Calibrate weights for bootstrap replicates by using iterative proportional updating to match population totals on various household and personal levels.
recalib( dat, hid = attr(dat, "hid"), weights = attr(dat, "weights"), b.rep = attr(dat, "b.rep"), period = attr(dat, "period"), conP.var = NULL, conH.var = NULL, epsP = 0.01, epsH = 0.02, ... )
| dat | either data.frame or data.table containing the sample survey for various periods. |
|---|---|
| hid | character specifying the name of the column in |
| weights | character specifying the name of the column in |
| b.rep | character specifying the names of the columns in |
| period | character specifying the name of the column in |
| conP.var | character vector containig person-specific variables to which
weights should be calibrated or a list of such character vectors.
Contingency tables for the population are calculated per |
| conH.var | character vector containig household-specific variables to
which weights should be calibrated or a list of such character vectors.
Contingency tables for the population are calculated per |
| epsP | numeric value specifying the convergence limit for |
| epsH | numeric value specifying the convergence limit for |
| ... | additional arguments passed on to function |
Returns a data.table containing the survey data as well as the calibrated weights for the bootstrap replicates. The original bootstrap replicates are overwritten by the calibrated weights. If calibration of a bootstrap replicate does not converge the bootsrap weight is not returned and numeration of the returned bootstrap weights is reduced by one.
recalib takes survey data (dat) containing the bootstrap replicates
generated by draw.bootstrap and calibrates weights for each bootstrap
replication according to population totals for person- or household-specific
variables.
dat must be household data where household members correspond to multiple
rows with the same household identifier. The data should at least containt
the following columns:
Column indicating the sample period;
Column indicating the household ID;
Column containing the household sample weights;
Columns which contain the bootstrap replicates (see output of draw.bootstrap);
Columns indicating person- or household-specific variables for which sample weight should be adjusted.
For each period and each variable in conP.var and/or conH.var contingency
tables are estimated to get margin totals on personal- and/or
household-specific variables in the population.
Afterwards the bootstrap replicates are multiplied with the original sample
weight and the resulting product ist then adjusted using ipf() to match the
previously calcualted contingency tables. In this process the columns of the
bootstrap replicates are overwritten by the calibrated weights.
ipf() for more information on iterative
proportional fitting.
Johannes Gussenbauer, Alexander Kowarik, Statistics Austria
if (FALSE) { eusilc <- demo.eusilc(prettyNames = TRUE) dat_boot <- draw.bootstrap(eusilc, REP = 10, hid = "hid", weights = "pWeight", strata = "region", period = "year") # calibrate weight for bootstrap replicates dat_boot_calib <- recalib(dat_boot, conP.var = "gender", conH.var = "region", verbose = TRUE) # calibrate on other variables dat_boot_calib <- recalib(dat_boot, conP.var = c("gender", "age"), conH.var = c("region", "hsize"), verbose = TRUE) # supply contingency tables directly conP <- xtabs(pWeight ~ age + gender + year, data = eusilc) conH <- xtabs(pWeight ~ hsize + region + year, data = eusilc[!duplicated(paste(db030,year))]) dat_boot_calib <- recalib(dat_boot, conP.var = NULL, conH.var = NULL, conP = list(conP), conH = list(conH), verbose = TRUE) }