surveysummary {survey} | R Documentation |
Compute means, variances, ratios and totals for data from complex surveys.
## S3 method for class 'survey.design': svymean(x, design, na.rm=FALSE,deff=FALSE,...) ## S3 method for class 'twophase': svymean(x, design, na.rm=FALSE,deff=FALSE,...) ## S3 method for class 'svyrep.design': svymean(x, design, na.rm=FALSE, rho=NULL, return.replicates=FALSE, deff=FALSE,...) ## S3 method for class 'survey.design': svyvar(x, design, na.rm=FALSE,...) ## S3 method for class 'svyrep.design': svyvar(x, design, na.rm=FALSE, rho=NULL, return.replicates=FALSE,...,estimate.only=FALSE) ## S3 method for class 'survey.design': svytotal(x, design, na.rm=FALSE,deff=FALSE,...) ## S3 method for class 'twophase': svytotal(x, design, na.rm=FALSE,deff=FALSE,...) ## S3 method for class 'svyrep.design': svytotal(x, design, na.rm=FALSE, rho=NULL, return.replicates=FALSE, deff=FALSE,...) ## 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,...) cv(object,...) deff(object, quietly=FALSE,...) make.formula(names)
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 (see below) |
object |
The result of one of the other survey summary functions |
quietly |
Don't warn when there is no design effect computed |
estimate.only |
Don't compute standard errors (useful when
svyvar is used to estimate the design effect) |
... |
additional arguments to cv methods,not currently
used |
names |
vector of character strings |
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. Combining this with the
interaction
function, allows crosstabulations. See
ftable.svystat
for formatting the output.
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
and
svyglm
, to get ratio or regression estimates of totals.
The design effect compares the variance of a mean or total to the
variance from a study of the same size using simple random sampling
without replacement. Note that the design effect will be incorrect if
the weights have been rescaled so that they are not reciprocals of
sampling probabilities. To obtain an estimate of the design effect
comparing to simple random sampling with replacement, which does not
have this requirement, use deff="replace"
. This with-replacement
design effect is the square of Kish's "deft".
The cv
function computes the coefficient of variation of a
statistic such as ratio, mean or total. The default method is for any
object with methods for SE
and coef
.
make.formula
makes a formula from a vector of names. This is
useful because formulas as the best way to specify variables to the
survey functions.
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
for constructing design objects.
svyquantile
for quantiles
ftable.svystat
for more attractive tables
svyciprop
for more accurate confidence intervals for
proportions near 0 or 1.
svyttest
for comparing two means.
data(api) ## 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) svymean(~interaction(stype, comp.imp), 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) svymean(~api00, jkstrat) svymean(~factor(stype),jkstrat) svyvar(~api00,jkstrat) svyquantile(~api00, jkstrat, c(.25,.5,.75)) svytotal(~enroll, jkstrat) svyratio(~api.stu, ~enroll, jkstrat) # coefficients of variation cv(svytotal(~enroll,dstrat)) cv(svyratio(~api.stu, ~enroll, jkstrat)) # extracting statistic and variance coef(svytotal(~enroll,dstrat)) vcov(svymean(~api00+api99,jkstrat)) # Design effect svymean(~api00, dstrat, deff=TRUE) svymean(~api00, dstrat, deff="replace") svymean(~api00, jkstrat, deff=TRUE) svymean(~api00, jkstrat, deff="replace") (a<-svytotal(~enroll, dclus1, deff=TRUE)) deff(a)