surveysummary  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,influence=FALSE,...)
## S3 method for class 'survey.design2'
svymean(x, design, na.rm=FALSE,deff=FALSE,influence=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,influence=FALSE,...)
## S3 method for class 'survey.design2'
svytotal(x, design, na.rm=FALSE,deff=FALSE,influence=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,...)
## S3 method for class 'svystat'
confint(object, parm, level = 0.95,df =Inf,...)
## S3 method for class 'svrepstat'
confint(object, parm, level = 0.95,df =Inf,...)
cv(object,...)
deff(object, quietly=FALSE,...)
make.formula(names)
x 
A formula, vector or matrix 
design 

na.rm 
Should cases with missing values be dropped? 
influence 
Should a matrix of influence functions be returned
(primarily to support 
rho 
parameter for Fay's variance estimator in a BRR design 
return.replicates 
Return the replicate means/totals? 
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

parm 
a specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered. 
level 
the confidence level required. 
df 
degrees of freedom for tdistribution in confidence
interval, use 
... 
additional arguments to 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.
svyvar
estimates the population variance. The object returned
includes the full matrix of estimated population variances and
covariances, but by default only the diagonal elements are printed. To
display the whole matrix use as.matrix(v)
or print(v,
covariance=TRUE)
.
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 withreplacement
design effect is the square of Kish's "deft".
The design effect for a subset of a design conditions on the size of the subset. That is, it compares the variance of the estimate to the variance of an estimate based on a simple random sample of the same size as the subset, taken from the subpopulation. So, for example, under stratified random sampling the design effect in a subset consisting of a single stratum will be 1.0.
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.
These objects have methods for vcov
, SE
, coef
,
confint
, svycontrast
.
Thomas Lumley
svydesign
, as.svrepdesign
,
svrepdesign
for constructing design objects.
degf
to extract degrees of freedom from a design.
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.
svycontrast
for linear and nonlinear functions of estimates.
data(api)
## onestage cluster sample
dclus1<svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)
svymean(~api00, dclus1, deff=TRUE)
svymean(~factor(stype),dclus1)
svymean(~interaction(stype, comp.imp), dclus1)
svyquantile(~api00, dclus1, c(.25,.5,.75))
svytotal(~enroll, dclus1, deff=TRUE)
svyratio(~api.stu, ~enroll, dclus1)
v<svyvar(~api00+api99, dclus1)
v
print(v, cov=TRUE)
as.matrix(v)
# replicate weights  jackknife (this is slower)
dstrat<svydesign(id=~1,strata=~stype, weights=~pw,
data=apistrat, fpc=~fpc)
jkstrat<as.svrepdesign(dstrat)
svymean(~api00, jkstrat)
svymean(~factor(stype),jkstrat)
svyvar(~api00+api99,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 information from the results
coef(svytotal(~enroll,dstrat))
vcov(svymean(~api00+api99,jkstrat))
SE(svymean(~enroll, dstrat))
confint(svymean(~api00+api00, dclus1))
confint(svymean(~api00+api00, dclus1), df=degf(dclus1))
# 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)
## weights that are *already* calibrated to population size
sum(weights(dclus1))
nrow(apipop)
cdclus1< svydesign(id=~dnum, weights=~pw, data=apiclus1,
fpc=~fpc,calibrate.formula=~1)
SE(svymean(~enroll, dclus1))
## not equal to SE(mean)
SE(svytotal(~enroll, dclus1))/nrow(apipop)
## equal to SE(mean)
SE(svytotal(~enroll, cdclus1))/nrow(apipop)
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