Description Usage Arguments Details Value See Also Examples
Given a function or expression computing a statistic based on sampling
weights, withReplicates
evaluates the statistic and produces a
replicatebased estimate of variance. vcov.svrep.design
produces
the variance estimate from a set of replicates and the design object.
1 2 3 4 5 6 7 8 9 10 11 12  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)
## S3 method for class 'svrepstat'
withReplicates(design, theta, ..., return.replicates=FALSE)
## S3 method for class 'svyimputationList'
withReplicates(design, theta, ..., return.replicates=FALSE)
## S3 method for class 'svyrep.design'
vcov(object, replicates, centre,...)

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? 
object 
The replicateweights design object used to create the replicates 
replicates 
A set of replicates 
centre 
The centering value for variance calculation. If

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.
For the svrepstat
method a function will get the point estimate, and an expression will be evaluated with
.replicate
set to each replicate. The method can only be used
when the svrepstat
object includes replicates.
The svyimputationList
method runs withReplicates
on each imputed design (which must be replicateweight designs).
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51  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)$unique) )
data(nhanes)
nhanesdesign < svydesign(id=~SDMVPSU, strata=~SDMVSTRA, weights=~WTMEC2YR, nest=TRUE,data=nhanes)
logistic < svyglm(HI_CHOL~race+agecat+RIAGENDR, design=as.svrepdesign(nhanesdesign),
family=quasibinomial, return.replicates=TRUE)
fitted<predict(logistic, return.replicates=TRUE, type="response")
sensitivity<function(pred,actual) mean(pred>0.1 & actual)/mean(actual)
withReplicates(fitted, sensitivity, actual=logistic$y)
## Not run:
library(quantreg)
data(api)
## onestage 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)
## pearson correlation
dstrat < svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)
bstrat< as.svrepdesign(dstrat,type="subbootstrap")
v < svyvar(~api00+api99, bstrat, return.replicates=TRUE)
vcor<cov2cor(as.matrix(v))[2,1]
vreps<v$replicates
correps<apply(vreps,1, function(v) v[2]/sqrt(v[1]*v[4]))
vcov(bstrat,correps, centre=vcor)

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