# svyrmpg: Relative median poverty gap In convey: Income Concentration Analysis with Complex Survey Samples

 svyrmpg R Documentation

## Relative median poverty gap

### Description

Estimate the difference between the at-risk-of-poverty threshold (`arpt`) and the median of incomes less than the `arpt` relative to the `arpt`.

### Usage

```svyrmpg(formula, design, ...)

## S3 method for class 'survey.design'
svyrmpg(
formula,
design,
quantiles = 0.5,
percent = 0.6,
na.rm = FALSE,
thresh = FALSE,
poor_median = FALSE,
...
)

## S3 method for class 'svyrep.design'
svyrmpg(
formula,
design,
quantiles = 0.5,
percent = 0.6,
na.rm = FALSE,
thresh = FALSE,
poor_median = FALSE,
...
)

## S3 method for class 'DBIsvydesign'
svyrmpg(formula, design, ...)
```

### Arguments

 `formula` a formula specifying the income variable `design` a design object of class `survey.design` or class `svyrep.design` from the `survey` library. `...` future expansion `quantiles` income quantile, usually .5 (median) `percent` fraction of the quantile, usually .60 `na.rm` Should cases with missing values be dropped? `thresh` return the poverty poverty threshold `poor_median` return the median income of poor people

### 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.

`svyarpt`

### Examples

```
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 )

svyrmpg( ~eqincome , design = des_eusilc, thresh = TRUE )

# replicate-weighted design
des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" )
des_eusilc_rep <- convey_prep( des_eusilc_rep )

svyrmpg( ~eqincome , design = des_eusilc_rep, thresh = TRUE )

## Not run:

# linearized design using a variable with missings
svyrmpg( ~ py010n , design = des_eusilc )
svyrmpg( ~ py010n , design = des_eusilc , na.rm = TRUE )
# replicate-weighted design using a variable with missings
svyrmpg( ~ py010n , design = des_eusilc_rep )
svyrmpg( ~ py010n , 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 )

svyrmpg( ~ eqincome , design = dbd_eusilc )

dbRemoveTable( conn , 'eusilc' )

dbDisconnect( conn , shutdown = TRUE )

## End(Not run)

```

convey documentation built on April 28, 2022, 1:06 a.m.