| Title: | Income Concentration Analysis with Complex Survey Samples |
|---|---|
| Description: | Variance estimation on indicators of income concentration and poverty using complex sample survey designs. Wrapper around the 'survey' package. |
| Authors: | Djalma Pessoa [aut], Anthony Damico [aut, cre], Guilherme Jacob [aut] |
| Maintainer: | Anthony Damico <[email protected]> |
| License: | GPL-3 |
| Version: | 1.0.1 |
| Built: | 2024-11-16 06:22:15 UTC |
| Source: | https://github.com/ajdamico/convey |
Generalized linearization of a smooth function of survey statistics
contrastinf(exprlist, infunlist)contrastinf(exprlist, infunlist)
exprlist |
a call |
infunlist |
a list of lists, each having two components: value - the estimate value and lin - the linearized variable |
The call must use function that deriv knows how to differentiate. It allows to compute the linearized variable of a complex indicator from the linearized variables of simpler component variables, avoiding the formal derivatives calculations.
a list with two components: values - the estimate value and lin - the linearized variable
Djalma Pessoa, Guilherme Jacob, and Anthony Damico
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.
svyqsr
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) w <- weights(des_eusilc) # ratio linearization T1 = list(value = sum(w*eusilc$eqincome) , lin = eusilc$eqincome ) T2 = list(value = sum(w), lin = rep (1, nrow(eusilc)) ) list_all <- list( T1 = T1, T2 = T2) lin_R = contrastinf (quote(T1/T2), list_all) # estimate of the variable eqincome mean lin_R$value # se estimate of the variable eqincome mean SE(svytotal(lin_R$lin, des_eusilc)) # to check, use svymean (~eqincome, des_eusilc) # quintile share ratio (qsr) linearization S20 <- svyisq(~ eqincome, design = des_eusilc, .20, linearized = TRUE) S20_val <- coef (S20); attributes (S20_val) <- NULL S20_lin <- attr(S20 , "linearized" ) S80 <- svyisq(~ eqincome, design = des_eusilc, .80, linearized = TRUE) S80_val <- coef (S80); attributes (S80_val) <- NULL S80_lin <- attr(S80 , "linearized" ) SU <- list (value = S80_val, lin = S80_lin ) SI <- list (value = S20_val, lin = S20_lin ) TOT <- list(value = sum( w * eusilc$eqincome) , lin = eusilc$eqincome ) list_all <- list (TOT = TOT, SI = SI, SU = SU ) lin_QSR <- contrastinf( quote((TOT-SU)/SI), list_all) # estimate of the qsr lin_QSR$value # se estimate of the qsr: SE(svytotal(lin_QSR$lin, des_eusilc)) # to check, use svyqsr(~eqincome, des_eusilc ) # proportion of income below the quantile .20 list_all <- list (TOT = TOT, SI = SI ) lin_Lor <- contrastinf( quote(SI/TOT), list_all) # estimate of the proportion of income below the quantile .20 lin_Lor$value # se estimate SE(svytotal(lin_Lor$lin,des_eusilc))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) w <- weights(des_eusilc) # ratio linearization T1 = list(value = sum(w*eusilc$eqincome) , lin = eusilc$eqincome ) T2 = list(value = sum(w), lin = rep (1, nrow(eusilc)) ) list_all <- list( T1 = T1, T2 = T2) lin_R = contrastinf (quote(T1/T2), list_all) # estimate of the variable eqincome mean lin_R$value # se estimate of the variable eqincome mean SE(svytotal(lin_R$lin, des_eusilc)) # to check, use svymean (~eqincome, des_eusilc) # quintile share ratio (qsr) linearization S20 <- svyisq(~ eqincome, design = des_eusilc, .20, linearized = TRUE) S20_val <- coef (S20); attributes (S20_val) <- NULL S20_lin <- attr(S20 , "linearized" ) S80 <- svyisq(~ eqincome, design = des_eusilc, .80, linearized = TRUE) S80_val <- coef (S80); attributes (S80_val) <- NULL S80_lin <- attr(S80 , "linearized" ) SU <- list (value = S80_val, lin = S80_lin ) SI <- list (value = S20_val, lin = S20_lin ) TOT <- list(value = sum( w * eusilc$eqincome) , lin = eusilc$eqincome ) list_all <- list (TOT = TOT, SI = SI, SU = SU ) lin_QSR <- contrastinf( quote((TOT-SU)/SI), list_all) # estimate of the qsr lin_QSR$value # se estimate of the qsr: SE(svytotal(lin_QSR$lin, des_eusilc)) # to check, use svyqsr(~eqincome, des_eusilc ) # proportion of income below the quantile .20 list_all <- list (TOT = TOT, SI = SI ) lin_Lor <- contrastinf( quote(SI/TOT), list_all) # estimate of the proportion of income below the quantile .20 lin_Lor$value # se estimate SE(svytotal(lin_Lor$lin,des_eusilc))
sets the population of reference for poverty threshold estimation (needed for convey functions that use a global poverty threshold) within the design. this function generally should be run immediately after the full design object creation with svydesign or svrepdesign
convey_prep(design)convey_prep(design)
design |
a survey design object of the library survey. |
functions in the convey package that use a global poverty threshold require the complete (pre-subsetted) design in order to calculate variances correctly. this function stores the full design object as a separate attribute so that functions from the survey package such as subset and svyby do not disrupt the calculation of error terms.
the same survey object with a full_design attribute as the storage space for the unsubsetted survey design
Djalma Pessoa, Anthony Damico, and Guilherme Jacob
library(survey) library(laeken) data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) ) # linearized design: convey_prep must be run as soon as the linearized design has been created des_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 , weights = ~rb050 , data = eusilc ) des_eusilc <- convey_prep( des_eusilc ) # now this linearized design object is ready for analysis! # # # CORRECT usage example # # # des_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 , weights = ~rb050 , data = eusilc ) des_eusilc <- convey_prep( des_eusilc ) sub_eusilc <- subset( des_eusilc , age > 20 ) # since convey_prep() was run immediately after creating the design # this will calculate the variance accurately SE( svyarpt( ~ eqincome , sub_eusilc ) ) # # # end of CORRECT usage example # # # # # # INCORRECT usage example # # # des_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 , weights = ~rb050 , data = eusilc ) sub_eusilc <- subset( des_eusilc , age > 20 ) sub_eusilc <- convey_prep( sub_eusilc ) # since convey_prep() was not run immediately after creating the design # this will make the variance wrong SE( svyarpt( ~ eqincome , sub_eusilc ) ) # # # end of INCORRECT usage example # # #library(survey) library(laeken) data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) ) # linearized design: convey_prep must be run as soon as the linearized design has been created des_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 , weights = ~rb050 , data = eusilc ) des_eusilc <- convey_prep( des_eusilc ) # now this linearized design object is ready for analysis! # # # CORRECT usage example # # # des_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 , weights = ~rb050 , data = eusilc ) des_eusilc <- convey_prep( des_eusilc ) sub_eusilc <- subset( des_eusilc , age > 20 ) # since convey_prep() was run immediately after creating the design # this will calculate the variance accurately SE( svyarpt( ~ eqincome , sub_eusilc ) ) # # # end of CORRECT usage example # # # # # # INCORRECT usage example # # # des_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 , weights = ~rb050 , data = eusilc ) sub_eusilc <- subset( des_eusilc , age > 20 ) sub_eusilc <- convey_prep( sub_eusilc ) # since convey_prep() was not run immediately after creating the design # this will make the variance wrong SE( svyarpt( ~ eqincome , sub_eusilc ) ) # # # end of INCORRECT usage example # # #
computes the derivative of a function in a point using kernel estimation
densfun(formula, design, x, h = NULL, FUN = "F", na.rm = FALSE, ...)densfun(formula, design, x, h = NULL, FUN = "F", na.rm = FALSE, ...)
formula |
a formula specifying the income variable |
design |
a design object of class |
x |
the point where the derivative is calculated |
h |
value of the bandwidth based on the whole sample |
FUN |
if |
na.rm |
Should cases with missing values be dropped? |
... |
future expansion |
the value of the derivative at x
Djalma Pessoa and Anthony Damico
library(laeken) data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) ) library(survey) des_eusilc <- svydesign(ids = ~rb030, strata =~db040, weights = ~rb050, data = eusilc) des_eusilc <- convey_prep( des_eusilc ) densfun (~eqincome, design=des_eusilc, 10000, FUN="F" ) # linearized design using a variable with missings densfun ( ~ py010n , design = des_eusilc, 10000, FUN="F" ) densfun ( ~ py010n , design = des_eusilc , 10000,FUN="F", na.rm = TRUE )library(laeken) data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) ) library(survey) des_eusilc <- svydesign(ids = ~rb030, strata =~db040, weights = ~rb050, data = eusilc) des_eusilc <- convey_prep( des_eusilc ) densfun (~eqincome, design=des_eusilc, 10000, FUN="F" ) # linearized design using a variable with missings densfun ( ~ py010n , design = des_eusilc, 10000, FUN="F" ) densfun ( ~ py010n , design = des_eusilc , 10000,FUN="F", na.rm = TRUE )
Using the whole sample, computes the bandwith used to get the linearized variable
h_fun(incvar, w)h_fun(incvar, w)
incvar |
income variable used in the estimation of the indicators |
w |
vector of design weights |
value of the bandwidth
Djalma Pessoa, Guilherme Jacob, and Anthony Damico
Computes the linearized variable of the cdf function in a point.
icdf(formula, design, x, na.rm = FALSE, ...)icdf(formula, design, x, na.rm = FALSE, ...)
formula |
a formula specifying the income variable |
design |
a design object of class |
x |
the point where the cdf is calculated |
na.rm |
Should cases with missing values be dropped? |
... |
future expansion |
Object of class "cvystat", which are vectors with a "var" attribute giving the variance and a "statistic" attribute giving the name of the statistic.
Djalma Pessoa and Anthony Damico
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(laeken) data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) ) library(survey) des_eusilc <- svydesign(ids = ~rb030, strata =~db040, weights = ~rb050, data = eusilc) des_eusilc <- convey_prep( des_eusilc ) icdf(~eqincome, design=des_eusilc, 10000 ) # linearized design using a variable with missings icdf( ~ py010n , design = des_eusilc, 10000 ) icdf( ~ py010n , design = des_eusilc , 10000, na.rm = TRUE )library(laeken) data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) ) library(survey) des_eusilc <- svydesign(ids = ~rb030, strata =~db040, weights = ~rb050, data = eusilc) des_eusilc <- convey_prep( des_eusilc ) icdf(~eqincome, design=des_eusilc, 10000 ) # linearized design using a variable with missings icdf( ~ py010n , design = des_eusilc, 10000 ) icdf( ~ py010n , design = des_eusilc , 10000, na.rm = TRUE )
Estimate the proportion of persons with income below the at-risk-of-poverty threshold.
svyarpr(formula, design, ...) ## S3 method for class 'survey.design' svyarpr(formula, design, quantiles = 0.5, percent = 0.6, na.rm = FALSE, ...) ## S3 method for class 'svyrep.design' svyarpr(formula, design, quantiles = 0.5, percent = 0.6, na.rm = FALSE, ...) ## S3 method for class 'DBIsvydesign' svyarpr(formula, design, ...)svyarpr(formula, design, ...) ## S3 method for class 'survey.design' svyarpr(formula, design, quantiles = 0.5, percent = 0.6, na.rm = FALSE, ...) ## S3 method for class 'svyrep.design' svyarpr(formula, design, quantiles = 0.5, percent = 0.6, na.rm = FALSE, ...) ## S3 method for class 'DBIsvydesign' svyarpr(formula, design, ...)
formula |
a formula specifying the income variable |
design |
a design object of class |
... |
arguments passed on to 'svyarpt' |
quantiles |
income quantile, usually .50 (median) |
percent |
fraction of the quantile, usually .60 |
na.rm |
Should cases with missing values be dropped? |
you must run the convey_prep function on your survey design object immediately after creating it with the svydesign or svrepdesign function.
Object of class "cvystat", which are vectors with a "var" attribute giving the variance and a "statistic" attribute giving the name of the statistic.
Djalma Pessoa and Anthony Damico
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 ) svyarpr( ~eqincome , design = des_eusilc ) # replicate-weighted design des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" ) des_eusilc_rep <- convey_prep( des_eusilc_rep ) svyarpr( ~eqincome , design = des_eusilc_rep ) ## Not run: # linearized design using a variable with missings svyarpr( ~ py010n , design = des_eusilc ) svyarpr( ~ py010n , design = des_eusilc , na.rm = TRUE ) # replicate-weighted design using a variable with missings svyarpr( ~ py010n , design = des_eusilc_rep ) svyarpr( ~ 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 ) svyarpr( ~ eqincome , design = dbd_eusilc ) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)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 ) svyarpr( ~eqincome , design = des_eusilc ) # replicate-weighted design des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" ) des_eusilc_rep <- convey_prep( des_eusilc_rep ) svyarpr( ~eqincome , design = des_eusilc_rep ) ## Not run: # linearized design using a variable with missings svyarpr( ~ py010n , design = des_eusilc ) svyarpr( ~ py010n , design = des_eusilc , na.rm = TRUE ) # replicate-weighted design using a variable with missings svyarpr( ~ py010n , design = des_eusilc_rep ) svyarpr( ~ 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 ) svyarpr( ~ eqincome , design = dbd_eusilc ) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)
The standard definition is to use 60% of the median income.
svyarpt(formula, design, ...) ## S3 method for class 'survey.design' svyarpt(formula, design, quantiles = 0.5, percent = 0.6, na.rm = FALSE, ...) ## S3 method for class 'svyrep.design' svyarpt(formula, design, quantiles = 0.5, percent = 0.6, na.rm = FALSE, ...) ## S3 method for class 'DBIsvydesign' svyarpt(formula, design, ...)svyarpt(formula, design, ...) ## S3 method for class 'survey.design' svyarpt(formula, design, quantiles = 0.5, percent = 0.6, na.rm = FALSE, ...) ## S3 method for class 'svyrep.design' svyarpt(formula, design, quantiles = 0.5, percent = 0.6, na.rm = FALSE, ...) ## S3 method for class 'DBIsvydesign' svyarpt(formula, design, ...)
formula |
a formula specifying the income variable |
design |
a design object of class |
... |
arguments passed on to 'survey::oldsvyquantile' |
quantiles |
income quantile quantiles, usually .50 (median) |
percent |
fraction of the quantile, usually .60 |
na.rm |
Should cases with missing values be dropped? |
you must run the convey_prep function on your survey design object immediately after creating it with the svydesign or svrepdesign function.
Object of class "cvystat", which are vectors with a "var" attribute giving the variance and a "statistic" attribute giving the name of the statistic.
Djalma Pessoa and Anthony Damico
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 ) svyarpt( ~eqincome , design = des_eusilc ) # replicate-weighted design des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" ) des_eusilc_rep <- convey_prep( des_eusilc_rep ) svyarpt( ~eqincome , design = des_eusilc_rep ) ## Not run: # linearized design using a variable with missings svyarpt( ~ py010n , design = des_eusilc ) svyarpt( ~ py010n , design = des_eusilc , na.rm = TRUE ) # replicate-weighted design using a variable with missings svyarpt( ~ py010n , design = des_eusilc_rep ) svyarpt( ~ 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 ) svyarpt( ~ eqincome , design = dbd_eusilc ) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)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 ) svyarpt( ~eqincome , design = des_eusilc ) # replicate-weighted design des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" ) des_eusilc_rep <- convey_prep( des_eusilc_rep ) svyarpt( ~eqincome , design = des_eusilc_rep ) ## Not run: # linearized design using a variable with missings svyarpt( ~ py010n , design = des_eusilc ) svyarpt( ~ py010n , design = des_eusilc , na.rm = TRUE ) # replicate-weighted design using a variable with missings svyarpt( ~ py010n , design = des_eusilc_rep ) svyarpt( ~ 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 ) svyarpt( ~ eqincome , design = dbd_eusilc ) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)
Estimate the Atkinson index, an inequality measure
svyatk(formula, design, ...) ## S3 method for class 'survey.design' svyatk( formula, design, epsilon = 1, na.rm = FALSE, deff = FALSE, linearized = FALSE, influence = FALSE, ... ) ## S3 method for class 'svyrep.design' svyatk( formula, design, epsilon = 1, na.rm = FALSE, deff = FALSE, linearized = FALSE, return.replicates = FALSE, ... ) ## S3 method for class 'DBIsvydesign' svyatk(formula, design, ...)svyatk(formula, design, ...) ## S3 method for class 'survey.design' svyatk( formula, design, epsilon = 1, na.rm = FALSE, deff = FALSE, linearized = FALSE, influence = FALSE, ... ) ## S3 method for class 'svyrep.design' svyatk( formula, design, epsilon = 1, na.rm = FALSE, deff = FALSE, linearized = FALSE, return.replicates = FALSE, ... ) ## S3 method for class 'DBIsvydesign' svyatk(formula, design, ...)
formula |
a formula specifying the income variable |
design |
a design object of class |
... |
future expansion |
epsilon |
a parameter that determines the sensivity towards inequality in the bottom of the distribution. Defaults to |
na.rm |
Should cases with missing values be dropped? |
deff |
Return the design effect (see |
linearized |
Should a matrix of linearized variables be returned |
influence |
Should a matrix of (weighted) influence functions be returned? (for compatibility with |
return.replicates |
Return the replicate estimates? |
you must run the convey_prep function on your survey design object immediately after creating it with the svydesign or svrepdesign function.
Object of class "cvystat", which are vectors with a "var" attribute giving the variance and a "statistic" attribute giving the name of the statistic.
Guilherme Jacob, Djalma Pessoa and Anthony Damico
Matti Langel (2012). Measuring inequality in finite population sampling. PhD thesis: Universite de Neuchatel, URL https://doc.rero.ch/record/29204/files/00002252.pdf.
Martin Biewen and Stephen Jenkins (2002). Estimation of Generalized Entropy and Atkinson Inequality Indices from Complex Survey Data. DIW Discussion Papers, No.345, URL https://www.diw.de/documents/publikationen/73/diw_01.c.40394.de/dp345.pdf.
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 ) ## Not run: # 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 ) ## End(Not run)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 ) ## Not run: # 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 ) ## End(Not run)
Estimate the FGT measure.
svyfgt(formula, design, ...) ## S3 method for class 'survey.design' svyfgt( formula, design, g, type_thresh = "abs", abs_thresh = NULL, percent = 0.6, quantiles = 0.5, na.rm = FALSE, thresh = FALSE, deff = FALSE, linearized = FALSE, influence = FALSE, ... ) ## S3 method for class 'svyrep.design' svyfgt( formula, design, g, type_thresh = "abs", abs_thresh = NULL, percent = 0.6, quantiles = 0.5, na.rm = FALSE, thresh = FALSE, deff = FALSE, linearized = FALSE, return.replicates = FALSE, ... ) ## S3 method for class 'DBIsvydesign' svyfgt(formula, design, ...)svyfgt(formula, design, ...) ## S3 method for class 'survey.design' svyfgt( formula, design, g, type_thresh = "abs", abs_thresh = NULL, percent = 0.6, quantiles = 0.5, na.rm = FALSE, thresh = FALSE, deff = FALSE, linearized = FALSE, influence = FALSE, ... ) ## S3 method for class 'svyrep.design' svyfgt( formula, design, g, type_thresh = "abs", abs_thresh = NULL, percent = 0.6, quantiles = 0.5, na.rm = FALSE, thresh = FALSE, deff = FALSE, linearized = FALSE, return.replicates = FALSE, ... ) ## S3 method for class 'DBIsvydesign' svyfgt(formula, design, ...)
formula |
a formula specifying the income variable |
design |
a design object of class |
... |
passed to |
g |
If g=0 estimates the headcount ratio; If g=1 estimates the average normalised poverty gap, and if g=2 estimates the average squared normalised poverty gap |
type_thresh |
type of poverty threshold. If "abs" the threshold is fixed and given the value of abs_thresh; if "relq" it is given by percent times the quantile; if "relm" it is percent times the mean. |
abs_thresh |
poverty threshold value if type_thresh is "abs" |
percent |
the multiple of the the quantile or mean used in the poverty threshold definition |
quantiles |
the quantile used used in the poverty threshold definition |
na.rm |
Should cases with missing values be dropped? |
thresh |
return the poverty threshold value |
deff |
Return the design effect (see |
linearized |
Should a matrix of linearized variables be returned? |
influence |
Should a matrix of (weighted) influence functions be returned? (for compatibility with |
return.replicates |
Return the replicate estimates? |
you must run the convey_prep function on your survey design object immediately after creating it with the svydesign or svrepdesign function.
The FGT poverty measures have three special cases.
When g = 0, the FGT measure is the headcount poverty rate, assigning the same "poverty-weight" to all persons below the poverty line.
When g = 1, it becomes the poverty gap ratio, a measure which accounts for the intensity of income shortfall among the poor.
When g = 2. it becomes the squared poverty gap ratio, a measure that also accounts for inequality of poverty intesity across the poor.
The g is a poverty sensitivity parameter, adding more weight to people with greater income shortfalls as it increases.
Object of class "cvystat", which are vectors with a "var" attribute giving the variance and a "statistic" attribute giving the name of the statistic.
Djalma Pessoa, Anthony Damico, and Guilherme Jacob
James Foster, Joel Greer and Erik Thorbecke (1984). A class of decomposable poverty measures. Econometrica, Vol.52, No.3, pp. 761-766.
Y.G. Berger and C. J. Skinner (2003), Variance estimation for a low income proportion. Journal of the Royal Statistical Society: Series C (Applied Statistics), Vol. 52, No. 4, pp. 457-468. DOI doi:10.1111/1467-9876.00417
Buhong Zheng (2001). Statistical inference for poverty measures with relative poverty lines. Journal of Econometrics, Vol. 101, pp. 337-356.
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 ) # replicate-weighted design des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" ) des_eusilc_rep <- convey_prep( des_eusilc_rep ) # headcount ratio, poverty threshold fixed svyfgt(~eqincome, des_eusilc, g=0, abs_thresh=10000) # poverty gap index, poverty threshold fixed svyfgt(~eqincome, des_eusilc, g=1, abs_thresh=10000) # headcount ratio, poverty threshold equal to arpt svyfgt(~eqincome, des_eusilc, g=0, type_thresh= "relq" , thresh = TRUE) # poverty gap index, poverty threshold equal to arpt svyfgt(~eqincome, des_eusilc, g=1, type_thresh= "relq", thresh = TRUE) # headcount ratio, poverty threshold equal to .6 times the mean svyfgt(~eqincome, des_eusilc, g=0, type_thresh= "relm", thresh = TRUE) # poverty gap index, poverty threshold equal to 0.6 times the mean svyfgt(~eqincome, des_eusilc, g=1, type_thresh= "relm" , thresh = TRUE) # using svrep.design: # headcount ratio, poverty threshold fixed svyfgt(~eqincome, des_eusilc_rep, g=0, abs_thresh=10000) # poverty gap index, poverty threshold fixed svyfgt(~eqincome, des_eusilc, g=1, abs_thresh=10000) # headcount ratio, poverty threshold equal to arpt svyfgt(~eqincome, des_eusilc_rep, g=0, type_thresh= "relq" , thresh = TRUE) # poverty gap index, poverty threshold equal to arpt svyfgt(~eqincome, des_eusilc, g=1, type_thresh= "relq", thresh = TRUE) # headcount ratio, poverty threshold equal to .6 times the mean svyfgt(~eqincome, des_eusilc_rep, g=0, type_thresh= "relm" , thresh = TRUE) # poverty gap index, poverty threshold equal to 0.6 times the mean svyfgt(~eqincome, des_eusilc_rep, g=1, type_thresh= "relm", thresh = TRUE) ## Not run: # 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 ) # headcount ratio, poverty threshold fixed svyfgt(~eqincome, dbd_eusilc, g=0, abs_thresh=10000) # poverty gap index, poverty threshold fixed svyfgt(~eqincome, dbd_eusilc, g=1, abs_thresh=10000) # headcount ratio, poverty threshold equal to arpt svyfgt(~eqincome, dbd_eusilc, g=0, type_thresh= "relq", thresh = TRUE) # poverty gap index, poverty threshold equal to arpt svyfgt(~eqincome, dbd_eusilc, g=1, type_thresh= "relq") # headcount ratio, poverty threshold equal to .6 times the mean svyfgt(~eqincome, dbd_eusilc, g=0, type_thresh= "relm") # poverty gap index, poverty threshold equal to 0.6 times the mean svyfgt(~eqincome, dbd_eusilc, g=1, type_thresh= "relm") dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)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 ) # headcount ratio, poverty threshold fixed svyfgt(~eqincome, des_eusilc, g=0, abs_thresh=10000) # poverty gap index, poverty threshold fixed svyfgt(~eqincome, des_eusilc, g=1, abs_thresh=10000) # headcount ratio, poverty threshold equal to arpt svyfgt(~eqincome, des_eusilc, g=0, type_thresh= "relq" , thresh = TRUE) # poverty gap index, poverty threshold equal to arpt svyfgt(~eqincome, des_eusilc, g=1, type_thresh= "relq", thresh = TRUE) # headcount ratio, poverty threshold equal to .6 times the mean svyfgt(~eqincome, des_eusilc, g=0, type_thresh= "relm", thresh = TRUE) # poverty gap index, poverty threshold equal to 0.6 times the mean svyfgt(~eqincome, des_eusilc, g=1, type_thresh= "relm" , thresh = TRUE) # using svrep.design: # headcount ratio, poverty threshold fixed svyfgt(~eqincome, des_eusilc_rep, g=0, abs_thresh=10000) # poverty gap index, poverty threshold fixed svyfgt(~eqincome, des_eusilc, g=1, abs_thresh=10000) # headcount ratio, poverty threshold equal to arpt svyfgt(~eqincome, des_eusilc_rep, g=0, type_thresh= "relq" , thresh = TRUE) # poverty gap index, poverty threshold equal to arpt svyfgt(~eqincome, des_eusilc, g=1, type_thresh= "relq", thresh = TRUE) # headcount ratio, poverty threshold equal to .6 times the mean svyfgt(~eqincome, des_eusilc_rep, g=0, type_thresh= "relm" , thresh = TRUE) # poverty gap index, poverty threshold equal to 0.6 times the mean svyfgt(~eqincome, des_eusilc_rep, g=1, type_thresh= "relm", thresh = TRUE) ## Not run: # 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 ) # headcount ratio, poverty threshold fixed svyfgt(~eqincome, dbd_eusilc, g=0, abs_thresh=10000) # poverty gap index, poverty threshold fixed svyfgt(~eqincome, dbd_eusilc, g=1, abs_thresh=10000) # headcount ratio, poverty threshold equal to arpt svyfgt(~eqincome, dbd_eusilc, g=0, type_thresh= "relq", thresh = TRUE) # poverty gap index, poverty threshold equal to arpt svyfgt(~eqincome, dbd_eusilc, g=1, type_thresh= "relq") # headcount ratio, poverty threshold equal to .6 times the mean svyfgt(~eqincome, dbd_eusilc, g=0, type_thresh= "relm") # poverty gap index, poverty threshold equal to 0.6 times the mean svyfgt(~eqincome, dbd_eusilc, g=1, type_thresh= "relm") dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)
Estimate the Foster et al. (1984) poverty class and its components
svyfgtdec(formula, design, ...) ## S3 method for class 'survey.design' svyfgtdec( formula, design, g, type_thresh = "abs", abs_thresh = NULL, percent = 0.6, quantiles = 0.5, na.rm = FALSE, thresh = FALSE, ... ) ## S3 method for class 'svyrep.design' svyfgtdec( formula, design, g, type_thresh = "abs", abs_thresh = NULL, percent = 0.6, quantiles = 0.5, na.rm = FALSE, thresh = FALSE, return.replicates = FALSE, ... ) ## S3 method for class 'DBIsvydesign' svyfgtdec(formula, design, ...)svyfgtdec(formula, design, ...) ## S3 method for class 'survey.design' svyfgtdec( formula, design, g, type_thresh = "abs", abs_thresh = NULL, percent = 0.6, quantiles = 0.5, na.rm = FALSE, thresh = FALSE, ... ) ## S3 method for class 'svyrep.design' svyfgtdec( formula, design, g, type_thresh = "abs", abs_thresh = NULL, percent = 0.6, quantiles = 0.5, na.rm = FALSE, thresh = FALSE, return.replicates = FALSE, ... ) ## S3 method for class 'DBIsvydesign' svyfgtdec(formula, design, ...)
formula |
a formula specifying the income variable |
design |
a design object of class |
... |
additional arguments. Currently not used. |
g |
If g=2 estimates the average squared normalised poverty gap. This function is defined for g >= 2 only, |
type_thresh |
type of poverty threshold. If "abs" the threshold is fixed and given the value of abs_thresh; if "relq" it is given by percent times the quantile; if "relm" it is percent times the mean. |
abs_thresh |
poverty threshold value if type_thresh is "abs" |
percent |
the multiple of the the quantile or mean used in the poverty threshold definition |
quantiles |
the quantile used used in the poverty threshold definition |
na.rm |
Should cases with missing values be dropped? |
thresh |
return the poverty threshold value |
return.replicates |
Return the replicate estimates? |
you must run the convey_prep function on your survey design object immediately after creating it with the svydesign or svrepdesign function.
Object of class "cvydstat", with estimates for the FGT(g), FGT(0), FGT(1), income gap ratio and GEI(income gaps; epsilon = g) with a "var" attribute giving the variance-covariance matrix.
A "statistic" attribute giving the name of the statistic.
Guilherme Jacob, Djalma Pessoa and Anthony Damico
Oihana Aristondo, Cassilda Lasso De La vega and Ana Urrutia (2010). A new multiplicative decomposition for the Foster-Greer-Thorbecke poverty indices. Bulletin of Economic Research, Vol.62, No.3, pp. 259-267. University of Wisconsin. <doi:10.1111/j.1467-8586.2009.00320.x>
James Foster, Joel Greer and Erik Thorbecke (1984). A class of decomposable poverty measures. Econometrica, Vol.52, No.3, pp. 761-766.
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 ) # replicate-weighted design des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" ) des_eusilc_rep <- convey_prep( des_eusilc_rep ) # absolute poverty threshold svyfgtdec(~eqincome, des_eusilc, g=2, abs_thresh=10000) # poverty threshold equal to arpt svyfgtdec(~eqincome, des_eusilc, g=2, type_thresh= "relq" , thresh = TRUE) # poverty threshold equal to 0.6 times the mean svyfgtdec(~eqincome, des_eusilc, g=2, type_thresh= "relm" , thresh = TRUE) # using svrep.design: # absolute poverty threshold svyfgtdec(~eqincome, des_eusilc_rep, g=2, abs_thresh=10000) # poverty threshold equal to arpt svyfgtdec(~eqincome, des_eusilc_rep, g=2, type_thresh= "relq" , thresh = TRUE) # poverty threshold equal to 0.6 times the mean svyfgtdec(~eqincome, des_eusilc_rep, g=2, type_thresh= "relm" , thresh = TRUE) ## Not run: # 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 ) # absolute poverty threshold svyfgtdec(~eqincome, dbd_eusilc, g=2, abs_thresh=10000) # poverty threshold equal to arpt svyfgtdec(~eqincome, dbd_eusilc, g=2, type_thresh= "relq" , thresh = TRUE) # poverty threshold equal to 0.6 times the mean svyfgtdec(~eqincome, dbd_eusilc, g=2, type_thresh= "relm" , thresh = TRUE) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)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 ) # absolute poverty threshold svyfgtdec(~eqincome, des_eusilc, g=2, abs_thresh=10000) # poverty threshold equal to arpt svyfgtdec(~eqincome, des_eusilc, g=2, type_thresh= "relq" , thresh = TRUE) # poverty threshold equal to 0.6 times the mean svyfgtdec(~eqincome, des_eusilc, g=2, type_thresh= "relm" , thresh = TRUE) # using svrep.design: # absolute poverty threshold svyfgtdec(~eqincome, des_eusilc_rep, g=2, abs_thresh=10000) # poverty threshold equal to arpt svyfgtdec(~eqincome, des_eusilc_rep, g=2, type_thresh= "relq" , thresh = TRUE) # poverty threshold equal to 0.6 times the mean svyfgtdec(~eqincome, des_eusilc_rep, g=2, type_thresh= "relm" , thresh = TRUE) ## Not run: # 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 ) # absolute poverty threshold svyfgtdec(~eqincome, dbd_eusilc, g=2, abs_thresh=10000) # poverty threshold equal to arpt svyfgtdec(~eqincome, dbd_eusilc, g=2, type_thresh= "relq" , thresh = TRUE) # poverty threshold equal to 0.6 times the mean svyfgtdec(~eqincome, dbd_eusilc, g=2, type_thresh= "relm" , thresh = TRUE) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)
Estimate the generalized entropy index, a measure of inequality
svygei(formula, design, ...) ## S3 method for class 'survey.design' svygei( formula, design, epsilon = 1, na.rm = FALSE, deff = FALSE, linearized = FALSE, influence = FALSE, ... ) ## S3 method for class 'svyrep.design' svygei( formula, design, epsilon = 1, na.rm = FALSE, deff = FALSE, linearized = FALSE, return.replicates = FALSE, ... ) ## S3 method for class 'DBIsvydesign' svygei(formula, design, ...)svygei(formula, design, ...) ## S3 method for class 'survey.design' svygei( formula, design, epsilon = 1, na.rm = FALSE, deff = FALSE, linearized = FALSE, influence = FALSE, ... ) ## S3 method for class 'svyrep.design' svygei( formula, design, epsilon = 1, na.rm = FALSE, deff = FALSE, linearized = FALSE, return.replicates = FALSE, ... ) ## S3 method for class 'DBIsvydesign' svygei(formula, design, ...)
formula |
a formula specifying the income variable |
design |
a design object of class |
... |
future expansion |
epsilon |
a parameter that determines the sensivity towards inequality in the top of the distribution. Defaults to epsilon = 1. |
na.rm |
Should cases with missing values be dropped? |
deff |
Return the design effect (see |
linearized |
Should a matrix of linearized variables be returned |
influence |
Should a matrix of (weighted) influence functions be returned? (for compatibility with |
return.replicates |
Return the replicate estimates? |
you must run the convey_prep function on your survey design object immediately after creating it with the svydesign or svrepdesign function.
This measure only allows for strictly positive variables.
Object of class "cvystat", which are vectors with a "var" attribute giving the variance and a "statistic" attribute giving the name of the statistic.
Guilherme Jacob, Djalma Pessoa and Anthony Damico
Matti Langel (2012). Measuring inequality in finite population sampling. PhD thesis: Universite de Neuchatel, URL https://doc.rero.ch/record/29204/files/00002252.pdf.
Martin Biewen and Stephen Jenkins (2002). Estimation of Generalized Entropy and Atkinson Inequality Indices from Complex Survey Data. DIW Discussion Papers, No.345, URL https://www.diw.de/documents/publikationen/73/diw_01.c.40394.de/dp345.pdf.
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) # linearized design svygei( ~eqincome , subset(des_eusilc, eqincome > 0), epsilon = 0 ) svygei( ~eqincome , subset(des_eusilc, eqincome > 0), epsilon = .5 ) svygei( ~eqincome , subset(des_eusilc, eqincome > 0), epsilon = 1 ) svygei( ~eqincome , subset(des_eusilc, eqincome > 0), epsilon = 2 ) # replicate-weighted design svygei( ~eqincome , subset(des_eusilc_rep, eqincome > 0), epsilon = 0 ) svygei( ~eqincome , subset(des_eusilc_rep, eqincome > 0), epsilon = .5 ) svygei( ~eqincome , subset(des_eusilc_rep, eqincome > 0), epsilon = 1 ) svygei( ~eqincome , subset(des_eusilc_rep, eqincome > 0), epsilon = 2 ) ## Not run: # linearized design using a variable with missings svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 0 ) svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 0, na.rm = TRUE ) svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = .5 ) svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = .5, na.rm = TRUE ) svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 1 ) svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 1, na.rm = TRUE ) svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 2 ) svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 2, na.rm = TRUE ) # replicate-weighted design using a variable with missings svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = 0 ) svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = 0, na.rm = TRUE ) svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = .5 ) svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = .5, na.rm = TRUE ) svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = 1 ) svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = 1, na.rm = TRUE ) svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = 2 ) svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = 2, 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 ) # database-backed linearized design svygei( ~eqincome , subset(dbd_eusilc, eqincome > 0), epsilon = 0 ) svygei( ~eqincome , dbd_eusilc, epsilon = .5 ) svygei( ~eqincome , subset(dbd_eusilc, eqincome > 0), epsilon = 1 ) svygei( ~eqincome , dbd_eusilc, epsilon = 2 ) # database-backed linearized design using a variable with missings svygei( ~py010n , subset(dbd_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 0 ) svygei( ~py010n , subset(dbd_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 0, na.rm = TRUE ) svygei( ~py010n , dbd_eusilc, epsilon = .5 ) svygei( ~py010n , dbd_eusilc, epsilon = .5, na.rm = TRUE ) svygei( ~py010n , subset(dbd_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 1 ) svygei( ~py010n , subset(dbd_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 1, na.rm = TRUE ) svygei( ~py010n , dbd_eusilc, epsilon = 2 ) svygei( ~py010n , dbd_eusilc, epsilon = 2, na.rm = TRUE ) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)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) # linearized design svygei( ~eqincome , subset(des_eusilc, eqincome > 0), epsilon = 0 ) svygei( ~eqincome , subset(des_eusilc, eqincome > 0), epsilon = .5 ) svygei( ~eqincome , subset(des_eusilc, eqincome > 0), epsilon = 1 ) svygei( ~eqincome , subset(des_eusilc, eqincome > 0), epsilon = 2 ) # replicate-weighted design svygei( ~eqincome , subset(des_eusilc_rep, eqincome > 0), epsilon = 0 ) svygei( ~eqincome , subset(des_eusilc_rep, eqincome > 0), epsilon = .5 ) svygei( ~eqincome , subset(des_eusilc_rep, eqincome > 0), epsilon = 1 ) svygei( ~eqincome , subset(des_eusilc_rep, eqincome > 0), epsilon = 2 ) ## Not run: # linearized design using a variable with missings svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 0 ) svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 0, na.rm = TRUE ) svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = .5 ) svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = .5, na.rm = TRUE ) svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 1 ) svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 1, na.rm = TRUE ) svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 2 ) svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 2, na.rm = TRUE ) # replicate-weighted design using a variable with missings svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = 0 ) svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = 0, na.rm = TRUE ) svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = .5 ) svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = .5, na.rm = TRUE ) svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = 1 ) svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = 1, na.rm = TRUE ) svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = 2 ) svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = 2, 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 ) # database-backed linearized design svygei( ~eqincome , subset(dbd_eusilc, eqincome > 0), epsilon = 0 ) svygei( ~eqincome , dbd_eusilc, epsilon = .5 ) svygei( ~eqincome , subset(dbd_eusilc, eqincome > 0), epsilon = 1 ) svygei( ~eqincome , dbd_eusilc, epsilon = 2 ) # database-backed linearized design using a variable with missings svygei( ~py010n , subset(dbd_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 0 ) svygei( ~py010n , subset(dbd_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 0, na.rm = TRUE ) svygei( ~py010n , dbd_eusilc, epsilon = .5 ) svygei( ~py010n , dbd_eusilc, epsilon = .5, na.rm = TRUE ) svygei( ~py010n , subset(dbd_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 1 ) svygei( ~py010n , subset(dbd_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 1, na.rm = TRUE ) svygei( ~py010n , dbd_eusilc, epsilon = 2 ) svygei( ~py010n , dbd_eusilc, epsilon = 2, na.rm = TRUE ) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)
Estimates the group decomposition of the generalized entropy index
svygeidec(formula, subgroup, design, ...) ## S3 method for class 'survey.design' svygeidec( formula, subgroup, design, epsilon = 1, na.rm = FALSE, deff = FALSE, linearized = FALSE, influence = FALSE, ... ) ## S3 method for class 'svyrep.design' svygeidec( formula, subgroup, design, epsilon = 1, na.rm = FALSE, deff = FALSE, linearized = FALSE, return.replicates = FALSE, ... ) ## S3 method for class 'DBIsvydesign' svygeidec(formula, subgroup, design, ...)svygeidec(formula, subgroup, design, ...) ## S3 method for class 'survey.design' svygeidec( formula, subgroup, design, epsilon = 1, na.rm = FALSE, deff = FALSE, linearized = FALSE, influence = FALSE, ... ) ## S3 method for class 'svyrep.design' svygeidec( formula, subgroup, design, epsilon = 1, na.rm = FALSE, deff = FALSE, linearized = FALSE, return.replicates = FALSE, ... ) ## S3 method for class 'DBIsvydesign' svygeidec(formula, subgroup, design, ...)
formula |
a formula specifying the income variable |
subgroup |
a formula specifying the group variable |
design |
a design object of class |
... |
future expansion |
epsilon |
a parameter that determines the sensivity towards inequality in the top of the distribution. Defaults to epsilon = 1. |
na.rm |
Should cases with missing values be dropped? Observations containing missing values in income or group variables will be dropped. |
deff |
Return the design effect (see |
linearized |
Should a matrix of linearized variables be returned |
influence |
Should a matrix of (weighted) influence functions be returned? (for compatibility with |
return.replicates |
Return the replicate estimates? |
you must run the convey_prep function on your survey design object immediately after creating it with the svydesign or svrepdesign function.
This measure only allows for strictly positive variables.
Object of class "cvydstat", which are vectors with a "var" attribute giving the variance-covariance matrix and a "statistic" attribute giving the name of the statistic.
Guilherme Jacob, Djalma Pessoa and Anthony Damico
Anthony F. Shorrocks (1984). Inequality decomposition groups population subgroups. Econometrica, v. 52, n. 6, 1984, pp. 1369-1385. DOI doi:10.2307/1913511.
Martin Biewen and Stephen Jenkins (2002). Estimation of Generalized Entropy and Atkinson Inequality Indices from Complex Survey Data. DIW Discussion Papers, No.345, URL https://www.diw.de/documents/publikationen/73/diw_01.c.40394.de/dp345.pdf.
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) # linearized design svygeidec( ~eqincome , ~rb090 , subset( des_eusilc, eqincome > 0 ) , epsilon = 0 ) svygeidec( ~eqincome , ~rb090 , subset( des_eusilc, eqincome > 0 ) , epsilon = .5 ) svygeidec( ~eqincome , ~rb090 , subset( des_eusilc, eqincome > 0 ) , epsilon = 1 ) svygeidec( ~eqincome , ~rb090 , subset( des_eusilc, eqincome > 0 ) , epsilon = 2 ) # replicate-weighted design svygeidec( ~eqincome , ~rb090 , subset( des_eusilc_rep, eqincome > 0 ) , epsilon = 0 ) svygeidec( ~eqincome , ~rb090 , subset( des_eusilc_rep, eqincome > 0 ) , epsilon = .5 ) svygeidec( ~eqincome , ~rb090 , subset( des_eusilc_rep, eqincome > 0 ) , epsilon = 1 ) svygeidec( ~eqincome , ~rb090 , subset( des_eusilc_rep, eqincome > 0 ) , epsilon = 2 ) ## Not run: # linearized design using a variable with missings sub_des_eusilc <- subset(des_eusilc, py010n > 0 | is.na(py010n) ) svygeidec( ~py010n , ~rb090 , sub_des_eusilc , epsilon = 0 ) svygeidec( ~py010n , ~rb090 , sub_des_eusilc , epsilon = 0, na.rm = TRUE ) svygeidec( ~py010n , ~rb090 , sub_des_eusilc , epsilon = 1 ) svygeidec( ~py010n , ~rb090 , sub_des_eusilc , epsilon = 1, na.rm = TRUE ) # replicate-weighted design using a variable with missings sub_des_eusilc_rep <- subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ) svygeidec( ~py010n , ~rb090 , sub_des_eusilc_rep , epsilon = 0 ) svygeidec( ~py010n , ~rb090 , sub_des_eusilc_rep , epsilon = 0, na.rm = TRUE ) svygeidec( ~py010n , ~rb090 , sub_des_eusilc_rep , epsilon = 1 ) svygeidec( ~py010n , ~rb090 , sub_des_eusilc_rep , epsilon = 1, 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 ) # database-backed linearized design svygeidec( ~eqincome , ~rb090 , subset(dbd_eusilc, eqincome > 0) , epsilon = 0 ) svygeidec( ~eqincome , ~rb090 , subset(dbd_eusilc, eqincome > 0) , epsilon = .5 ) svygeidec( ~eqincome , ~rb090 , subset(dbd_eusilc, eqincome > 0) , epsilon = 1 ) svygeidec( ~eqincome , ~rb090 , subset(dbd_eusilc, eqincome > 0) , epsilon = 2 ) # database-backed linearized design using a variable with missings sub_dbd_eusilc <- subset(dbd_eusilc, py010n > 0 | is.na(py010n) ) svygeidec( ~py010n , ~rb090 , sub_dbd_eusilc , epsilon = 0 ) svygeidec( ~py010n , ~rb090 , sub_dbd_eusilc , epsilon = 0, na.rm = TRUE ) svygeidec( ~py010n , ~rb090 , sub_dbd_eusilc , epsilon = .5 ) svygeidec( ~py010n , ~rb090 , sub_dbd_eusilc , epsilon = .5, na.rm = TRUE ) svygeidec( ~py010n , ~rb090 , sub_dbd_eusilc , epsilon = 1 ) svygeidec( ~py010n , ~rb090 , sub_dbd_eusilc , epsilon = 1, na.rm = TRUE ) svygeidec( ~py010n , ~rb090 , sub_dbd_eusilc , epsilon = 2 ) svygeidec( ~py010n , ~rb090 , sub_dbd_eusilc , epsilon = 2, na.rm = TRUE ) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)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) # linearized design svygeidec( ~eqincome , ~rb090 , subset( des_eusilc, eqincome > 0 ) , epsilon = 0 ) svygeidec( ~eqincome , ~rb090 , subset( des_eusilc, eqincome > 0 ) , epsilon = .5 ) svygeidec( ~eqincome , ~rb090 , subset( des_eusilc, eqincome > 0 ) , epsilon = 1 ) svygeidec( ~eqincome , ~rb090 , subset( des_eusilc, eqincome > 0 ) , epsilon = 2 ) # replicate-weighted design svygeidec( ~eqincome , ~rb090 , subset( des_eusilc_rep, eqincome > 0 ) , epsilon = 0 ) svygeidec( ~eqincome , ~rb090 , subset( des_eusilc_rep, eqincome > 0 ) , epsilon = .5 ) svygeidec( ~eqincome , ~rb090 , subset( des_eusilc_rep, eqincome > 0 ) , epsilon = 1 ) svygeidec( ~eqincome , ~rb090 , subset( des_eusilc_rep, eqincome > 0 ) , epsilon = 2 ) ## Not run: # linearized design using a variable with missings sub_des_eusilc <- subset(des_eusilc, py010n > 0 | is.na(py010n) ) svygeidec( ~py010n , ~rb090 , sub_des_eusilc , epsilon = 0 ) svygeidec( ~py010n , ~rb090 , sub_des_eusilc , epsilon = 0, na.rm = TRUE ) svygeidec( ~py010n , ~rb090 , sub_des_eusilc , epsilon = 1 ) svygeidec( ~py010n , ~rb090 , sub_des_eusilc , epsilon = 1, na.rm = TRUE ) # replicate-weighted design using a variable with missings sub_des_eusilc_rep <- subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ) svygeidec( ~py010n , ~rb090 , sub_des_eusilc_rep , epsilon = 0 ) svygeidec( ~py010n , ~rb090 , sub_des_eusilc_rep , epsilon = 0, na.rm = TRUE ) svygeidec( ~py010n , ~rb090 , sub_des_eusilc_rep , epsilon = 1 ) svygeidec( ~py010n , ~rb090 , sub_des_eusilc_rep , epsilon = 1, 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 ) # database-backed linearized design svygeidec( ~eqincome , ~rb090 , subset(dbd_eusilc, eqincome > 0) , epsilon = 0 ) svygeidec( ~eqincome , ~rb090 , subset(dbd_eusilc, eqincome > 0) , epsilon = .5 ) svygeidec( ~eqincome , ~rb090 , subset(dbd_eusilc, eqincome > 0) , epsilon = 1 ) svygeidec( ~eqincome , ~rb090 , subset(dbd_eusilc, eqincome > 0) , epsilon = 2 ) # database-backed linearized design using a variable with missings sub_dbd_eusilc <- subset(dbd_eusilc, py010n > 0 | is.na(py010n) ) svygeidec( ~py010n , ~rb090 , sub_dbd_eusilc , epsilon = 0 ) svygeidec( ~py010n , ~rb090 , sub_dbd_eusilc , epsilon = 0, na.rm = TRUE ) svygeidec( ~py010n , ~rb090 , sub_dbd_eusilc , epsilon = .5 ) svygeidec( ~py010n , ~rb090 , sub_dbd_eusilc , epsilon = .5, na.rm = TRUE ) svygeidec( ~py010n , ~rb090 , sub_dbd_eusilc , epsilon = 1 ) svygeidec( ~py010n , ~rb090 , sub_dbd_eusilc , epsilon = 1, na.rm = TRUE ) svygeidec( ~py010n , ~rb090 , sub_dbd_eusilc , epsilon = 2 ) svygeidec( ~py010n , ~rb090 , sub_dbd_eusilc , epsilon = 2, na.rm = TRUE ) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)
Estimate the Gini coefficient, an inequality measure
svygini(formula, design, ...) ## S3 method for class 'survey.design' svygini( formula, design, na.rm = FALSE, deff = FALSE, linearized = FALSE, influence = FALSE, ... ) ## S3 method for class 'svyrep.design' svygini( formula, design, na.rm = FALSE, deff = FALSE, linearized = FALSE, return.replicates = FALSE, ... ) ## S3 method for class 'DBIsvydesign' svygini(formula, design, ...)svygini(formula, design, ...) ## S3 method for class 'survey.design' svygini( formula, design, na.rm = FALSE, deff = FALSE, linearized = FALSE, influence = FALSE, ... ) ## S3 method for class 'svyrep.design' svygini( formula, design, na.rm = FALSE, deff = FALSE, linearized = FALSE, return.replicates = FALSE, ... ) ## S3 method for class 'DBIsvydesign' svygini(formula, design, ...)
formula |
a formula specifying the income variable |
design |
a design object of class |
... |
future expansion |
na.rm |
Should cases with missing values be dropped? |
deff |
Return the design effect (see |
linearized |
Should a matrix of linearized variables be returned |
influence |
Should a matrix of (weighted) influence functions be returned? (for compatibility with |
return.replicates |
Return the replicate estimates? |
you must run the convey_prep function on your survey design object immediately after creating it with the svydesign or svrepdesign function.
Object of class "cvystat", which are vectors with a "var" attribute giving the variance and a "statistic" attribute giving the name of the statistic.
Djalma Pessoa, Guilherme Jacob, and Anthony Damico
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) svygini( ~eqincome , design = des_eusilc ) # replicate-weighted design des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" ) des_eusilc_rep <- convey_prep(des_eusilc_rep) svygini( ~eqincome , design = des_eusilc_rep ) ## Not run: # linearized design using a variable with missings svygini( ~ py010n , design = des_eusilc ) svygini( ~ py010n , design = des_eusilc , na.rm = TRUE ) # replicate-weighted design using a variable with missings svygini( ~ py010n , design = des_eusilc_rep ) svygini( ~ 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 ) svygini( ~ eqincome , design = dbd_eusilc ) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)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) svygini( ~eqincome , design = des_eusilc ) # replicate-weighted design des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" ) des_eusilc_rep <- convey_prep(des_eusilc_rep) svygini( ~eqincome , design = des_eusilc_rep ) ## Not run: # linearized design using a variable with missings svygini( ~ py010n , design = des_eusilc ) svygini( ~ py010n , design = des_eusilc , na.rm = TRUE ) # replicate-weighted design using a variable with missings svygini( ~ py010n , design = des_eusilc_rep ) svygini( ~ 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 ) svygini( ~ eqincome , design = dbd_eusilc ) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)
Estimate the difference between the average gross hourly earnings of men and women expressed as a percentage of the average gross hourly earnings of men.
svygpg(formula, design, ...) ## S3 method for class 'survey.design' svygpg(formula, design, sex, na.rm = FALSE, ...) ## S3 method for class 'svyrep.design' svygpg(formula, design, sex, na.rm = FALSE, ...) ## S3 method for class 'DBIsvydesign' svygpg(formula, design, sex, ...)svygpg(formula, design, ...) ## S3 method for class 'survey.design' svygpg(formula, design, sex, na.rm = FALSE, ...) ## S3 method for class 'svyrep.design' svygpg(formula, design, sex, na.rm = FALSE, ...) ## S3 method for class 'DBIsvydesign' svygpg(formula, design, sex, ...)
formula |
a formula specifying the gross hourly earnings variable |
design |
a design object of class |
... |
future expansion |
sex |
formula with a factor with labels 'male' and 'female' |
na.rm |
Should cases with missing values be dropped? |
you must run the convey_prep function on your survey design object immediately after creating it with the svydesign or svrepdesign function.
Object of class "cvystat", which are vectors with a "var" attribute giving the variance and a "statistic" attribute giving the name of the statistic.
Djalma Pessoa and Anthony Damico
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(laeken) library(survey) data(ses) names( ses ) <- gsub( "size" , "size_" , tolower( names( ses ) ) ) des_ses <- svydesign(id=~1, weights=~weights, data=ses) des_ses <- convey_prep(des_ses) # linearized design svygpg(~earningshour, des_ses, ~sex) # replicate-weighted design des_ses_rep <- as.svrepdesign( des_ses , type = "bootstrap" ) des_ses_rep <- convey_prep(des_ses_rep) svygpg(~earningshour, des_ses_rep, ~sex) ## Not run: # database-backed design library(RSQLite) library(DBI) dbfile <- tempfile() conn <- dbConnect( RSQLite::SQLite() , dbfile ) dbWriteTable( conn , 'ses' , ses ) dbd_ses <- svydesign(id=~1, weights=~weights, data="ses", dbname=dbfile, dbtype="SQLite") dbd_ses <- convey_prep( dbd_ses ) svygpg(formula=~earningshour, design=dbd_ses, sex= ~sex) dbRemoveTable( conn , 'ses' ) ## End(Not run)library(laeken) library(survey) data(ses) names( ses ) <- gsub( "size" , "size_" , tolower( names( ses ) ) ) des_ses <- svydesign(id=~1, weights=~weights, data=ses) des_ses <- convey_prep(des_ses) # linearized design svygpg(~earningshour, des_ses, ~sex) # replicate-weighted design des_ses_rep <- as.svrepdesign( des_ses , type = "bootstrap" ) des_ses_rep <- convey_prep(des_ses_rep) svygpg(~earningshour, des_ses_rep, ~sex) ## Not run: # database-backed design library(RSQLite) library(DBI) dbfile <- tempfile() conn <- dbConnect( RSQLite::SQLite() , dbfile ) dbWriteTable( conn , 'ses' , ses ) dbd_ses <- svydesign(id=~1, weights=~weights, data="ses", dbname=dbfile, dbtype="SQLite") dbd_ses <- convey_prep( dbd_ses ) svygpg(formula=~earningshour, design=dbd_ses, sex= ~sex) dbRemoveTable( conn , 'ses' ) ## End(Not run)
Computes the linearized variable of a quantile of variable.
svyiqalpha(formula, design, ...) ## S3 method for class 'survey.design' svyiqalpha(formula, design, alpha, na.rm = FALSE, ...) ## S3 method for class 'svyrep.design' svyiqalpha(formula, design, alpha, na.rm = FALSE, ...) ## S3 method for class 'DBIsvydesign' svyiqalpha(formula, design, ...)svyiqalpha(formula, design, ...) ## S3 method for class 'survey.design' svyiqalpha(formula, design, alpha, na.rm = FALSE, ...) ## S3 method for class 'svyrep.design' svyiqalpha(formula, design, alpha, na.rm = FALSE, ...) ## S3 method for class 'DBIsvydesign' svyiqalpha(formula, design, ...)
formula |
a formula specifying the income variable |
design |
a design object of class |
... |
arguments passed on to 'survey::oldsvyquantile' |
alpha |
the order of the quantile |
na.rm |
Should cases with missing values be dropped? |
you must run the convey_prep function on your survey design object immediately after creating it with the svydesign or svrepdesign function.
Object of class "cvystat", which are vectors with a "var" attribute giving the variance and a "statistic" attribute giving the name of the statistic.
Djalma Pessoa and Anthony Damico
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(laeken) data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) ) library(survey) # linearized design des_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 , weights = ~rb050 , data = eusilc ) des_eusilc <- convey_prep(des_eusilc) svyiqalpha( ~eqincome , design = des_eusilc, .50 ) # replicate-weighted design des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" ) des_eusilc_rep <- convey_prep(des_eusilc_rep) svyiqalpha( ~eqincome , design = des_eusilc_rep, .50 ) ## Not run: # linearized design using a variable with missings svyiqalpha( ~ py010n , design = des_eusilc, .50 ) svyiqalpha( ~ py010n , design = des_eusilc , .50, na.rm = TRUE ) # replicate-weighted design using a variable with missings svyiqalpha( ~ py010n , design = des_eusilc_rep, .50 ) svyiqalpha( ~ py010n , design = des_eusilc_rep ,.50, 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 ) svyiqalpha( ~ eqincome , design = dbd_eusilc, .50 ) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)library(laeken) data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) ) library(survey) # linearized design des_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 , weights = ~rb050 , data = eusilc ) des_eusilc <- convey_prep(des_eusilc) svyiqalpha( ~eqincome , design = des_eusilc, .50 ) # replicate-weighted design des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" ) des_eusilc_rep <- convey_prep(des_eusilc_rep) svyiqalpha( ~eqincome , design = des_eusilc_rep, .50 ) ## Not run: # linearized design using a variable with missings svyiqalpha( ~ py010n , design = des_eusilc, .50 ) svyiqalpha( ~ py010n , design = des_eusilc , .50, na.rm = TRUE ) # replicate-weighted design using a variable with missings svyiqalpha( ~ py010n , design = des_eusilc_rep, .50 ) svyiqalpha( ~ py010n , design = des_eusilc_rep ,.50, 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 ) svyiqalpha( ~ eqincome , design = dbd_eusilc, .50 ) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)
Computes the linearized variable of the total in the lower tail of the distribution of a variable.
svyisq(formula, design, ...) ## S3 method for class 'survey.design' svyisq( formula, design, alpha, quantile = FALSE, upper = FALSE, na.rm = FALSE, deff = FALSE, linearized = FALSE, influence = FALSE, ... ) ## S3 method for class 'svyrep.design' svyisq( formula, design, alpha, quantile = FALSE, upper = FALSE, na.rm = FALSE, deff = FALSE, linearized = FALSE, return.replicates = FALSE, ... ) ## S3 method for class 'DBIsvydesign' svyisq(formula, design, ...)svyisq(formula, design, ...) ## S3 method for class 'survey.design' svyisq( formula, design, alpha, quantile = FALSE, upper = FALSE, na.rm = FALSE, deff = FALSE, linearized = FALSE, influence = FALSE, ... ) ## S3 method for class 'svyrep.design' svyisq( formula, design, alpha, quantile = FALSE, upper = FALSE, na.rm = FALSE, deff = FALSE, linearized = FALSE, return.replicates = FALSE, ... ) ## S3 method for class 'DBIsvydesign' svyisq(formula, design, ...)
formula |
a formula specifying the income variable |
design |
a design object of class |
... |
arguments passed on to 'survey::oldsvyquantile' |
alpha |
the order of the quantile |
quantile |
return the upper bound of the lower tail |
upper |
return the total in the total in the upper tail. Defaults to |
na.rm |
Should cases with missing values be dropped? |
deff |
Return the design effect (see |
linearized |
Should a matrix of linearized variables be returned |
influence |
Should a matrix of (weighted) influence functions be returned? (for compatibility with |
return.replicates |
Return the replicate estimates? |
you must run the convey_prep function on your survey design object immediately after creating it with the svydesign or svrepdesign function.
Object of class "cvystat", which are vectors with a "var" attribute giving the variance and a "statistic" attribute giving the name of the statistic.
Djalma Pessoa, Guilherme Jacob, and Anthony Damico
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(laeken) data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) ) library(survey) des_eusilc <- svydesign(ids = ~rb030, strata =~db040, weights = ~rb050, data = eusilc) des_eusilc <- convey_prep(des_eusilc) svyisq(~eqincome, design=des_eusilc,.20 , quantile = TRUE) # replicate-weighted design des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" ) des_eusilc_rep <- convey_prep(des_eusilc_rep) svyisq( ~eqincome , design = des_eusilc_rep, .20 , quantile = TRUE ) ## Not run: # linearized design using a variable with missings svyisq( ~ py010n , design = des_eusilc, .20 ) svyisq( ~ py010n , design = des_eusilc , .20, na.rm = TRUE ) # replicate-weighted design using a variable with missings svyisq( ~ py010n , design = des_eusilc_rep, .20 ) svyisq( ~ py010n , design = des_eusilc_rep , .20, 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 ) svyisq( ~ eqincome , design = dbd_eusilc, .20 ) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)library(laeken) data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) ) library(survey) des_eusilc <- svydesign(ids = ~rb030, strata =~db040, weights = ~rb050, data = eusilc) des_eusilc <- convey_prep(des_eusilc) svyisq(~eqincome, design=des_eusilc,.20 , quantile = TRUE) # replicate-weighted design des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" ) des_eusilc_rep <- convey_prep(des_eusilc_rep) svyisq( ~eqincome , design = des_eusilc_rep, .20 , quantile = TRUE ) ## Not run: # linearized design using a variable with missings svyisq( ~ py010n , design = des_eusilc, .20 ) svyisq( ~ py010n , design = des_eusilc , .20, na.rm = TRUE ) # replicate-weighted design using a variable with missings svyisq( ~ py010n , design = des_eusilc_rep, .20 ) svyisq( ~ py010n , design = des_eusilc_rep , .20, 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 ) svyisq( ~ eqincome , design = dbd_eusilc, .20 ) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)
Estimate the J-divergence measure, an entropy-based measure of inequality
svyjdiv(formula, design, ...) ## S3 method for class 'survey.design' svyjdiv( formula, design, na.rm = FALSE, deff = FALSE, linearized = FALSE, influence = FALSE, ... ) ## S3 method for class 'svyrep.design' svyjdiv( formula, design, na.rm = FALSE, deff = FALSE, linearized = FALSE, return.replicates = FALSE, ... ) ## S3 method for class 'DBIsvydesign' svyjdiv(formula, design, ...)svyjdiv(formula, design, ...) ## S3 method for class 'survey.design' svyjdiv( formula, design, na.rm = FALSE, deff = FALSE, linearized = FALSE, influence = FALSE, ... ) ## S3 method for class 'svyrep.design' svyjdiv( formula, design, na.rm = FALSE, deff = FALSE, linearized = FALSE, return.replicates = FALSE, ... ) ## S3 method for class 'DBIsvydesign' svyjdiv(formula, design, ...)
formula |
a formula specifying the income variable |
design |
a design object of class |
... |
future expansion |
na.rm |
Should cases with missing values be dropped? |
deff |
Return the design effect (see |
linearized |
Should a matrix of linearized variables be returned |
influence |
Should a matrix of (weighted) influence functions be returned? (for compatibility with |
return.replicates |
Return the replicate estimates? |
you must run the convey_prep function on your survey design object immediately after creating it with the svydesign or svrepdesign function.
This measure only allows for strictly positive variables.
Object of class "cvystat", which are vectors with a "var" attribute giving the variance and a "statistic" attribute giving the name of the statistic.
Guilherme Jacob, Djalma Pessoa, and Anthony Damico
Nicholas Rohde (2016). J-divergence measurements of economic inequality. J. R. Statist. Soc. A, v. 179, Part 3 (2016), pp. 847-870. DOI doi:10.1111/rssa.12153.
Martin Biewen and Stephen Jenkins (2002). Estimation of Generalized Entropy and Atkinson Inequality Indices from Complex Survey Data. DIW Discussion Papers, No.345, URL https://www.diw.de/documents/publikationen/73/diw_01.c.40394.de/dp345.pdf.
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) svyjdiv( ~eqincome , design = subset( des_eusilc , eqincome > 0 ) ) # replicate-weighted design des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" ) des_eusilc_rep <- convey_prep(des_eusilc_rep) svyjdiv( ~eqincome , design = subset( des_eusilc_rep , eqincome > 0 ) ) ## Not run: # linearized design using a variable with missings svyjdiv( ~py010n , design = subset( des_eusilc , py010n > 0 | is.na( py010n ) ) ) svyjdiv( ~py010n , design = subset( des_eusilc , py010n > 0 | is.na( py010n ) ), na.rm = TRUE ) # replicate-weighted design using a variable with missings svyjdiv( ~py010n , design = subset( des_eusilc_rep , py010n > 0 | is.na( py010n ) ) ) svyjdiv( ~py010n , design = subset( des_eusilc_rep , py010n > 0 | is.na( py010n ) ) , 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 ) svyjdiv( ~eqincome , design = subset( dbd_eusilc , eqincome > 0 ) ) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)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) svyjdiv( ~eqincome , design = subset( des_eusilc , eqincome > 0 ) ) # replicate-weighted design des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" ) des_eusilc_rep <- convey_prep(des_eusilc_rep) svyjdiv( ~eqincome , design = subset( des_eusilc_rep , eqincome > 0 ) ) ## Not run: # linearized design using a variable with missings svyjdiv( ~py010n , design = subset( des_eusilc , py010n > 0 | is.na( py010n ) ) ) svyjdiv( ~py010n , design = subset( des_eusilc , py010n > 0 | is.na( py010n ) ), na.rm = TRUE ) # replicate-weighted design using a variable with missings svyjdiv( ~py010n , design = subset( des_eusilc_rep , py010n > 0 | is.na( py010n ) ) ) svyjdiv( ~py010n , design = subset( des_eusilc_rep , py010n > 0 | is.na( py010n ) ) , 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 ) svyjdiv( ~eqincome , design = subset( dbd_eusilc , eqincome > 0 ) ) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)
Estimates the group decomposition of the generalized entropy index
svyjdivdec(formula, subgroup, design, ...) ## S3 method for class 'survey.design' svyjdivdec( formula, subgroup, design, na.rm = FALSE, deff = FALSE, linearized = FALSE, influence = FALSE, ... ) ## S3 method for class 'svyrep.design' svyjdivdec( formula, subgroup, design, na.rm = FALSE, deff = FALSE, linearized = FALSE, return.replicates = FALSE, ... ) ## S3 method for class 'DBIsvydesign' svyjdivdec(formula, subgroup, design, ...)svyjdivdec(formula, subgroup, design, ...) ## S3 method for class 'survey.design' svyjdivdec( formula, subgroup, design, na.rm = FALSE, deff = FALSE, linearized = FALSE, influence = FALSE, ... ) ## S3 method for class 'svyrep.design' svyjdivdec( formula, subgroup, design, na.rm = FALSE, deff = FALSE, linearized = FALSE, return.replicates = FALSE, ... ) ## S3 method for class 'DBIsvydesign' svyjdivdec(formula, subgroup, design, ...)
formula |
a formula specifying the income variable |
subgroup |
a formula specifying the group variable |
design |
a design object of class |
... |
future expansion |
na.rm |
Should cases with missing values be dropped? Observations containing missing values in income or group variables will be dropped. |
deff |
Return the design effect (see |
linearized |
Should a matrix of linearized variables be returned |
influence |
Should a matrix of (weighted) influence functions be returned? (for compatibility with |
return.replicates |
Return the replicate estimates? |
you must run the convey_prep function on your survey design object immediately after creating it with the svydesign or svrepdesign function.
This measure only allows for strictly positive variables.
Object of class "cvydstat", which are vectors with a "var" attribute giving the variance-covariance matrix and a "statistic" attribute giving the name of the statistic.
Guilherme Jacob, Djalma Pessoa, and Anthony Damico
Anthony F. Shorrocks (1984). Inequality decomposition by population subgroups. Econometrica, v. 52, n. 6, 1984, pp. 1369-1385. DOI doi:10.2307/1913511.
Nicholas Rohde (2016). J-divergence measurements of economic inequality. J. R. Statist. Soc. A, v. 179, Part 3 (2016), pp. 847-870. DOI doi:10.1111/rssa.12153.
Martin Biewen and Stephen Jenkins (2002). Estimation of Generalized Entropy and Atkinson Inequality Indices from Complex Survey Data. DIW Discussion Papers, No.345, URL https://www.diw.de/documents/publikationen/73/diw_01.c.40394.de/dp345.pdf.
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) # linearized design svyjdivdec( ~eqincome , ~rb090 , subset(des_eusilc, eqincome > 0) ) # replicate-weighted design svyjdivdec( ~eqincome , ~rb090 , subset(des_eusilc_rep, eqincome > 0) ) ## Not run: # linearized design using a variable with missings sub_des_eusilc <- subset(des_eusilc, py010n > 0 | is.na(py010n) ) svyjdivdec( ~py010n , ~rb090 , sub_des_eusilc ) svyjdivdec( ~py010n , ~rb090 , sub_des_eusilc , na.rm = TRUE ) # replicate-weighted design using a variable with missings sub_des_eusilc_rep <- subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ) svyjdivdec( ~py010n , ~rb090 , sub_des_eusilc_rep ) svyjdivdec( ~py010n , ~rb090 , sub_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 ) # database-backed linearized design svyjdivdec( ~eqincome , ~rb090 , subset(dbd_eusilc, eqincome > 0) ) # database-backed linearized design using a variable with missings sub_dbd_eusilc <- subset(dbd_eusilc, py010n > 0 | is.na(py010n) ) svyjdivdec( ~py010n , ~rb090 , sub_dbd_eusilc ) svyjdivdec( ~py010n , ~rb090 , sub_dbd_eusilc , na.rm = TRUE ) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)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) # linearized design svyjdivdec( ~eqincome , ~rb090 , subset(des_eusilc, eqincome > 0) ) # replicate-weighted design svyjdivdec( ~eqincome , ~rb090 , subset(des_eusilc_rep, eqincome > 0) ) ## Not run: # linearized design using a variable with missings sub_des_eusilc <- subset(des_eusilc, py010n > 0 | is.na(py010n) ) svyjdivdec( ~py010n , ~rb090 , sub_des_eusilc ) svyjdivdec( ~py010n , ~rb090 , sub_des_eusilc , na.rm = TRUE ) # replicate-weighted design using a variable with missings sub_des_eusilc_rep <- subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ) svyjdivdec( ~py010n , ~rb090 , sub_des_eusilc_rep ) svyjdivdec( ~py010n , ~rb090 , sub_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 ) # database-backed linearized design svyjdivdec( ~eqincome , ~rb090 , subset(dbd_eusilc, eqincome > 0) ) # database-backed linearized design using a variable with missings sub_dbd_eusilc <- subset(dbd_eusilc, py010n > 0 | is.na(py010n) ) svyjdivdec( ~py010n , ~rb090 , sub_dbd_eusilc ) svyjdivdec( ~py010n , ~rb090 , sub_dbd_eusilc , na.rm = TRUE ) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)
Estimate the Lorenz curve, an inequality graph
svylorenz(formula, design, ...) ## S3 method for class 'survey.design' svylorenz( formula, design, quantiles = seq(0, 1, 0.1), empirical = FALSE, plot = TRUE, add = FALSE, curve.col = "red", ci = TRUE, alpha = 0.05, na.rm = FALSE, deff = FALSE, linearized = FALSE, influence = FALSE, ... ) ## S3 method for class 'svyrep.design' svylorenz( formula, design, quantiles = seq(0, 1, 0.1), empirical = FALSE, plot = TRUE, add = FALSE, curve.col = "red", ci = TRUE, alpha = 0.05, na.rm = FALSE, deff = FALSE, linearized = FALSE, return.replicates = FALSE, ... ) ## S3 method for class 'DBIsvydesign' svylorenz(formula, design, ...)svylorenz(formula, design, ...) ## S3 method for class 'survey.design' svylorenz( formula, design, quantiles = seq(0, 1, 0.1), empirical = FALSE, plot = TRUE, add = FALSE, curve.col = "red", ci = TRUE, alpha = 0.05, na.rm = FALSE, deff = FALSE, linearized = FALSE, influence = FALSE, ... ) ## S3 method for class 'svyrep.design' svylorenz( formula, design, quantiles = seq(0, 1, 0.1), empirical = FALSE, plot = TRUE, add = FALSE, curve.col = "red", ci = TRUE, alpha = 0.05, na.rm = FALSE, deff = FALSE, linearized = FALSE, return.replicates = FALSE, ... ) ## S3 method for class 'DBIsvydesign' svylorenz(formula, design, ...)
formula |
a formula specifying the income variable |
design |
a design object of class |
... |
additional arguments passed to |
quantiles |
a sequence of probabilities that defines the quantiles sum to be calculated |
empirical |
Should an empirical Lorenz curve be estimated as well? Defaults to |
plot |
Should the Lorenz curve be plotted? Defaults to |
add |
Should a new curve be plotted on the current graph? |
curve.col |
a string defining the color of the curve. |
ci |
Should the confidence interval be plotted? Defaults to |
alpha |
a number that especifies de confidence level for the graph. |
na.rm |
Should cases with missing values be dropped? Defaults to |
deff |
Return the design effect (see |
linearized |
Should a matrix of linearized variables be returned |
influence |
Should a matrix of (weighted) influence functions be returned? (for compatibility with |
return.replicates |
Return the replicate estimates? |
you must run the convey_prep function on your survey design object immediately after creating it with the svydesign or svrepdesign function.
Notice that the 'empirical' curve is observation-based and is the one actually used to calculate the Gini index. On the other hand, the quantile-based curve is used to estimate the shares, SEs and confidence intervals.
This way, as the number of quantiles of the quantile-based function increases, the quantile-based curve approacches the observation-based curve.
Object of class "survey::oldsvyquantile", which are vectors with a "quantiles" attribute giving the proportion of income below that quantile,
and a "SE" attribute giving the standard errors of the estimates.
Guilherme Jacob, Djalma Pessoa and Anthony Damico
Milorad Kovacevic and David Binder (1997). Variance Estimation for Measures of Income Inequality and Polarization - The Estimating Equations Approach. Journal of Official Statistics, Vol.13, No.1, 1997. pp. 41 58. URL https://www.scb.se/contentassets/ca21efb41fee47d293bbee5bf7be7fb3/variance-estimation-for-measures-of-income-inequality-and-polarization—the-estimating-equations-approach.pdf.
Shlomo Yitzhaki and Robert Lerman (1989). Improving the accuracy of estimates of Gini coefficients. Journal of Econometrics, Vol.42(1), pp. 43-47, September.
Matti Langel (2012). Measuring inequality in finite population sampling. PhD thesis. URL http://doc.rero.ch/record/29204.
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 ) svylorenz( ~eqincome , des_eusilc, seq(0,1,.05), alpha = .01 ) # replicate-weighted design des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" ) des_eusilc_rep <- convey_prep( des_eusilc_rep ) svylorenz( ~eqincome , des_eusilc_rep, seq(0,1,.05), alpha = .01 ) ## Not run: # linearized design using a variable with missings svylorenz( ~py010n , des_eusilc, seq(0,1,.05), alpha = .01 ) svylorenz( ~py010n , des_eusilc, seq(0,1,.05), alpha = .01, na.rm = TRUE ) # demonstration of `curve.col=` and `add=` parameters svylorenz( ~eqincome , des_eusilc, seq(0,1,.05), alpha = .05 , add = TRUE , curve.col = 'green' ) # replicate-weighted design using a variable with missings svylorenz( ~py010n , des_eusilc_rep, seq(0,1,.05), alpha = .01 ) svylorenz( ~py010n , des_eusilc_rep, seq(0,1,.05), alpha = .01, 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 ) svylorenz( ~eqincome , dbd_eusilc, seq(0,1,.05), alpha = .01 ) # highlithing the difference between the quantile-based curve and the empirical version: svylorenz( ~eqincome , dbd_eusilc, seq(0,1,.5), empirical = TRUE, ci = FALSE, curve.col = "green" ) svylorenz( ~eqincome , dbd_eusilc, seq(0,1,.5), alpha = .01, add = TRUE ) legend( "topleft", c("Quantile-based", "Empirical"), lwd = c(1,1), col = c("red", "green")) # as the number of quantiles increases, the difference between the curves gets smaller svylorenz( ~eqincome , dbd_eusilc, seq(0,1,.01), empirical = TRUE, ci = FALSE, curve.col = "green" ) svylorenz( ~eqincome , dbd_eusilc, seq(0,1,.01), alpha = .01, add = TRUE ) legend( "topleft", c("Quantile-based", "Empirical"), lwd = c(1,1), col = c("red", "green")) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)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 ) svylorenz( ~eqincome , des_eusilc, seq(0,1,.05), alpha = .01 ) # replicate-weighted design des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" ) des_eusilc_rep <- convey_prep( des_eusilc_rep ) svylorenz( ~eqincome , des_eusilc_rep, seq(0,1,.05), alpha = .01 ) ## Not run: # linearized design using a variable with missings svylorenz( ~py010n , des_eusilc, seq(0,1,.05), alpha = .01 ) svylorenz( ~py010n , des_eusilc, seq(0,1,.05), alpha = .01, na.rm = TRUE ) # demonstration of `curve.col=` and `add=` parameters svylorenz( ~eqincome , des_eusilc, seq(0,1,.05), alpha = .05 , add = TRUE , curve.col = 'green' ) # replicate-weighted design using a variable with missings svylorenz( ~py010n , des_eusilc_rep, seq(0,1,.05), alpha = .01 ) svylorenz( ~py010n , des_eusilc_rep, seq(0,1,.05), alpha = .01, 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 ) svylorenz( ~eqincome , dbd_eusilc, seq(0,1,.05), alpha = .01 ) # highlithing the difference between the quantile-based curve and the empirical version: svylorenz( ~eqincome , dbd_eusilc, seq(0,1,.5), empirical = TRUE, ci = FALSE, curve.col = "green" ) svylorenz( ~eqincome , dbd_eusilc, seq(0,1,.5), alpha = .01, add = TRUE ) legend( "topleft", c("Quantile-based", "Empirical"), lwd = c(1,1), col = c("red", "green")) # as the number of quantiles increases, the difference between the curves gets smaller svylorenz( ~eqincome , dbd_eusilc, seq(0,1,.01), empirical = TRUE, ci = FALSE, curve.col = "green" ) svylorenz( ~eqincome , dbd_eusilc, seq(0,1,.01), alpha = .01, add = TRUE ) legend( "topleft", c("Quantile-based", "Empirical"), lwd = c(1,1), col = c("red", "green")) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)
Estimate the median of incomes less than the at-risk-of-poverty threshold (arpt).
svypoormed(formula, design, ...) ## S3 method for class 'survey.design' svypoormed(formula, design, quantiles = 0.5, percent = 0.6, na.rm = FALSE, ...) ## S3 method for class 'svyrep.design' svypoormed(formula, design, quantiles = 0.5, percent = 0.6, na.rm = FALSE, ...) ## S3 method for class 'DBIsvydesign' svypoormed(formula, design, ...)svypoormed(formula, design, ...) ## S3 method for class 'survey.design' svypoormed(formula, design, quantiles = 0.5, percent = 0.6, na.rm = FALSE, ...) ## S3 method for class 'svyrep.design' svypoormed(formula, design, quantiles = 0.5, percent = 0.6, na.rm = FALSE, ...) ## S3 method for class 'DBIsvydesign' svypoormed(formula, design, ...)
formula |
a formula specifying the income variable |
design |
a design object of class |
... |
arguments passed on to 'survey::oldsvyquantile' |
quantiles |
income quantile, usually .5 (median) |
percent |
fraction of the quantile, usually .60 |
na.rm |
Should cases with missing values be dropped? |
you must run the convey_prep function on your survey design object immediately after creating it with the svydesign or svrepdesign function.
Object of class "cvystat", which are vectors with a "var" attribute giving the variance and a "statistic" attribute giving the name of the statistic.
Djalma Pessoa and Anthony Damico
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 ) svypoormed( ~eqincome , design = des_eusilc ) # replicate-weighted design des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" ) des_eusilc_rep <- convey_prep( des_eusilc_rep ) svypoormed( ~eqincome , design = des_eusilc_rep ) ## Not run: # linearized design using a variable with missings svypoormed( ~ py010n , design = des_eusilc ) svypoormed( ~ py010n , design = des_eusilc , na.rm = TRUE ) # replicate-weighted design using a variable with missings svypoormed( ~ py010n , design = des_eusilc_rep ) svypoormed( ~ 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 ) svypoormed( ~ eqincome , design = dbd_eusilc ) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)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 ) svypoormed( ~eqincome , design = des_eusilc ) # replicate-weighted design des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" ) des_eusilc_rep <- convey_prep( des_eusilc_rep ) svypoormed( ~eqincome , design = des_eusilc_rep ) ## Not run: # linearized design using a variable with missings svypoormed( ~ py010n , design = des_eusilc ) svypoormed( ~ py010n , design = des_eusilc , na.rm = TRUE ) # replicate-weighted design using a variable with missings svypoormed( ~ py010n , design = des_eusilc_rep ) svypoormed( ~ 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 ) svypoormed( ~ eqincome , design = dbd_eusilc ) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)
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 class '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 class '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 class 'DBIsvydesign' svyqsr(formula, design, ...)svyqsr(formula, design, ...) ## S3 method for class '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 class '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 class 'DBIsvydesign' svyqsr(formula, design, ...)
formula |
a formula specifying the income variable |
design |
a design object of class |
... |
future expansion |
alpha1 |
order of the lower quintile |
alpha2 |
order of the upper quintile |
na.rm |
Should cases with missing values be dropped? |
upper_quant |
return the lower bound of highest earners |
lower_quant |
return the upper bound of lowest earners |
upper_tot |
return the highest earners total |
lower_tot |
return the lowest earners total |
deff |
Return the design effect (see |
linearized |
Should a matrix of linearized variables be returned |
influence |
Should a matrix of (weighted) influence functions be returned? (for compatibility with |
return.replicates |
Return the replicate estimates? |
you must run the convey_prep function on your survey design object immediately after creating it with the svydesign or svrepdesign function.
Object of class "cvystat", which are vectors with a "var" attribute giving the variance and a "statistic" attribute giving the name of the statistic.
Djalma Pessoa and Anthony Damico
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 ) # 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 ) ## Not run: # 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 ) ## End(Not run)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 ) # 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 ) ## Not run: # 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 ) ## End(Not run)
Estimate Peichl, Schaefer and Scheicher (2010) richness measures.
svyrich(formula, design, ...) ## S3 method for class 'survey.design' svyrich( formula, design, type_measure, g, type_thresh = "abs", abs_thresh = NULL, percent = 1.5, quantiles = 0.5, thresh = FALSE, na.rm = FALSE, deff = FALSE, linearized = FALSE, ... ) ## S3 method for class 'svyrep.design' svyrich( formula, design, type_measure, g, type_thresh = "abs", abs_thresh = NULL, percent = 1.5, quantiles = 0.5, thresh = FALSE, na.rm = FALSE, deff = FALSE, linearized = FALSE, return.replicates = FALSE, ... ) ## S3 method for class 'DBIsvydesign' svyrich(formula, design, ...)svyrich(formula, design, ...) ## S3 method for class 'survey.design' svyrich( formula, design, type_measure, g, type_thresh = "abs", abs_thresh = NULL, percent = 1.5, quantiles = 0.5, thresh = FALSE, na.rm = FALSE, deff = FALSE, linearized = FALSE, ... ) ## S3 method for class 'svyrep.design' svyrich( formula, design, type_measure, g, type_thresh = "abs", abs_thresh = NULL, percent = 1.5, quantiles = 0.5, thresh = FALSE, na.rm = FALSE, deff = FALSE, linearized = FALSE, return.replicates = FALSE, ... ) ## S3 method for class 'DBIsvydesign' svyrich(formula, design, ...)
formula |
a formula specifying the income variable |
design |
a design object of class |
... |
passed to |
type_measure |
A string "Cha", "FGTT1" or "FGTT2" defining the richness measure. |
g |
Richness preference parameter. |
type_thresh |
type of richness threshold. If "abs" the threshold is fixed and given the value of abs_thresh; if "relq" it is given by |
abs_thresh |
richness threshold value if type_thresh is "abs" |
percent |
the multiple of the quantile or mean used in the richness threshold definition. Defaults to |
quantiles |
the quantile used used in the richness threshold definition. Defaults to |
thresh |
return the richness threshold value |
na.rm |
Should cases with missing values be dropped? |
deff |
Return the design effect (see |
linearized |
Should a matrix of linearized variables be returned |
return.replicates |
Return the replicate estimates? |
you must run the convey_prep function on your survey design object immediately after creating it with the svydesign or svrepdesign function.
Object of class "cvystat", which are vectors with a "var" attribute giving the variance and a "statistic" attribute giving the name of the statistic.
Guilherme Jacob, Djalma Pessoa and Anthony Damico
Michal Brzezinski (2014). Statistical Inference for Richness Measures. Applied Economics, Vol. 46, No. 14, pp. 1599-1608, DOI doi:10.1080/00036846.2014.880106.
Andreas Peichl, Thilo Schaefer, and Christoph Scheicher (2010). Measuring richness and poverty: A micro data application to Europe and Germany. Review of Income and Wealth, Vol. 56, No.3, pp. 597-619.
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.
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 ) # concave Chakravarty richness measure # higher g= parameters tend toward headcount ratio, richness threshold fixed svyrich(~eqincome, des_eusilc, type_measure = "Cha" , g=3, abs_thresh=30000) # g=1 parameter computes the richness gap index, richness threshold fixed svyrich(~eqincome, des_eusilc, type_measure = "Cha" , g=1, abs_thresh=30000) # higher g= parameters tend toward headcount ratio, richness threshold equal to the median svyrich(~eqincome, des_eusilc, type_measure = "Cha" , g=3, type_thresh= "relq" ) # g=1 parameter computes the richness gap index, richness threshold equal to the median svyrich(~eqincome, des_eusilc, type_measure = "Cha" , g=1, type_thresh= "relq" ) # higher g= parameters tend toward headcount ratio, richness threshold equal to the mean svyrich(~eqincome, des_eusilc, type_measure = "Cha" , g=3, type_thresh= "relm" ) # g=1 parameter computes the richness gap index, richness threshold equal to the mean svyrich(~eqincome, des_eusilc, type_measure = "Cha" , g=1, type_thresh= "relm" ) # using svrep.design: # higher g= parameters tend toward headcount ratio, richness threshold fixed svyrich(~eqincome, des_eusilc_rep, type_measure = "Cha" , g=3, abs_thresh=30000 ) # g=1 parameter computes the richness gap index, richness threshold fixed svyrich(~eqincome, des_eusilc_rep, type_measure = "Cha" , g=1, abs_thresh=30000 ) # higher g= parameters tend toward headcount ratio, richness threshold equal to the median svyrich(~eqincome, des_eusilc_rep, type_measure = "Cha" , g=3, type_thresh= "relq" ) # g=1 parameter computes the richness gap index, richness threshold equal to the median svyrich(~eqincome, des_eusilc_rep, type_measure = "Cha" , g=1, type_thresh= "relq" ) # higher g= parameters tend toward headcount ratio, richness threshold equal to the mean svyrich(~eqincome, des_eusilc_rep, type_measure = "Cha" , g=3, type_thresh= "relm" ) # g=1 parameter computes the richness gap index, richness threshold equal to the mean svyrich(~eqincome, des_eusilc_rep, type_measure = "Cha" , g=1, type_thresh= "relm" ) ## Not run: # 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 ) # higher g= parameters tend toward headcount ratio, richness threshold fixed svyrich(~eqincome, dbd_eusilc, type_measure = "Cha" , g=3, abs_thresh=30000 ) # g=1 parameter computes the richness gap index, richness threshold fixed svyrich(~eqincome, dbd_eusilc, type_measure = "Cha" , g=1, abs_thresh=30000 ) # higher g= parameters tend toward headcount ratio, richness threshold equal to the median svyrich(~eqincome, dbd_eusilc, type_measure = "Cha" , g=3, type_thresh= "relq" ) # g=1 parameter computes the richness gap index, richness threshold equal to the median svyrich(~eqincome, dbd_eusilc, type_measure = "Cha" , g=1, type_thresh= "relq" ) # higher g= parameters tend toward headcount ratio, richness threshold equal to the mean svyrich(~eqincome, dbd_eusilc, type_measure = "Cha" , g=3, type_thresh= "relm" ) # g=1 parameter computes the richness gap index, richness threshold equal to the mean svyrich(~eqincome, dbd_eusilc, type_measure = "Cha" , g=1, type_thresh= "relm" ) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)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 ) # concave Chakravarty richness measure # higher g= parameters tend toward headcount ratio, richness threshold fixed svyrich(~eqincome, des_eusilc, type_measure = "Cha" , g=3, abs_thresh=30000) # g=1 parameter computes the richness gap index, richness threshold fixed svyrich(~eqincome, des_eusilc, type_measure = "Cha" , g=1, abs_thresh=30000) # higher g= parameters tend toward headcount ratio, richness threshold equal to the median svyrich(~eqincome, des_eusilc, type_measure = "Cha" , g=3, type_thresh= "relq" ) # g=1 parameter computes the richness gap index, richness threshold equal to the median svyrich(~eqincome, des_eusilc, type_measure = "Cha" , g=1, type_thresh= "relq" ) # higher g= parameters tend toward headcount ratio, richness threshold equal to the mean svyrich(~eqincome, des_eusilc, type_measure = "Cha" , g=3, type_thresh= "relm" ) # g=1 parameter computes the richness gap index, richness threshold equal to the mean svyrich(~eqincome, des_eusilc, type_measure = "Cha" , g=1, type_thresh= "relm" ) # using svrep.design: # higher g= parameters tend toward headcount ratio, richness threshold fixed svyrich(~eqincome, des_eusilc_rep, type_measure = "Cha" , g=3, abs_thresh=30000 ) # g=1 parameter computes the richness gap index, richness threshold fixed svyrich(~eqincome, des_eusilc_rep, type_measure = "Cha" , g=1, abs_thresh=30000 ) # higher g= parameters tend toward headcount ratio, richness threshold equal to the median svyrich(~eqincome, des_eusilc_rep, type_measure = "Cha" , g=3, type_thresh= "relq" ) # g=1 parameter computes the richness gap index, richness threshold equal to the median svyrich(~eqincome, des_eusilc_rep, type_measure = "Cha" , g=1, type_thresh= "relq" ) # higher g= parameters tend toward headcount ratio, richness threshold equal to the mean svyrich(~eqincome, des_eusilc_rep, type_measure = "Cha" , g=3, type_thresh= "relm" ) # g=1 parameter computes the richness gap index, richness threshold equal to the mean svyrich(~eqincome, des_eusilc_rep, type_measure = "Cha" , g=1, type_thresh= "relm" ) ## Not run: # 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 ) # higher g= parameters tend toward headcount ratio, richness threshold fixed svyrich(~eqincome, dbd_eusilc, type_measure = "Cha" , g=3, abs_thresh=30000 ) # g=1 parameter computes the richness gap index, richness threshold fixed svyrich(~eqincome, dbd_eusilc, type_measure = "Cha" , g=1, abs_thresh=30000 ) # higher g= parameters tend toward headcount ratio, richness threshold equal to the median svyrich(~eqincome, dbd_eusilc, type_measure = "Cha" , g=3, type_thresh= "relq" ) # g=1 parameter computes the richness gap index, richness threshold equal to the median svyrich(~eqincome, dbd_eusilc, type_measure = "Cha" , g=1, type_thresh= "relq" ) # higher g= parameters tend toward headcount ratio, richness threshold equal to the mean svyrich(~eqincome, dbd_eusilc, type_measure = "Cha" , g=3, type_thresh= "relm" ) # g=1 parameter computes the richness gap index, richness threshold equal to the mean svyrich(~eqincome, dbd_eusilc, type_measure = "Cha" , g=1, type_thresh= "relm" ) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)
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 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, ...)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, ...)
formula |
a formula specifying the income variable |
design |
a design object of class |
... |
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 |
you must run the convey_prep function on your survey design object immediately after creating it with the svydesign or svrepdesign function.
Object of class "cvystat", which are vectors with a "var" attribute giving the variance and a "statistic" attribute giving the name of the statistic.
Djalma Pessoa and Anthony Damico
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$eqincome_miss)<- ind_miss==1 # linearized design des_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 , weights = ~rb050 , data = eusilc ) des_eusilc <- convey_prep(des_eusilc) svyrmir( ~eqincome , design = des_eusilc , age = ~age, med_old = TRUE ) # replicate-weighted design des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" ) des_eusilc_rep <- convey_prep(des_eusilc_rep) svyrmir( ~eqincome , design = des_eusilc_rep, age= ~age, med_old = TRUE ) ## Not run: # linearized design using a variable with missings svyrmir( ~ eqincome_miss , design = des_eusilc,age= ~age) svyrmir( ~ eqincome_miss , design = des_eusilc , age= ~age, na.rm = TRUE ) # replicate-weighted design using a variable with missings svyrmir( ~ eqincome_miss , design = des_eusilc_rep,age= ~age ) svyrmir( ~ eqincome_miss , design = des_eusilc_rep ,age= ~age, na.rm = TRUE ) # database-backed design library(RSQLite) library(DBI) dbfile <- tempfile() conn <- dbConnect( RSQLite::SQLite() , dbfile ) dbWriteTable( conn , 'eusilc' , eusilc ) dbd_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 , weights = ~rb050 , data="eusilc", dbname=dbfile, dbtype="SQLite" ) dbd_eusilc <- convey_prep( dbd_eusilc ) svyrmir( ~eqincome , design = dbd_eusilc , age = ~age ) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)library(survey) library(laeken) data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) ) # missing completely at random, missingness rate = .20 ind_miss <- rbinom(nrow(eusilc), 1, .20 ) eusilc$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)
Estimate the difference between the at-risk-of-poverty threshold (arpt) and the median of incomes less than the arpt relative to the arpt.
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, ...)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, ...)
formula |
a formula specifying the income variable |
design |
a design object of class |
... |
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 |
you must run the convey_prep function on your survey design object immediately after creating it with the svydesign or svrepdesign function.
Object of class "cvystat", which are vectors with a "var" attribute giving the variance and a "statistic" attribute giving the name of the statistic.
Djalma Pessoa and Anthony Damico
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 ) 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)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)
Estimate the Watts measure for the cases: alpha=0 headcount ratio and alpha=1 poverty gap index.
svywatts(formula, design, ...) ## S3 method for class 'survey.design' svywatts( formula, design, type_thresh = "abs", abs_thresh = NULL, percent = 0.6, quantiles = 0.5, thresh = FALSE, na.rm = FALSE, deff = FALSE, linearized = FALSE, influence = FALSE, ... ) ## S3 method for class 'svyrep.design' svywatts( formula, design, type_thresh = "abs", abs_thresh = NULL, percent = 0.6, quantiles = 0.5, thresh = FALSE, na.rm = FALSE, deff = FALSE, linearized = FALSE, return.replicates = FALSE, ... ) ## S3 method for class 'DBIsvydesign' svywatts(formula, design, ...)svywatts(formula, design, ...) ## S3 method for class 'survey.design' svywatts( formula, design, type_thresh = "abs", abs_thresh = NULL, percent = 0.6, quantiles = 0.5, thresh = FALSE, na.rm = FALSE, deff = FALSE, linearized = FALSE, influence = FALSE, ... ) ## S3 method for class 'svyrep.design' svywatts( formula, design, type_thresh = "abs", abs_thresh = NULL, percent = 0.6, quantiles = 0.5, thresh = FALSE, na.rm = FALSE, deff = FALSE, linearized = FALSE, return.replicates = FALSE, ... ) ## S3 method for class 'DBIsvydesign' svywatts(formula, design, ...)
formula |
a formula specifying the income variable |
design |
a design object of class |
... |
passed to |
type_thresh |
type of poverty threshold. If "abs" the threshold is fixed and given the value of abs_thresh; if "relq" it is given by percent times the quantile; if "relm" it is percent times the mean. |
abs_thresh |
poverty threshold value if type_thresh is "abs" |
percent |
the multiple of the the quantile or mean used in the poverty threshold definition |
quantiles |
the quantile used used in the poverty threshold definition |
thresh |
return the poverty threshold value |
na.rm |
Should cases with missing values be dropped? |
deff |
Return the design effect (see |
linearized |
Should a matrix of linearized variables be returned |
influence |
Should a matrix of (weighted) influence functions be returned? (for compatibility with |
return.replicates |
Return the replicate estimates? |
you must run the convey_prep function on your survey design object immediately after creating it with the svydesign or svrepdesign function.
For the svywatts and svywattsdec functions, zeroes and negative numbers in the analysis domain cause an error because of the logarithm function in the definition of this poverty measure. However, zeroes and negative values in the full survey design that are outside of the domain of analysis are valid to calculate the poverty threshold because zeroes and negatives are not a problem for computing quantiles (used when type_thresh = "relq") or means (used when type_thresh = "relm") . Missing values are treated differently. NA values anywhere in the full survey design (not only the subset, or the domain of analysis) will cause these quantiles and means to return NA results. To ignore NA values throughout, set na.rm = TRUE.
Object of class "cvystat", which are vectors with a "var" attribute giving the variance and a "statistic" attribute giving the name of the statistic.
Guilherme Jacob, Djalma Pessoa, and Anthony Damico
Harold W. Watts (1968). An economic definition of poverty. Institute For Research on Poverty Discussion Papers, n.5. University of Wisconsin. URL https://www.irp.wisc.edu/publications/dps/pdfs/dp568.pdf.
Buhong Zheng (2001). Statistical inference for poverty measures with relative poverty lines. Journal of Econometrics, Vol. 101, pp. 337-356.
Vijay Verma and Gianni Betti (2011). Taylor linearization sampling errors and design effects for poverty measures and other complex statistics. Journal Of Applied Statistics, Vol.38, No.8, pp. 1549-1576, DOI doi:10.1080/02664763.2010.515674.
Anthony B. Atkinson (1987). On the measurement of poverty. Econometrica, Vol.55, No.4, (Jul., 1987), pp. 749-764, DOI doi:10.2307/1911028.
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 ) # replicate-weighted design des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" ) des_eusilc_rep <- convey_prep( des_eusilc_rep ) # filter positive incomes des_eusilc <- subset( des_eusilc , eqincome > 0 ) des_eusilc_rep <- subset( des_eusilc_rep , eqincome > 0 ) # poverty threshold fixed svywatts(~eqincome, des_eusilc , abs_thresh=10000) # poverty threshold equal to arpt svywatts(~eqincome, des_eusilc , type_thresh= "relq", thresh = TRUE) # poverty threshold equal to 0.6 times the mean svywatts(~eqincome, des_eusilc , type_thresh= "relm" , thresh = TRUE) # using svrep.design: # poverty threshold fixed svywatts(~eqincome, des_eusilc_rep , abs_thresh=10000) # poverty threshold equal to arpt svywatts(~eqincome, des_eusilc_rep , type_thresh= "relq", thresh = TRUE) # poverty threshold equal to 0.6 times the mean svywatts(~eqincome, des_eusilc_rep , type_thresh= "relm" , thresh = TRUE) ## Not run: # 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 ) # filter positive incomes dbd_eusilc <- subset( dbd_eusilc , eqincome > 0 ) # poverty threshold fixed svywatts(~eqincome, dbd_eusilc , abs_thresh=10000) # poverty threshold equal to arpt svywatts(~eqincome, dbd_eusilc , type_thresh= "relq", thresh = TRUE) # poverty threshold equal to 0.6 times the mean svywatts(~eqincome, dbd_eusilc , type_thresh= "relm" , thresh = TRUE) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)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 ) # filter positive incomes des_eusilc <- subset( des_eusilc , eqincome > 0 ) des_eusilc_rep <- subset( des_eusilc_rep , eqincome > 0 ) # poverty threshold fixed svywatts(~eqincome, des_eusilc , abs_thresh=10000) # poverty threshold equal to arpt svywatts(~eqincome, des_eusilc , type_thresh= "relq", thresh = TRUE) # poverty threshold equal to 0.6 times the mean svywatts(~eqincome, des_eusilc , type_thresh= "relm" , thresh = TRUE) # using svrep.design: # poverty threshold fixed svywatts(~eqincome, des_eusilc_rep , abs_thresh=10000) # poverty threshold equal to arpt svywatts(~eqincome, des_eusilc_rep , type_thresh= "relq", thresh = TRUE) # poverty threshold equal to 0.6 times the mean svywatts(~eqincome, des_eusilc_rep , type_thresh= "relm" , thresh = TRUE) ## Not run: # 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 ) # filter positive incomes dbd_eusilc <- subset( dbd_eusilc , eqincome > 0 ) # poverty threshold fixed svywatts(~eqincome, dbd_eusilc , abs_thresh=10000) # poverty threshold equal to arpt svywatts(~eqincome, dbd_eusilc , type_thresh= "relq", thresh = TRUE) # poverty threshold equal to 0.6 times the mean svywatts(~eqincome, dbd_eusilc , type_thresh= "relm" , thresh = TRUE) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)
Estimate the Watts (1968) poverty measure and its components
svywattsdec(formula, design, ...) ## S3 method for class 'survey.design' svywattsdec( formula, design, type_thresh = "abs", abs_thresh = NULL, percent = 0.6, quantiles = 0.5, na.rm = FALSE, thresh = FALSE, ... ) ## S3 method for class 'svyrep.design' svywattsdec( formula, design, type_thresh = "abs", abs_thresh = NULL, percent = 0.6, quantiles = 0.5, na.rm = FALSE, thresh = FALSE, return.replicates = FALSE, ... ) ## S3 method for class 'DBIsvydesign' svywattsdec(formula, design, ...)svywattsdec(formula, design, ...) ## S3 method for class 'survey.design' svywattsdec( formula, design, type_thresh = "abs", abs_thresh = NULL, percent = 0.6, quantiles = 0.5, na.rm = FALSE, thresh = FALSE, ... ) ## S3 method for class 'svyrep.design' svywattsdec( formula, design, type_thresh = "abs", abs_thresh = NULL, percent = 0.6, quantiles = 0.5, na.rm = FALSE, thresh = FALSE, return.replicates = FALSE, ... ) ## S3 method for class 'DBIsvydesign' svywattsdec(formula, design, ...)
formula |
a formula specifying the income variable |
design |
a design object of class |
... |
additional arguments. Currently not used. |
type_thresh |
type of poverty threshold. If "abs" the threshold is fixed and given the value of abs_thresh; if "relq" it is given by percent times the quantile; if "relm" it is percent times the mean. |
abs_thresh |
poverty threshold value if type_thresh is "abs" |
percent |
the multiple of the the quantile or mean used in the poverty threshold definition |
quantiles |
the quantile used used in the poverty threshold definition |
na.rm |
Should cases with missing values be dropped? |
thresh |
return the poverty threshold value |
return.replicates |
Return the replicate estimates? |
you must run the convey_prep function on your survey design object immediately after creating it with the svydesign or svrepdesign function.
For the svywatts and svywattsdec functions, zeroes and negative numbers in the analysis domain cause an error because of the logarithm function in the definition of this poverty measure. However, zeroes and negative values in the full survey design that are outside of the domain of analysis are valid to calculate the poverty threshold because zeroes and negatives are not a problem for computing quantiles (used when type_thresh = "relq") or means (used when type_thresh = "relm") . Missing values are treated differently. NA values anywhere in the full survey design (not only the subset, or the domain of analysis) will cause these quantiles and means to return NA results. To ignore NA values throughout, set na.rm = TRUE.
Object of class "cvydstat", with estimates for the Watts index, FGT(0), Watts Poverty Gap Ratio, and Theil(poor incomes) with a "var" attribute giving the variance-covariance matrix.
A "statistic" attribute giving the name of the statistic.
Guilherme Jacob, Djalma Pessoa, and Anthony Damico
McKinley L. Blackburn (1989). Poverty measurement: an index related to a Theil measure of inequality. Journal of Business & Economic Statistics, Vol.7, No.4, pp. 475-481, DOI doi:10.1080/07350015.1989.10509760.
Satya R. Chakravarty, Joseph Deutsch and Jacques Silber (2008). On the Watts multidimensional poverty index and its decomposition. World Development, Vol.36, No.6, pp.1067-1077.
Harold W. Watts (1968). An economic definition of poverty. Institute For Research on Poverty Discussion Papers, n.5. University of Wisconsin. URL https://www.irp.wisc.edu/publications/dps/pdfs/dp568.pdf.
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 ) # replicate-weighted design des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" ) des_eusilc_rep <- convey_prep( des_eusilc_rep ) # filter positive incomes des_eusilc <- subset( des_eusilc , eqincome > 0 ) des_eusilc_rep <- subset( des_eusilc_rep , eqincome > 0 ) # absolute poverty threshold svywattsdec(~eqincome, des_eusilc, abs_thresh=10000) # poverty threshold equal to arpt svywattsdec(~eqincome, des_eusilc, type_thresh= "relq" , thresh = TRUE) # poverty threshold equal to 0.6 times the mean svywattsdec(~eqincome, des_eusilc, type_thresh= "relm" , thresh = TRUE) # using svrep.design: # absolute poverty threshold svywattsdec(~eqincome, des_eusilc_rep, abs_thresh=10000) # poverty threshold equal to arpt svywattsdec(~eqincome, des_eusilc_rep, type_thresh= "relq" , thresh = TRUE) # poverty threshold equal to 0.6 times the mean svywattsdec(~eqincome, des_eusilc_rep, type_thresh= "relm" , thresh = TRUE) ## Not run: # 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 ) dbd_eusilc <- subset( dbd_eusilc , eqincome > 0 ) # absolute poverty threshold svywattsdec(~eqincome, dbd_eusilc, abs_thresh=10000) # poverty threshold equal to arpt svywattsdec(~eqincome, dbd_eusilc, type_thresh= "relq" , thresh = TRUE) # poverty threshold equal to 0.6 times the mean svywattsdec(~eqincome, dbd_eusilc, type_thresh= "relm" , thresh = TRUE) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)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 ) # filter positive incomes des_eusilc <- subset( des_eusilc , eqincome > 0 ) des_eusilc_rep <- subset( des_eusilc_rep , eqincome > 0 ) # absolute poverty threshold svywattsdec(~eqincome, des_eusilc, abs_thresh=10000) # poverty threshold equal to arpt svywattsdec(~eqincome, des_eusilc, type_thresh= "relq" , thresh = TRUE) # poverty threshold equal to 0.6 times the mean svywattsdec(~eqincome, des_eusilc, type_thresh= "relm" , thresh = TRUE) # using svrep.design: # absolute poverty threshold svywattsdec(~eqincome, des_eusilc_rep, abs_thresh=10000) # poverty threshold equal to arpt svywattsdec(~eqincome, des_eusilc_rep, type_thresh= "relq" , thresh = TRUE) # poverty threshold equal to 0.6 times the mean svywattsdec(~eqincome, des_eusilc_rep, type_thresh= "relm" , thresh = TRUE) ## Not run: # 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 ) dbd_eusilc <- subset( dbd_eusilc , eqincome > 0 ) # absolute poverty threshold svywattsdec(~eqincome, dbd_eusilc, abs_thresh=10000) # poverty threshold equal to arpt svywattsdec(~eqincome, dbd_eusilc, type_thresh= "relq" , thresh = TRUE) # poverty threshold equal to 0.6 times the mean svywattsdec(~eqincome, dbd_eusilc, type_thresh= "relm" , thresh = TRUE) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)
Estimate the Zenga index, a measure of inequality
svyzenga(formula, design, ...) ## S3 method for class 'survey.design' svyzenga( formula, design, na.rm = FALSE, deff = FALSE, linearized = FALSE, influence = FALSE, ... ) ## S3 method for class 'svyrep.design' svyzenga( formula, design, na.rm = FALSE, deff = FALSE, linearized = FALSE, return.replicates = FALSE, ... ) ## S3 method for class 'DBIsvydesign' svyzenga(formula, design, ...)svyzenga(formula, design, ...) ## S3 method for class 'survey.design' svyzenga( formula, design, na.rm = FALSE, deff = FALSE, linearized = FALSE, influence = FALSE, ... ) ## S3 method for class 'svyrep.design' svyzenga( formula, design, na.rm = FALSE, deff = FALSE, linearized = FALSE, return.replicates = FALSE, ... ) ## S3 method for class 'DBIsvydesign' svyzenga(formula, design, ...)
formula |
a formula specifying the income variable |
design |
a design object of class |
... |
future expansion |
na.rm |
Should cases with missing values be dropped? |
deff |
Return the design effect (see |
linearized |
Should a matrix of linearized variables be returned |
influence |
Should a matrix of (weighted) influence functions be returned? (for compatibility with |
return.replicates |
Return the replicate estimates? |
you must run the convey_prep function on your survey design object immediately after creating it with the svydesign or svrepdesign function.
Object of class "cvystat", which are vectors with a "var" attribute giving the variance and a "statistic" attribute giving the name of the statistic.
Djalma Pessoa, Guilherme Jacob, and Anthony Damico
Lucio Barabesi, Giancarlo Diana and Pier Francesco Perri (2016). Linearization of inequality indices in the design-based framework. Statistics, 50(5), 1161-1172. DOI doi:10.1080/02331888.2015.1135924.
Matti Langel and Yves Tille (2012). Inference by linearization for Zenga's new inequality index: a comparison with the Gini index. Metrika, 75, 1093-1110. DOI doi:10.1007/s00184-011-0369-1.
Matti Langel (2012). Measuring inequality in finite population sampling. PhD thesis: Universite de Neuchatel, URL https://doc.rero.ch/record/29204/files/00002252.pdf.
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) svyzenga( ~eqincome , design = des_eusilc ) # replicate-weighted design des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" ) des_eusilc_rep <- convey_prep(des_eusilc_rep) svyzenga( ~eqincome , design = des_eusilc_rep ) ## Not run: # linearized design using a variable with missings svyzenga( ~ py010n , design = des_eusilc ) svyzenga( ~ py010n , design = des_eusilc , na.rm = TRUE ) # replicate-weighted design using a variable with missings svyzenga( ~ py010n , design = des_eusilc_rep ) svyzenga( ~ 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 ) svyzenga( ~ eqincome , design = dbd_eusilc ) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)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) svyzenga( ~eqincome , design = des_eusilc ) # replicate-weighted design des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" ) des_eusilc_rep <- convey_prep(des_eusilc_rep) svyzenga( ~eqincome , design = des_eusilc_rep ) ## Not run: # linearized design using a variable with missings svyzenga( ~ py010n , design = des_eusilc ) svyzenga( ~ py010n , design = des_eusilc , na.rm = TRUE ) # replicate-weighted design using a variable with missings svyzenga( ~ py010n , design = des_eusilc_rep ) svyzenga( ~ 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 ) svyzenga( ~ eqincome , design = dbd_eusilc ) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)