withReplicates {survey} | R Documentation |
Given a function or expression computing a statistic based on sampling
weights, withReplicates
evaluates the statistic and produces a
replicate-based estimate of variance.
withReplicates(design, theta,..., return.replicates=FALSE) ## S3 method for class 'svyrep.design' withReplicates(design, theta, rho = NULL, ..., scale.weights=FALSE, return.replicates=FALSE) ## S3 method for class 'svrepvar' withReplicates(design, theta, ..., return.replicates=FALSE)
design |
A survey design with replicate weights (eg from |
theta |
A function or expression: see Details below |
rho |
If |
... |
Other arguments to |
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? |
The method for svyrep.design
objects evaluates a function or
expression using the sampling weights and then each set of replicate
weights. The method for svrepvar
objects evaluates the function
or expression on an estimated population covariance matrix and its
replicates, to simplify multivariate statistics such as structural
equation models.
For the svyrep.design
method, 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
.
For the svrepvar
method a function will get the covariance
matrix as its first argument, and an expression will be evaluated with
.replicate
set to the variance matrix.
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.
svrepdesign
, as.svrepdesign
, svrVar
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)) data(api) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) rclus1<-as.svrepdesign(dclus1) varmat<-svyvar(~api00+api99+ell+meals+hsg+mobility,rclus1,return.replicates=TRUE) withReplicates(varmat, quote( factanal(covmat=.replicate, factors=2)$unique) ) ## Not run: 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)))) ## End(Not run)