survey (version 3.25)

withReplicates: Compute variances by replicate weighting

Description

Given a function or expression computing a statistic based on sampling weights, withReplicates evaluates the statistic and produces a replicate-based estimate of variance.

Usage

withReplicates(design, theta, rho = NULL, ..., scale.weights=FALSE, return.replicates=FALSE)

Arguments

design

A survey design with replicate weights (eg from svrepdesign)

theta

A function or expression: see Details below

rho

If design uses BRR weights, rho optionally specifies the parameter for Fay's variance estimator.

...

Other arguments to theta

scale.weights

Divide the probability weights by their sum (can help with overflow problems)

return.replicates

Return the replicate estimates as well as the variance?

Value

• If return.replicates=FALSE, the weighted statistic, with the variance matrix as the "var" attribute. If return.replicates=TRUE, a list with elements theta for the usual return value and replicates for the replicates.

Details

If theta is a function its first argument will be a vector of weights and the second argument will be a data frame containing the variables from the design object. If it is an expression, the sampling weights will be available as the variable .weights. Variables in the design object will also be in scope. It is possible to use global variables in the expression, but unwise, as they may be masked by local variables inside withReplicates.

See Also

svrepdesign, as.svrepdesign, svrVar

Examples

data(scd)
repweights<-2*cbind(c(1,0,1,0,1,0), c(1,0,0,1,0,1), c(0,1,1,0,0,1),
c(0,1,0,1,1,0))
scdrep<-svrepdesign(data=scd, type="BRR", repweights=repweights)

a<-svyratio(~alive, ~arrests, design=scdrep)
print(a$ratio)
print(a$var)
withReplicates(scdrep, quote(sum(.weights*alive)/sum(.weights*arrests)))
withReplicates(scdrep, function(w,data)
sum(w*data$alive)/sum(w*data$arrests))

library(quantreg)
data(api)
## one-stage cluster sample
dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)
## convert to bootstrap
bclus1<-as.svrepdesign(dclus1,type="bootstrap", replicates=100)

## median regression
withReplicates(bclus1, quote(coef(rq(api00~api99, tau=0.5, weights=.weights))))