Given a function or expression computing a statistic based on sampling
weights, `withReplicates`

evaluates the statistic and produces a
replicate-based estimate of variance.

1 2 3 4 5 6 7 8 | ```
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)
``` |

`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.

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.

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 | ```
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)
## 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)
``` |

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