surveysummary {survey} | R Documentation |
Compute means, variances, ratios and totals for data from complex surveys.
svymean(x, design, na.rm=FALSE,deff=FALSE) svrepmean(x, design, na.rm=FALSE, rho=NULL, return.replicates=FALSE, deff=FALSE) svyvar(x, design, na.rm=FALSE) svrepvar(x, design, na.rm=FALSE, rho=NULL, return.replicates=FALSE) svytotal(x, design, na.rm=FALSE,deff=FALSE) svreptotal(x, design, na.rm=FALSE, rho=NULL, return.replicates=FALSE, deff=FALSE) cv(object,...) ## S3 method for class 'svystat': coef(object,...) ## S3 method for class 'svrepstat': coef(object,...) ## S3 method for class 'svystat': vcov(object,...) ## S3 method for class 'svrepstat': vcov(object,...) deff(object, quietly=FALSE,...)
x |
A formula, vector or matrix |
design |
survey.design or svyrep.design object |
na.rm |
Should cases with missing values be dropped? |
rho |
parameter for Fay's variance estimator in a BRR design |
return.replicates |
Return the replicate means? |
deff |
Return the design effect |
object |
The result of one of the other survey summary functions |
quietly |
Don't warn when there is no design effect computed |
... |
additional arguments to cv methods,not currently used |
These functions perform weighted estimation, with each observation being weighted by the inverse of its sampling probability. Except for the table functions, these also give precision estimates that incorporate the effects of stratification and clustering.
Factor variables are converted to sets of indicator variables for each category in computing means and totals.
With na.rm=TRUE
, all cases with missing data are removed. With
na.rm=FALSE
cases with missing data are not removed and so will
produce missing results. When using replicate weights and
na.rm=FALSE
it may be useful to set
options(na.action="na.pass")
, otherwise all replicates with any
missing results will be discarded.
The svytotal
and svreptotal
functions estimate a
population total. Use predict
on svyratio
,
svrepratio
, svyglm
, svrepglm
to get ratio or regression estimates of totals.
The design effect compares the variance of a mean or total to the variance of a simple random sample of the same size. Although strictly speaking this should be a simple random sample without replacement, we compute as if it were a simple random sample with replacement.
The cv
function computes the coefficient of variation of a
statistic such as ratio, mean or total.
Objects of class "svystat"
or "svrepstat"
,
which are vectors with a "var"
attribute giving the variance
and a "statistic"
attribute giving the name of the statistic.
Thomas Lumley
svydesign
, as.svrepdesign
,
svrepdesign
, svyCprod
, mean
,
var
, svyquantile
data(api) ## population mean(apipop$api00) quantile(apipop$api00,c(.25,.5,.75)) var(apipop$api00) sum(apipop$enroll) sum(apipop$api.stu)/sum(apipop$enroll) ## one-stage cluster sample dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) summary(dclus1) svymean(~api00, dclus1, deff=TRUE) svymean(~factor(stype),dclus1) svyquantile(~api00, dclus1, c(.25,.5,.75)) svyvar(~api00, dclus1) svytotal(~enroll, dclus1, deff=TRUE) svyratio(~api.stu, ~enroll, dclus1) #stratified sample dstrat<-svydesign(id=~1, strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) summary(dstrat) svymean(~api00, dstrat) svyquantile(~api00, dstrat, c(.25,.5,.75)) svyvar(~api00, dstrat) svytotal(~enroll, dstrat) svyratio(~api.stu, ~enroll, dstrat) # replicate weights - jackknife (this is slow) jkstrat<-as.svrepdesign(dstrat) summary(jkstrat) svrepmean(~api00, jkstrat) svrepmean(~factor(stype),jkstrat) svrepvar(~api00,jkstrat) svrepquantile(~api00, jkstrat, c(.25,.5,.75)) svreptotal(~enroll, jkstrat) svrepratio(~api.stu, ~enroll, jkstrat) # coefficients of variation cv(svytotal(~enroll,dstrat)) cv(svrepratio(~api.stu, ~enroll, jkstrat)) # extracting statistic and variance coef(svytotal(~enroll,dstrat)) vcov(svrepmean(~api00+api99,jkstrat)) # Design effect a<-svymean(~api00, dclus1, deff=TRUE) a deff(a) # BRR method 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) svrepmean(~arrests+alive, design=scdrep)