simPop (version 0.2.15)

calibSample-methods: Calibrate sample weights

Description

Calibrate sample weights according to known marginal population totals. Based on initial sample weights, the so-called g-weights are computed by generalized raking procedures.

Details

The methods return a list containing both the g-weights (slot g_weights) as well as the final weights (slot final_weights) (initial sampling weights adjusted by the g-weights.

References

Deville, J.-C. and Saerndal{Saerndal}, C.-E. (1992) Calibration estimators in survey sampling. Journal of the American Statistical Association, 87(418), 376--382. Deville, J.-C., Saerndal{Saerndal}, C.-E. and Sautory, O. (1993) Generalized raking procedures in survey sampling. Journal of the American Statistical Association, 88(423), 1013--1020.

Examples

data(eusilcS)
eusilcS$agecut <- cut(eusilcS$age, 7)
inp <- specifyInput(data=eusilcS, hhid="db030", hhsize="hsize", strata="db040", weight="db090")

## for simplicity, we are using population data directly from the sample, but you get the idea
totals1 <- tableWt(eusilcS[, c("agecut","rb090")], weights=eusilcS$rb050)
totals2 <- tableWt(eusilcS[, c("rb090","agecut")], weights=eusilcS$rb050)
totals3 <- tableWt(eusilcS[, c("rb090","agecut","db040")], weights=eusilcS$rb050)
totals4 <- tableWt(eusilcS[, c("agecut","db040","rb090")], weights=eusilcS$rb050)

weights1 <- calibSample(inp, totals1)
totals1.df <- as.data.frame(totals1)
weights1.df <- calibSample(inp, totals1.df)
identical(weights1, weights1.df)

# we can also use a data.frame and an optional weight vector as input
df <- as.data.frame(inp@data)
w <- inp@data[[inp@weight]]
weights1.x <- calibSample(df, totals1.df, w=inp@data[[inp@weight]])
identical(weights1, weights1.x)

weights2 <- calibSample(inp, totals2)
totals2.df <- as.data.frame(totals2)
weights2.df <- calibSample(inp, totals2.df)
identical(weights2, weights2.df)

weights3 <- calibSample(inp, totals3)
totals3.df <- as.data.frame(totals3)
weights3.df <- calibSample(inp, totals3.df)
identical(weights3, weights3.df)

weights4 <- calibSample(inp, totals4)
totals4.df <- as.data.frame(totals4)
weights4.df <- calibSample(inp, totals4.df)
identical(weights4, weights4.df)