make.calfun.RdCreate 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.
make.calfun(Fm1, dF, name)Function \(F-1\) taking a vector u and a
vector of length 2, bounds.
Derivative of Fm1 wrt u: arguments u
and bounds
Character string to use as name
An object of class "calfun"
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
str(cal.linear)
#> List of 3
#> $ Fm1 :function (u, bounds)
#> $ dF :function (u, bounds)
#> $ name: chr "linear calibration"
#> - attr(*, "class")= chr "calfun"
cal.linear$Fm1
#> function (u, bounds)
#> pmin(pmax(u + 1, bounds$lower), bounds$upper) - 1
#> <bytecode: 0x1430cfe88>
#> <environment: namespace:survey>
cal.linear$dF
#> function (u, bounds)
#> as.numeric(u < bounds$upper - 1 & u > bounds$lower - 1)
#> <bytecode: 0x1430ce9c0>
#> <environment: namespace:survey>
hellinger <- make.calfun(Fm1=function(u, bounds) ((1-u/2)^-2)-1,
dF= function(u, bounds) (1-u/2)^-3 ,
name="hellinger distance")
hellinger
#> calibration metric: hellinger distance
data(api)
dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)
svymean(~api00,calibrate(dclus1, ~api99, pop=c(6194, 3914069),
calfun=hellinger))
#> mean SE
#> api00 666.79 3.2813
svymean(~api00,calibrate(dclus1, ~api99, pop=c(6194, 3914069),
calfun=cal.linear))
#> mean SE
#> api00 666.72 3.2959
svymean(~api00,calibrate(dclus1, ~api99, pop=c(6194,3914069),
calfun=cal.raking))
#> mean SE
#> api00 666.77 3.286