R/svyarpt.R

In convey: Income Concentration Analysis with Complex Survey Samples

#' At-risk-of-poverty threshold
#'
#' The standard definition is to use 60\% of the median income.
#'
#' @param formula a formula specifying the income variable
#' @param design a design object of class \code{survey.design} or class \code{svyrep.design} from the \code{survey} library.
#' @param quantiles income quantile quantiles, usually .50 (median)
#' @param percent fraction of the quantile, usually .60
#' @param na.rm Should cases with missing values be dropped?
#' @param ... arguments passed on to `survey::oldsvyquantile`
#'
#' @details you must run the \code{convey_prep} function on your survey design object immediately after creating it with the \code{svydesign} or \code{svrepdesign} function.
#'
#' @return Object of class "\code{cvystat}", which are vectors with a "\code{var}" attribute giving the variance and a "\code{statistic}" attribute giving the name of the statistic.
#'
#' @author Djalma Pessoa and Anthony Damico
#'
#' @seealso \code{\link{svyarpr}}
#'
#' @references Guillaume Osier (2009). Variance estimation for complex indicators
#' of poverty and inequality. \emph{Journal of the European Survey Research
#' Association}, Vol.3, No.3, pp. 167-195,
#' ISSN 1864-3361, URL \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 \url{https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X19990024882}.
#'
#' @keywords survey
#'
#' @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 )
#'
#' \dontrun{
#'
#' # 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 )
#'
#' }
#'
#' @export
svyarpt <-
	function(formula, design, ...) {

		if( length( attr( terms.formula( formula ) , "term.labels" ) ) > 1 ) stop( "convey package functions currently only support one variable in the `formula=` argument" )

		UseMethod("svyarpt", design)

	}

#' @rdname svyarpt
#' @export
svyarpt.survey.design <-
	function(formula, design, quantiles = 0.5, percent = 0.6,  na.rm = FALSE,...) {

		if (is.null(attr(design, "full_design"))) stop("you must run the ?convey_prep function on your linearized survey design object immediately after creating it with the svydesign() function.")

		# if the class of the full_design attribute is just a TRUE, then the design is
		# already the full design.  otherwise, pull the full_design from that attribute.
		if ("logical" %in% class(attr(design, "full_design")))
		full_design <- design else full_design <- attr(design, "full_design")
		incvar <- model.frame(formula, design$variables, na.action = na.pass)[[1]]

		if(na.rm){
			nas<-is.na(incvar)
			design<-design[!nas,]
			if (length(nas) > length(design$prob))
			incvar <- incvar[!nas]
			else incvar[nas] <- 0
		}

		if( is.null( names( design$prob ) ) ) ind <- as.character( seq( length( design$prob ) ) ) else ind <- names(design$prob)

		w <- 1/design$prob

		incvec <- model.frame(formula, full_design$variables, na.action = na.pass)[[1]]

		if(na.rm){
			nas<-is.na(incvec)
			full_design<-full_design[!nas,]
			if (length(nas) > length(full_design$prob))
			incvec <- incvec[!nas]
			else incvec[nas] <- 0
		}

		if( is.null( names( full_design$prob ) ) ) ncom <- as.character( seq( length( full_design$prob ) ) ) else ncom <- names(full_design$prob)

		wf <- 1/full_design$prob
		htot <- h_fun(incvar, w)
		q_alpha <- survey::oldsvyquantile(x = formula, design = design, quantiles = quantiles,
		method = "constant", na.rm = na.rm,...)
		q_alpha <- as.vector(q_alpha)
		rval <- percent * q_alpha
		Fprime <- densfun(formula = formula, design = design, q_alpha, h=htot, FUN = "F", na.rm=na.rm)
		N <- sum(w)
		if (sum(1/design$prob==0) > 0) ID <- 1*(1/design$prob!=0) else
		  ID <- 1 * ( ncom %in% ind )
		linquant<- -(1/(N * Fprime)) * ID*((incvec <= q_alpha) - quantiles)
		lin <- percent * linquant

		variance <- survey::svyrecvar(lin/full_design$prob, full_design$cluster,
		full_design$strata, full_design$fpc, postStrata = full_design$postStrata)

		colnames( variance ) <- rownames( variance ) <-  names( rval ) <- strsplit( as.character( formula )[[2]] , ' \\+ ' )[[1]]
		class(rval) <- c( "cvystat" , "svystat" )
		attr(rval, "var") <- variance
		attr(rval, "statistic") <- "arpt"
		attr(rval, "lin") <- lin
		rval
	}

#' @rdname svyarpt
#' @export
svyarpt.svyrep.design <-
	function(formula, design, quantiles = 0.5, percent = 0.6, na.rm = FALSE, ...) {

		if (is.null(attr(design, "full_design")))
		stop("you must run the ?convey_prep function on your replicate-weighted survey design object immediately after creating it with the svrepdesign() function.")


		# if the class of the full_design attribute is just a TRUE, then the design is
		# already the full design.  otherwise, pull the full_design from that attribute.
		if ("logical" %in% class(attr(design, "full_design"))) full_design <- design else full_design <- attr(design, "full_design")


		df <- model.frame(design)
		incvar <- model.frame(formula, design$variables, na.action = na.pass)[[1]]
		if(na.rm){
			nas<-is.na(incvar)
			design<-design[!nas,]
			df <- model.frame(design)
			incvar <- incvar[!nas]
		}

		w <- weights(design, "sampling")
		quant_val <- computeQuantiles(incvar, w, p = quantiles)
		quant_val <- as.vector(quant_val)
		rval <- percent * quant_val
		ww <- weights(design, "analysis")
		qq <- apply(ww, 2, function(wi) 0.6 * computeQuantiles(incvar, wi, p = quantiles))
		if(anyNA(qq))variance <- NA
		else variance <- survey::svrVar(qq, design$scale, design$rscales, mse = design$mse, coef = rval)

		variance <- as.matrix( variance )

		colnames( variance ) <- rownames( variance ) <-  names( rval ) <- strsplit( as.character( formula )[[2]] , ' \\+ ' )[[1]]
		class(rval) <- c( "cvystat" , "svrepstat" )
		attr(rval, "var") <- variance
		attr(rval, "statistic") <- "arpt"
		rval
	}

#' @rdname svyarpt
#' @export
svyarpt.DBIsvydesign <-
	function (formula, design, ...)
	{

		if (!( "logical" %in% class(attr(design, "full_design"))) ){

			full_design <- attr( design , "full_design" )

			full_design$variables <-
				getvars(
					formula,
					attr( design , "full_design" )$db$connection,
					attr( design , "full_design" )$db$tablename,
					updates = attr( design , "full_design" )$updates,
					subset = attr( design , "full_design" )$subset
				)

			attr( design , "full_design" ) <- full_design

			rm( full_design )

		}

		design$variables <-
			getvars(
				formula,
				design$db$connection,
				design$db$tablename,
				updates = design$updates,
				subset = design$subset
			)

		NextMethod("svyarpt", design)
	}