survey: make.calfun – R documentation

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make.calfun

Calibration metrics

Description

Create calibration metric for use in calibrate. The function F is the link function described in section 2 of Deville et al. To create a new calibration metric, specify F-1 and its derivative. The package provides cal.linear, cal.raking, cal.logit, which are standard, and cal.sinh from the CALMAR2 macro, for which F is the derivative of the inverse hyperbolic sine.

Usage

make.calfun(Fm1, dF, name)

Arguments

`Fm1`	Function F-1 taking a vector `u` and a vector of length 2, `bounds`.
`dF`	Derivative of `Fm1` wrt `u`: arguments `u` and `bounds`
`name`	Character string to use as name

Value

An object of class "calfun"

References

Deville J-C, Sarndal C-E, Sautory O (1993) Generalized Raking Procedures in Survey Sampling. JASA 88:1013-1020

Deville J-C, Sarndal C-E (1992) Calibration Estimators in Survey Sampling. JASA 87: 376-382

Examples

str(cal.linear)
cal.linear$Fm1
cal.linear$dF

hellinger <- make.calfun(Fm1=function(u, bounds)  ((1-u/2)^-2)-1,
                    dF= function(u, bounds) (1-u/2)^-3 ,
                    name="hellinger distance")

hellinger

data(api)
dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)

svymean(~api00,calibrate(dclus1, ~api99, pop=c(6194, 3914069),
         calfun=hellinger))

svymean(~api00,calibrate(dclus1, ~api99, pop=c(6194, 3914069),
         calfun=cal.linear))

svymean(~api00,calibrate(dclus1, ~api99, pop=c(6194,3914069),
          calfun=cal.raking))

survey

Analysis of Complex Survey Samples

v4.0

GPL-2 | GPL-3

Authors

Thomas Lumley

Initial release