simPop (version 0.6.0)

ipu2: Iterative Proportional Updating

Description

Adjust sampling weights to given totals based on household-level and/or individual level constraints.

Usage

ipu2(dat, hid = NULL, conP = NULL, conH = NULL, epsP = 1e-06,
  epsH = 0.01, verbose = FALSE, w = NULL, bound = 4, maxIter = 200,
  meanHH = TRUE, returnNA = TRUE)

Arguments

dat

a data.table containing household ids (optionally), base weights (optionally), household and/or personal level variables (numerical or categorical) that should be fitted.

hid

character vector specifying the variable containing household-ids within dat or NULL if such a variable does not exist.

conP

list or (partly) named list defining the constraints on person level. The list elements are contingency tables in array representation with dimnames corresponding to the names of the relevant calibration variables in dat. If a numerical variable is to be calibrated, the respective list element has to be named with the name of that numerical variable. Otherwise the list element shoud NOT be named.

conH

list or (partly) named list defining the constraints on household level. The list elements are contingency tables in array representation with dimnames corresponding to the names of the relevant calibration variables in dat. If a numerical variable is to be calibrated, the respective list element has to be named with the name of that numerical variable. Otherwise the list element shoud NOT be named.

epsP

numeric value or list (of numeric values and/or arrays) specifying the convergence limit(s) for conP. The list can contain numeric values and/or arrays which must appear in the same order as the corresponding constraints in conP. Also, an array must have the same dimensions and dimnames as the corresponding constraint in conP.

epsH

numeric value or list (of numeric values and/or arrays) specifying the convergence limit(s) for conH. The list can contain numeric values and/or arrays which must appear in the same order as the corresponding constraints in conH. Also, an array must have the same dimensions and dimnames as the corresponding constraint in conH.

verbose

if TRUE, some progress information will be printed.

w

character vector specifying the variable containing the base weights within dat or NULL if such a variable does not exist. In the latter case, every observation in dat is assigned a starting weight of 1.

bound

numeric value specifying the multiplier for determining the weight trimming boundary if the change of the base weights should be restricted, i.e. if the weights should stay between 1/bound*w and bound*w.

maxIter

numeric value specifying the maximum number of iterations that should be performed.

meanHH

if TRUE, every person in a household is assigned the mean of the person weights corresponding to the household.

returnNA

if TRUE, the calibrated weight will be set to NA in case of no convergence.

Value

The function will return the input data dat with the calibrated weights calibWeight as an additional column.

See Also

ipu

Examples

# NOT RUN {
nrowInp <- 1000
inp <- as.data.table(matrix(0, nrow=nrowInp, ncol=6))
colnames(inp) <- c("pid","hhid","income","agegroup","gender","region")
inp[,pid:=.I]
inp$hhid <- round(seq(from=1, to=round(nrowInp/3), length.out=nrowInp))
set.seed(223344)
# 4 age groups
inp$agegroup <- sample(c(1:4),nrowInp,replace=TRUE, prob=c(0.15,0.3,0.35,0.2))
# 2 gender groups
inp$gender <- sample(c(1,2),nrowInp,replace=TRUE)
# 3 regions
inp$region <- sample(c(1:3),nrowInp,replace=TRUE, prob=c(0.2,0.5,0.3))
inp[,region:=region[1], by=hhid]
# income
inp$income <- round(EnvStats::rpareto(nrowInp, 1000, 3))
inp[agegroup==1,income:=0]
# hhincome
inp[,hhinc:=sum(income),by=list(hhid)]

# constraints on person level
conP1 <- array(c(239741,601386,360193,480699,1199962,718892,560069,1399490,840041,
  320257,799359,479911),c(3,4))
dimnames(conP1) <- list(region=c("1", "2", "3"),agegroup=c("1", "2", "3", "4"))

conP2 <- array(c(800171,1999596,1198754,800595,2000601,1200283),c(3,2))
dimnames(conP2) <- list(region=c("1", "2", "3"),gender=c("1", "2"))

conP3 <- array(c(1020162500,2549062073,1528896458,1021294346,
  2551562798,1530314970),c(3,2))
dimnames(conP3) <- list(region=c("1", "2", "3"),gender=c("1", "2"))

# constraints on household level
conH1 <- array(c(2041271296,5104297066,3055724783),3)
dimnames(conH1) <- list(region=c("1", "2", "3"))

conH2 <- array(c(533267,1333931,799469),3)
dimnames(conH2) <- list(region=c("1", "2", "3"))

# array of convergence limits for conP1
epsP1 <- array(rep(c(0.9,rep(0.7,2)),4),c(3,4))
dimnames(epsP1) <- dimnames(conP1)

# example for base weights assuming a simple random sample of households stratified per region
inp[,xx:=1]
inp[, regSamp:=sum(xx),by=region]
reg <- cbind(region=1:3,regPop=addmargins(conP2,2)[1:3,"Sum"])
inp <- merge(inp,reg,by="region",sort=FALSE)
inp[,baseWeight:=regPop/regSamp]
inp[,xx:=NULL]

res1 <- ipu2(dat=inp, hid="hhid", w=NULL, conP=list(conP1, conP2, income=conP3),
     conH=list(hhinc=conH1, conH2),
     epsP=0.09, epsH=0.05, verbose=TRUE, bound=NULL, maxIter=200, meanHH=TRUE)

# with array epsP1
res2 <- ipu2(dat=inp, hid="hhid", w=NULL, conP=list(conP1, conP2, income=conP3),
    conH=list(hhinc=conH1, conH2),
    epsP=list(epsP1,0.07,0.07), epsH=0.05, verbose=TRUE, bound=NULL,
    maxIter=200, meanHH=TRUE)

# with base weights and bound
res3 <- ipu2(dat=inp, hid="hhid", w="baseWeight", conP=list(conP1, conP2),
    conH=list(hhinc=conH1, conH2),
    epsP=list(epsP1,0.05), epsH=1e-2, verbose=TRUE, bound=4, maxIter=200, meanHH=TRUE)
# }