# iterative proportional updating adjust sampling weights to given totals based on household-level and/or individual level constraints ```r ipu(inp, con, hid = NULL, eps = 1e-07, verbose = FALSE) ``` ## Arguments - `inp`: a `data.frame` or `data.table` containing household ids (optionally), counts for household and/or personal level attributes that should be fitted. - `con`: named list with each list element holding a constraint total with list-names relating to column-names in `inp`. - `hid`: character vector specifying the variable containing household-ids within `inp` or NULL if such a variable does not exist. - `eps`: number specifiying convergence limit - `verbose`: if TRUE, ipu will print some progress information. ## Examples ```r library(data.table) # basic example inp <- as.data.frame(matrix(0, nrow=8, ncol=6)) colnames(inp) <- c("hhid","hh1","hh2","p1","p2","p3") inp$hhid <- 1:8 inp$hh1[1:3] <- 1 inp$hh2[4:8] <- 1 inp$p1 <- c(1,1,2,1,0,1,2,1) inp$p2 <- c(1,0,1,0,2,1,1,1) inp$p3 <- c(1,1,0,2,1,0,2,0) con <- list(hh1=35, hh2=65, p1=91, p2=65, p3=104) res <- ipu(inp=inp, hid="hhid", con=con, verbose=FALSE) # more sophisticated # load sample and population data data(eusilcS) data(eusilcP) # variable generation and preparation eusilcS$hsize <- factor(eusilcS$hsize) # make sure, factor levels in sample and population match eusilcP$region <- factor(eusilcP$region, levels = levels(eusilcS$db040)) eusilcP$gender <- factor(eusilcP$gender, levels = levels(eusilcS$rb090)) eusilcP$hsize <- factor(eusilcP$hsize , levels = levels(eusilcS$hsize)) # generate input matrix # we want to adjust to variable "db040" (region) as household variables and # variable "rb090" (gender) as individual information library(data.table) samp <- data.table(eusilcS) pop <- data.table(eusilcP) setkeyv(samp, "db030") hh <- samp[!duplicated(samp$db030),] hhpop <- pop[!duplicated(pop$hid),] # reg contains for each region the number of households reg <- data.table(model.matrix(~db040 +0, data=hh)) # hsize contains for each household size the number of households hsize <- data.table(model.matrix(~factor(hsize) +0, data=hh)) # aggregate persons-level characteristics per household # gender contains for each household the number of males and females gender <- data.table(model.matrix(~db030+rb090 +0, data=samp)) setkeyv(gender, "db030") gender <- gender[, lapply(.SD, sum), by = key(gender)] # bind together and use it as input inp <- cbind(reg, hsize, gender) # the totals we want to calibrate to con <- c( as.list(xtabs(rep(1, nrow(hhpop)) ~ hhpop$region)), as.list(xtabs(rep(1, nrow(hhpop)) ~ hhpop$hsize)), as.list(xtabs(rep(1, nrow(eusilcP)) ~ eusilcP$gender)) ) # we need to have the same names as in 'inp' names(con) <- setdiff(names(inp), "db030") # run ipu und check results res <- ipu(inp=inp, hid="db030", con=con, verbose=TRUE) is <- sapply(2:(ncol(res)-1), function(x) { sum(res[,x]*res$weights) }) data.frame(required=unlist(con), is=is) ``` ## Author(s) Bernhard Meindl

ipu function