Compute variances by replicate weighting
Given a function or expression computing a statistic based on sampling
weights, withReplicates evaluates the statistic and produces a
replicate-based estimate of variance. vcov.svrep.design produces
the variance estimate from a set of replicates and the design object.
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 '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 replicate-weights 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.
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.
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) ## 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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