Description Usage Arguments Value Note Author(s) References Examples
Calibrate sample weights according to known marginal population totals. Based on initial sample weights, the socalled gweights are computed by generalized raking procedures. The final sample weights need to be computed by multiplying the resulting gweights with the initial sample weights.
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X 
a matrix of calibration variables. 
d 
a numeric vector giving the initial sample (or design) 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). If 
maxit 
a numeric value giving the maximum number of iterations. 
ginv 
a function that computes the MoorePenrose generalized
inverse (default: an optimized version of 
tol 
relative tolerance; convergence is achieved if the difference of all residuals (relative to the corresponding total) is smaller than this tolerance. 
attributes 
should additional attributes (currently

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, with improvements by Kirill Müller
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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