# surveysummary

##### Summary statistics for sample surveys

Compute means, variances, ratios and totals for data from complex surveys.

##### Usage

```
## 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)
```

##### Arguments

- 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

##### Details

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.

##### Value

- 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.

##### See Also

`svydesign`

, `as.svrepdesign`

,
`svrepdesign`

, `svyquantile`

,
`ftable.svystat`

##### Examples

```
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)
```

*Documentation reproduced from package survey, version 3.9-1, License: GPL-2 | GPL-3*