svyrmir: Relative median income ratio

In convey: Income Concentration Analysis with Complex Survey Samples

Relative median income ratio

Description

Estimate the ratio between the median income of people with age above 65 and the median income of people with age below 65.

Usage

svyrmir(formula, design, ...)

## S3 method for class 'survey.design'
svyrmir(
  formula,
  design,
  age,
  agelim = 65,
  quantiles = 0.5,
  na.rm = FALSE,
  med_old = FALSE,
  med_young = FALSE,
  ...
)

## S3 method for class 'svyrep.design'
svyrmir(
  formula,
  design,
  age,
  agelim = 65,
  quantiles = 0.5,
  na.rm = FALSE,
  med_old = FALSE,
  med_young = FALSE,
  ...
)

## S3 method for class 'DBIsvydesign'
svyrmir(formula, design, age, ...)

Arguments

formula	a formula specifying the income variable
design	a design object of class survey.design or class svyrep.design from the survey library.
...	arguments passed on to 'survey::oldsvyquantile'
age	formula defining the variable age
agelim	the age cutpoint, the default is 65
quantiles	income quantile, usually .5 (median)
na.rm	Should cases with missing values be dropped?
med_old	return the median income of people older than agelim
med_young	return the median income of people younger than agelim

Details

you must run the convey_prep function on your survey design object immediately after creating it with the svydesign or svrepdesign function.

Value

Object of class "cvystat", which are vectors with a "var" attribute giving the variance and a "statistic" attribute giving the name of the statistic.

Author(s)

Djalma Pessoa and Anthony Damico

References

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.

See Also

svyarpt

Examples

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 )

# 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 )

## Not run: 

# 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 )


## End(Not run)

convey documentation built on Oct. 16, 2024, 5:10 p.m.