# calibWeights: Calibrate sample weights In aalfons/laeken: Estimation of Indicators on Social Exclusion and Poverty

## 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.

## Usage

 ```1 2 3``` ```calibWeights(X, d, totals, q = NULL, method = c("raking", "linear", "logit"), bounds = c(0, 10), maxit = 500, tol = 1e-06, eps = .Machine\$double.eps) ```

## Arguments

 `X` a matrix of binary calibration variables (see `calibVars`). `d` a numeric vector giving the initial sample weights. `totals` a numeric vector of population totals corresponding to the calibration variables in `X`. `q` a numeric vector of positive values accounting for heteroscedasticity. Small values reduce the variation of the g-weights. `method` a character string specifying the calibration method to be used. Possible values are `"linear"` for the linear method, `"raking"` for the multiplicative method known as raking and `"logit"` for the logit method. `bounds` a numeric vector of length two giving bounds for the g-weights to be used in the logit method. The first value gives the lower bound (which must be smaller than or equal to 1) and the second value gives the upper bound (which must be larger than or equal to 1). `maxit` a numeric value giving the maximum number of iterations. `tol` the desired accuracy for the iterative procedure. `eps` the desired accuracy for computing the Moore-Penrose generalized inverse (see `ginv`).

## Details

The final sample weights need to be computed by multiplying the resulting g-weights with the initial sample weights.

## Value

A numeric vector containing the g-weights.

## Note

This is a faster implementation of parts of `calib` from package `sampling`. Note that the default calibration method is raking and that the truncated linear method is not yet implemented.

Andreas Alfons

## References

Deville, J.-C. and Särndal, C.-E. (1992) Calibration estimators in survey sampling. Journal of the American Statistical Association, 87(418), 376–382.

Deville, J.-C., Särndal, C.-E. and Sautory, O. (1993) Generalized raking procedures in survey sampling. Journal of the American Statistical Association, 88(423), 1013–1020.

`calibVars`, `bootVar`
 ``` 1 2 3 4 5 6 7 8 9 10``` ```data(eusilc) # construct auxiliary 0/1 variables for genders aux <- calibVars(eusilc\$rb090) # population totals totals <- c(3990798, 4191431) # compute g-weights g <- calibWeights(aux, eusilc\$rb050, totals) # compute final weights weights <- g * eusilc\$rb050 summary(weights) ```