eusilc$eqincome
is.na(eusilc$eqincome_miss)<- ind_miss==1
# linearized design
des_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 , weights = ~rb050 , data = eusilc )
des_eusilc <- convey_prep(des_eusilc)
svyrmir( ~eqincome , design = des_eusilc , age = ~age, med_old = TRUE )
#> rmir SE
#> eqincome 0.93304 0.0113
# replicate-weighted design
des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" )
des_eusilc_rep <- convey_prep(des_eusilc_rep)
svyrmir( ~eqincome , design = des_eusilc_rep, age= ~age, med_old = TRUE )
#> rmir SE
#> eqincome 0.93304 0.0125
if (FALSE) {
# linearized design using a variable with missings
svyrmir( ~ eqincome_miss , design = des_eusilc,age= ~age)
svyrmir( ~ eqincome_miss , design = des_eusilc , age= ~age, na.rm = TRUE )
# replicate-weighted design using a variable with missings
svyrmir( ~ eqincome_miss , design = des_eusilc_rep,age= ~age )
svyrmir( ~ eqincome_miss , design = des_eusilc_rep ,age= ~age, 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 )
svyrmir( ~eqincome , design = dbd_eusilc , age = ~age )
dbRemoveTable( conn , 'eusilc' )
dbDisconnect( conn , shutdown = TRUE )
}