Generalized linearization of a smooth function of survey statistics

Description

Generalized linearization of a smooth function of survey statistics

Usage

contrastinf(exprlist, infunlist)

Arguments

Details

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.

Value

a list with two components: values - the estimate value and lin - the linearized variable

Author(s)

Djalma Pessoa, Guilherme Jacob, and Anthony Damico

References

Guillaume Osier (2009). Variance estimation for complex indicators of poverty and inequality. Journal of the European Survey Research Association, Vol.3, No.3, pp. 167-195, ISSN 1864-3361, URL https://ojs.ub.uni-konstanz.de/srm/article/view/369.

See Also

svyqsr

Examples

library(survey)
library(laeken)
data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) )

# linearized design
des_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 ,  weights = ~rb050 , data = eusilc )
des_eusilc <- convey_prep(des_eusilc)

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))

prepare svydesign and svyrep.design objects for the convey package

Description

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

Usage

convey_prep(design)

Arguments

Details

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.

Value

the same survey object with a full_design attribute as the storage space for the unsubsetted survey design

Author(s)

Djalma Pessoa, Anthony Damico, and Guilherme Jacob

Examples

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 # # #

Estimate the derivative of the cdf function using kernel estimator

Description

computes the derivative of a function in a point using kernel estimation

Usage

densfun(formula, design, x, h = NULL, FUN = "F", na.rm = FALSE, ...)

Arguments

Value

the value of the derivative at x

Author(s)

Djalma Pessoa and Anthony Damico

Examples

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 )

Computes the bandwidth needed to compute the derivative of the cdf function

Description

Using the whole sample, computes the bandwith used to get the linearized variable

Usage

h_fun(incvar, w)

Arguments

Value

value of the bandwidth

Author(s)

Djalma Pessoa, Guilherme Jacob, and Anthony Damico

Linearization of the cumulative distribution function (cdf) of a variable

Description

Computes the linearized variable of the cdf function in a point.

Usage

icdf(formula, design, x, na.rm = FALSE, ...)

Arguments

Value

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

Author(s)

Djalma Pessoa and Anthony Damico

References

Guillaume Osier (2009). Variance estimation for complex indicators of poverty and inequality. Journal of the European Survey Research Association, Vol.3, No.3, pp. 167-195, ISSN 1864-3361, URL https://ojs.ub.uni-konstanz.de/srm/article/view/369. Jean-Claude Deville (1999). Variance estimation for complex statistics and estimators: linearization and residual techniques. Survey Methodology, 25, 193-203, URL https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X19990024882.

See Also

svyarpr

Examples

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 )

At-risk-of-poverty rate

Description

Estimate the proportion of persons with income below the at-risk-of-poverty threshold.

Usage

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, ...)

Arguments

Details

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

Value

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

Author(s)

Djalma Pessoa and Anthony Damico

References

Guillaume Osier (2009). Variance estimation for complex indicators of poverty and inequality. Journal of the European Survey Research Association, Vol.3, No.3, pp. 167-195, ISSN 1864-3361, URL https://ojs.ub.uni-konstanz.de/srm/article/view/369.

Jean-Claude Deville (1999). Variance estimation for complex statistics and estimators: linearization and residual techniques. Survey Methodology, 25, 193-203, URL https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X19990024882.

See Also

svyarpt

Examples

library(survey)
library(laeken)
data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) )

# 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)

At-risk-of-poverty threshold

Description

The standard definition is to use 60% of the median income.

Usage

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, ...)

Arguments

Details

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

Value

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

Author(s)

Djalma Pessoa and Anthony Damico

References

Guillaume Osier (2009). Variance estimation for complex indicators of poverty and inequality. Journal of the European Survey Research Association, Vol.3, No.3, pp. 167-195, ISSN 1864-3361, URL https://ojs.ub.uni-konstanz.de/srm/article/view/369.

Jean-Claude Deville (1999). Variance estimation for complex statistics and estimators: linearization and residual techniques. Survey Methodology, 25, 193-203, URL https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X19990024882.

See Also

svyarpr

Examples

library(survey)
library(laeken)
data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) )

# linearized design

des_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 ,  weights = ~rb050 , data = eusilc )
des_eusilc <- convey_prep( des_eusilc )
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)

Atkinson index

Description

Estimate the Atkinson index, an inequality measure

Usage

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, ...)

Arguments

Details

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

Value

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

Author(s)

Guilherme Jacob, Djalma Pessoa and Anthony Damico

References

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.

See Also

svygei

Examples

library(survey)
library(laeken)
data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) )

# linearized design
des_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 ,  weights = ~rb050 , data = eusilc )
des_eusilc <- convey_prep(des_eusilc)

# 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)

FGT measure of poverty

Description

Estimate the FGT measure.

Usage

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, ...)

Arguments

Details

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.

Value

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

Author(s)

Djalma Pessoa, Anthony Damico, and Guilherme Jacob

References

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.

See Also

svyarpt

Examples

library(survey)
library(laeken)
data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) )

# linearized design

des_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 ,  weights = ~rb050 , data = eusilc )
des_eusilc <- convey_prep( des_eusilc )

# 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)

FGT indices decomposition

Description

Estimate the Foster et al. (1984) poverty class and its components

Usage

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, ...)

Arguments

Details

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

Value

Object of class "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.

Author(s)

Guilherme Jacob, Djalma Pessoa and Anthony Damico

References

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.

See Also

svyfgt,svyfgt,svyfgt

Examples

library(survey)
library(laeken)
data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) )

# linearized design

des_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 ,  weights = ~rb050 , data = eusilc )
des_eusilc <- convey_prep( des_eusilc )

# 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)

Generalized entropy index

Description

Estimate the generalized entropy index, a measure of inequality

Usage

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, ...)

Arguments

Details

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.

Value

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

Author(s)

Guilherme Jacob, Djalma Pessoa and Anthony Damico

References

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.

See Also

svyatk

Examples

library(survey)
library(laeken)
data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) )

# linearized design
des_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 ,  weights = ~rb050 , data = eusilc )
des_eusilc <- convey_prep(des_eusilc)

# 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)

Generalized Entropy Index Decomposition

Description

Estimates the group decomposition of the generalized entropy index

Usage

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, ...)

Arguments

Details

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.

Value

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.

Author(s)

Guilherme Jacob, Djalma Pessoa and Anthony Damico

References

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.

See Also

svygei

Examples

library(survey)
library(laeken)
data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) )

# linearized design
des_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 ,  weights = ~rb050 , data = eusilc )
des_eusilc <- convey_prep(des_eusilc)

# 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)

Gini coefficient

Description

Estimate the Gini coefficient, an inequality measure

Usage

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, ...)

Arguments

Details

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

Value

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

Author(s)

Djalma Pessoa, Guilherme Jacob, and Anthony Damico

References

Guillaume Osier (2009). Variance estimation for complex indicators of poverty and inequality. Journal of the European Survey Research Association, Vol.3, No.3, pp. 167-195, ISSN 1864-3361, URL https://ojs.ub.uni-konstanz.de/srm/article/view/369.

Jean-Claude Deville (1999). Variance estimation for complex statistics and estimators: linearization and residual techniques. Survey Methodology, 25, 193-203, URL https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X19990024882.

See Also

svyarpr

Examples

library(survey)
library(laeken)
data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) )

# linearized design
des_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 ,  weights = ~rb050 , data = eusilc )
des_eusilc <- convey_prep(des_eusilc)

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)

Linearization of the gender pay (wage) gap

Description

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.

Usage

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, ...)

Arguments

Details

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

Value

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

Author(s)

Djalma Pessoa and Anthony Damico

References

Guillaume Osier (2009). Variance estimation for complex indicators of poverty and inequality. Journal of the European Survey Research Association, Vol.3, No.3, pp. 167-195, ISSN 1864-3361, URL https://ojs.ub.uni-konstanz.de/srm/article/view/369.

Jean-Claude Deville (1999). Variance estimation for complex statistics and estimators: linearization and residual techniques. Survey Methodology, 25, 193-203, URL https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X19990024882.

See Also

svyarpt

Examples

library(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)

Linearization of a variable quantile

Description

Computes the linearized variable of a quantile of variable.

Usage

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, ...)

Arguments

Details

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

Value

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

Author(s)

Djalma Pessoa and Anthony Damico

References

Guillaume Osier (2009). Variance estimation for complex indicators of poverty and inequality. Journal of the European Survey Research Association, Vol.3, No.3, pp. 167-195, ISSN 1864-3361, URL https://ojs.ub.uni-konstanz.de/srm/article/view/369.

Jean-Claude Deville (1999). Variance estimation for complex statistics and estimators: linearization and residual techniques. Survey Methodology, 25, 193-203, URL https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X19990024882.

See Also

svyarpr

Examples

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)

Linearization of the total below a quantile

Description

Computes the linearized variable of the total in the lower tail of the distribution of a variable.

Usage

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, ...)

Arguments

Details

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

Value

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

Author(s)

Djalma Pessoa, Guilherme Jacob, and Anthony Damico

References

Guillaume Osier (2009). Variance estimation for complex indicators of poverty and inequality. Journal of the European Survey Research Association, Vol.3, No.3, pp. 167-195, ISSN 1864-3361, URL https://ojs.ub.uni-konstanz.de/srm/article/view/369.

Jean-Claude Deville (1999). Variance estimation for complex statistics and estimators: linearization and residual techniques. Survey Methodology, 25, 193-203, URL https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X19990024882.

See Also

svyarpr

Examples

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)

J-divergence measure

Description

Estimate the J-divergence measure, an entropy-based measure of inequality

Usage

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, ...)

Arguments

Details

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.

Value

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

Author(s)

Guilherme Jacob, Djalma Pessoa, and Anthony Damico

References

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.

See Also

svygei

Examples

library(survey)
library(laeken)
data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) )

# linearized design
des_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 ,  weights = ~rb050 , data = eusilc )
des_eusilc <- convey_prep(des_eusilc)

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)

J-Divergence Decomposition

Description

Estimates the group decomposition of the generalized entropy index

Usage

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, ...)

Arguments

Details

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.

Value

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.

Author(s)

Guilherme Jacob, Djalma Pessoa, and Anthony Damico

References

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.

See Also

svyjdiv

Examples

library(survey)
library(laeken)
data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) )

# linearized design
des_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 ,  weights = ~rb050 , data = eusilc )
des_eusilc <- convey_prep(des_eusilc)

# 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)

Lorenz curve

Description

Estimate the Lorenz curve, an inequality graph

Usage

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, ...)

Arguments

Details

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.

Value

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.

Author(s)

Guilherme Jacob, Djalma Pessoa and Anthony Damico

References

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.

See Also

oldsvyquantile

Examples

library(survey)
library(laeken)
data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) )

# linearized design
des_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 ,  weights = ~rb050 , data = eusilc )
des_eusilc <- convey_prep( des_eusilc )
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)

Relative median poverty gap

Description

Estimate the median of incomes less than the at-risk-of-poverty threshold (arpt).

Usage

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, ...)

Arguments

Details

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

Value

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

Author(s)

Djalma Pessoa and Anthony Damico

References

Guillaume Osier (2009). Variance estimation for complex indicators of poverty and inequality. Journal of the European Survey Research Association, Vol.3, No.3, pp. 167-195, ISSN 1864-3361, URL https://ojs.ub.uni-konstanz.de/srm/article/view/369.

Jean-Claude Deville (1999). Variance estimation for complex statistics and estimators: linearization and residual techniques. Survey Methodology, 25, 193-203, URL https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X19990024882.

See Also

svyarpt

Examples

library(survey)
library(laeken)
data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) )

# 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)

Quintile Share Ratio

Description

Estimate ratio of the total income received by the highest earners to the total income received by lowest earners, defaulting to 20

Usage

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, ...)

Arguments

Details

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

Value

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

Author(s)

Djalma Pessoa and Anthony Damico

References

Guillaume Osier (2009). Variance estimation for complex indicators of poverty and inequality. Journal of the European Survey Research Association, Vol.3, No.3, pp. 167-195, ISSN 1864-3361, URL https://ojs.ub.uni-konstanz.de/srm/article/view/369.

Jean-Claude Deville (1999). Variance estimation for complex statistics and estimators: linearization and residual techniques. Survey Methodology, 25, 193-203, URL https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X19990024882.

See Also

svyarpt

Examples

library(survey)
library(laeken)
data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) )

# 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)

Richness measures

Description

Estimate Peichl, Schaefer and Scheicher (2010) richness measures.

Usage

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, ...)

Arguments

Details

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

Value

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

Author(s)

Guilherme Jacob, Djalma Pessoa and Anthony Damico

References

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.

See Also

svyfgt

Examples

library(survey)
library(laeken)
data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) )

# linearized design

des_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 ,  weights = ~rb050 , data = eusilc )
des_eusilc <- convey_prep( des_eusilc )

# 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)

Relative median income ratio

Description

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

Usage

svyrmir(formula, design, ...)

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

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

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

Arguments

Details

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

Value

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

Author(s)

Djalma Pessoa and Anthony Damico

References

Guillaume Osier (2009). Variance estimation for complex indicators of poverty and inequality. Journal of the European Survey Research Association, Vol.3, No.3, pp. 167-195, ISSN 1864-3361, URL https://ojs.ub.uni-konstanz.de/srm/article/view/369.

Jean-Claude Deville (1999). Variance estimation for complex statistics and estimators: linearization and residual techniques. Survey Methodology, 25, 193-203, URL https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X19990024882.

See Also

svyarpt

Examples

library(survey)
library(laeken)
data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) )

# missing completely at random, missingness rate = .20
ind_miss <- rbinom(nrow(eusilc), 1, .20 )
eusilc$eqincome_miss <- eusilc$eqincome
is.na(eusilc$eqincome_miss)<- ind_miss==1

# linearized design
des_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 ,  weights = ~rb050 , data = eusilc )
des_eusilc <- convey_prep(des_eusilc)

svyrmir( ~eqincome , design = des_eusilc , age = ~age, med_old = TRUE )

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

svyrmir( ~eqincome , design = des_eusilc_rep, age= ~age, med_old = TRUE )

## Not run: 

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

# database-backed design
library(RSQLite)
library(DBI)
dbfile <- tempfile()
conn <- dbConnect( RSQLite::SQLite() , dbfile )
dbWriteTable( conn , 'eusilc' , eusilc )

dbd_eusilc <-
	svydesign(
		ids = ~rb030 ,
		strata = ~db040 ,
		weights = ~rb050 ,
		data="eusilc",
		dbname=dbfile,
		dbtype="SQLite"
	)

dbd_eusilc <- convey_prep( dbd_eusilc )

svyrmir( ~eqincome , design = dbd_eusilc , age = ~age )

dbRemoveTable( conn , 'eusilc' )

dbDisconnect( conn , shutdown = TRUE )


## End(Not run)

Relative median poverty gap

Description

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

Usage

svyrmpg(formula, design, ...)

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

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

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

Arguments

Details

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

Value

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

Author(s)

Djalma Pessoa and Anthony Damico

References

Guillaume Osier (2009). Variance estimation for complex indicators of poverty and inequality. Journal of the European Survey Research Association, Vol.3, No.3, pp. 167-195, ISSN 1864-3361, URL https://ojs.ub.uni-konstanz.de/srm/article/view/369.

Jean-Claude Deville (1999). Variance estimation for complex statistics and estimators: linearization and residual techniques. Survey Methodology, 25, 193-203, URL https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X19990024882.

See Also

svyarpt

Examples

library(survey)
library(laeken)
data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) )

# 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)

Watts measure of poverty

Description

Estimate the Watts measure for the cases: alpha=0 headcount ratio and alpha=1 poverty gap index.

Usage

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, ...)

Arguments

Details

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.

Value

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

Author(s)

Guilherme Jacob, Djalma Pessoa, and Anthony Damico

References

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.

See Also

svyarpt

Examples

library(survey)
library(laeken)
data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) )

# linearized design

des_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 ,  weights = ~rb050 , data = eusilc )
des_eusilc <- convey_prep( des_eusilc )

# 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)

Watts poverty index decomposition

Description

Estimate the Watts (1968) poverty measure and its components

Usage

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, ...)

Arguments

Details

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.

Value

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.

Author(s)

Guilherme Jacob, Djalma Pessoa, and Anthony Damico

References

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.

See Also

svywatts,svyfgt,svyfgt

Examples

library(survey)
library(laeken)
data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) )

# linearized design
des_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 ,  weights = ~rb050 , data = eusilc )
des_eusilc <- convey_prep( des_eusilc )

# 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)

Zenga index

Description

Estimate the Zenga index, a measure of inequality

Usage

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, ...)

Arguments

Details

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

Value

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

Author(s)

Djalma Pessoa, Guilherme Jacob, and Anthony Damico

References

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.

See Also

svygini

Examples

library(survey)
library(laeken)
data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) )

# linearized design
des_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 ,  weights = ~rb050 , data = eusilc )
des_eusilc <- convey_prep(des_eusilc)

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)