Description Usage Arguments Details Value Note Author(s) References See Also Examples
Calibrate sample weights according to known marginal population totals. Based on initial sample weights, the socalled gweights are computed by generalized raking procedures.
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X 
a matrix of binary calibration variables (see

d 
a numeric vector giving the initial sample weights. 
totals 
a numeric vector of population totals corresponding to the
calibration variables in 
q 
a numeric vector of positive values accounting for heteroscedasticity. Small values reduce the variation of the gweights. 
method 
a character string specifying the calibration method to be
used. Possible values are 
bounds 
a numeric vector of length two giving bounds for the gweights 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 MoorePenrose generalized
inverse (see 
The final sample weights need to be computed by multiplying the resulting gweights with the initial sample weights.
A numeric vector containing the gweights.
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
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.
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