Luca Sartore and Kelly Toppin
Maintainer: Luca Sartore
Calibration forces the weighted estimates of calibration variables to match known totals. This improves the quality of the design-weighted estimates. It is used to adjust for non-response and/or under-coverage. The commonly used methods of calibration produce non-integer weights. In cases where weighted estimates must be integers, one must “integerize” the calibrated weights. However, this procedure often produces final weights that are very different for the “sample” weights. To counter this problem, the inca package provides specific functions for rounding real weights to integers, and performing an integer programming algorithm for calibration problems with integer weights.
For a complete list of exported functions, use
library(help = "inca") once the inca package is installed (see the
inst/INSTALL.md file for a detailed description of the setup process).
library(inca) set.seed(0) w <- rpois(150, 4) data <- matrix(rbinom(150000, 1, .3) * rpois(150000, 4), 1000, 150) y <- data %*% w w <- runif(150, 0, 7.5) print(sum(abs(y - data %*% w))) cw <- intcalibrate(w, ~. + 0, y, lower = 1, upper = 7, sparse = TRUE, data = data) print(sum(abs(y - data %*% cw))) barplot(table(cw), main = "Calibrated integer weights")
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