Specify a complex survey design.

```
svydesign(ids, probs=NULL, strata = NULL, variables = NULL, fpc=NULL,
data = NULL, nest = FALSE, check.strata = !nest, weights=NULL,pps=FALSE,...)
# S3 method for default
svydesign(ids, probs=NULL, strata = NULL, variables = NULL,
fpc=NULL,data = NULL, nest = FALSE, check.strata = !nest, weights=NULL,
pps=FALSE,variance=c("HT","YG"),...)
# S3 method for imputationList
svydesign(ids, probs = NULL, strata = NULL, variables = NULL,
fpc = NULL, data, nest = FALSE, check.strata = !nest, weights = NULL, pps=FALSE,
...)
# S3 method for character
svydesign(ids, probs = NULL, strata = NULL, variables = NULL,
fpc = NULL, data, nest = FALSE, check.strata = !nest, weights = NULL, pps=FALSE,
dbtype = "SQLite", dbname, ...)
```

ids

Formula or data frame specifying cluster ids from largest
level to smallest level, `~0`

or `~1`

is a formula for no clusters.

probs

Formula or data frame specifying cluster sampling probabilities

strata

Formula or vector specifying strata, use `NULL`

for no strata

variables

Formula or data frame specifying the variables
measured in the survey. If `NULL`

, the `data`

argument is
used.

fpc

Finite population correction: see Details below

weights

Formula or vector specifying sampling weights as an
alternative to `prob`

data

Data frame to look up variables in the formula
arguments, or database table name, or `imputationList`

object, see below

nest

If `TRUE`

, relabel cluster ids to enforce nesting
within strata

check.strata

If `TRUE`

, check that clusters are nested in
strata

pps

dbtype

name of database driver to pass to `dbDriver`

dbname

name of database (eg file name for SQLite)

variance

For `pps`

without replacement, use `variance="YG"`

for the Yates-Grundy estimator instead of the Horvitz-Thompson estimator

…

for future expansion

An object of class `survey.design`

.

The `svydesign`

object combines a data frame and all the survey
design information needed to analyse it. These objects are used by
the survey modelling and summary functions. The
`id`

argument is always required, the `strata`

,
`fpc`

, `weights`

and `probs`

arguments are
optional. If these variables are specified they must not have any
missing values.

By default, `svydesign`

assumes that all PSUs, even those in
different strata, have a unique value of the `id`

variable. This allows some data errors to be detected. If your PSUs
reuse the same identifiers across strata then set `nest=TRUE`

.

The finite population correction (fpc) is used to reduce the variance when a substantial fraction of the total population of interest has been sampled. It may not be appropriate if the target of inference is the process generating the data rather than the statistics of a particular finite population.

The finite population correction can be specified either as the total population size in each stratum or as the fraction of the total population that has been sampled. In either case the relevant population size is the sampling units. That is, sampling 100 units from a population stratum of size 500 can be specified as 500 or as 100/500=0.2. The exception is for PPS sampling without replacement, where the sampling probability (which will be different for each PSU) must be used.

If population sizes are specified but not sampling probabilities or weights, the sampling probabilities will be computed from the population sizes assuming simple random sampling within strata.

For multistage sampling the `id`

argument should specify a
formula with the cluster identifiers at each stage. If subsequent
stages are stratified `strata`

should also be specified as a
formula with stratum identifiers at each stage. The population size
for each level of sampling should also be specified in `fpc`

.
If `fpc`

is not specified then sampling is assumed to be with
replacement at the top level and only the first stage of cluster is
used in computing variances. If `fpc`

is specified but for fewer
stages than `id`

, sampling is assumed to be complete for
subsequent stages. The variance calculations for
multistage sampling assume simple or stratified random sampling
within clusters at each stage except possibly the last.

For PPS sampling without replacement it is necessary to specify the
probabilities for each stage of sampling using the `fpc`

arguments, and an overall `weight`

argument should not be
given. At the moment, multistage or stratified PPS sampling without
replacement is supported only with `pps="brewer"`

, or by
giving the full joint probability matrix using
`ppsmat`

. [Cluster sampling is supported by all
methods, but not subsampling within clusters].

The `dim`

, `"["`

, `"[<-"`

and na.action methods for
`survey.design`

objects operate on the dataframe specified by
`variables`

and ensure that the design information is properly
updated to correspond to the new data frame. With the `"[<-"`

method the new value can be a `survey.design`

object instead of a
data frame, but only the data frame is used. See also
`subset.survey.design`

for a simple way to select
subpopulations.

The `model.frame`

method extracts the observed data.

If the strata with only one PSU are not self-representing (or they are,
but `svydesign`

cannot tell based on `fpc`

) then the handling
of these strata for variance computation is determined by
`options("survey.lonely.psu")`

. See `svyCprod`

for
details.

`data`

may be a character string giving the name of a table or view
in a relational database that can be accessed through the `DBI`

interfaces. For DBI interfaces `dbtype`

should be the name of the database
driver and `dbname`

should be the name by which the driver identifies
the specific database (eg file name for SQLite).

The appropriate database interface package must already be loaded (eg
`RSQLite`

for SQLite). The survey design
object will contain only the design meta-data, and actual variables will
be loaded from the database as needed. Use
`close`

to close the database connection and
`open`

to reopen the connection, eg, after
loading a saved object.

The database interface does not attempt to modify the underlying database and so can be used with read-only permissions on the database.

If `data`

is an `imputationList`

object (from the "mitools"
package), `svydesign`

will return a `svyimputationList`

object
containing a set of designs. Use `with.svyimputationList`

to
do analyses on these designs and `MIcombine`

to combine the results.

`as.svrepdesign`

for converting to replicate weight designs,
`subset.survey.design`

for domain estimates,
`update.survey.design`

to add variables.

`mitools`

package for using multiple imputations

`svyrecvar`

and `svyCprod`

for details of
variance estimation

`election`

for examples of PPS sampling without replacement.

http://faculty.washington.edu/tlumley/survey/ for examples of database-backed objects.

# NOT RUN { data(api) # stratified sample dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) # one-stage cluster sample dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) # two-stage cluster sample: weights computed from population sizes. dclus2<-svydesign(id=~dnum+snum, fpc=~fpc1+fpc2, data=apiclus2) ## multistage sampling has no effect when fpc is not given, so ## these are equivalent. dclus2wr<-svydesign(id=~dnum+snum, weights=weights(dclus2), data=apiclus2) dclus2wr2<-svydesign(id=~dnum, weights=weights(dclus2), data=apiclus2) ## syntax for stratified cluster sample ##(though the data weren't really sampled this way) svydesign(id=~dnum, strata=~stype, weights=~pw, data=apistrat, nest=TRUE) ## PPS sampling without replacement data(election) dpps<- svydesign(id=~1, fpc=~p, data=election_pps, pps="brewer") ##database example: requires RSQLite # } # NOT RUN { library(RSQLite) dbclus1<-svydesign(id=~dnum, weights=~pw, fpc=~fpc, data="apiclus1",dbtype="SQLite", dbname=system.file("api.db",package="survey")) # }

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