survey (version 3.9-1)

svydesign: Survey sample analysis.

Description

Specify a complex survey design.

Usage

svydesign(ids, probs=NULL, strata = NULL, variables = NULL, fpc=NULL,
data = NULL, nest = FALSE, check.strata = !nest, weights=NULL,...)
## S3 method for class 'imputationList':
svydesign(ids, probs = NULL, strata = NULL, variables = NULL, 
    fpc = NULL, data, nest = FALSE, check.strata = !nest, weights = NULL, 
    ...)
## S3 method for class 'character':
svydesign(ids, probs = NULL, strata = NULL, variables = NULL, 
    fpc = NULL, data, nest = FALSE, check.strata = !nest, weights = NULL, 
    dbtype = "SQLite", dbname, ...)

Arguments

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

dbtype

name of database driver to pass to dbDriver

dbname

name of database (eg file name for SQLite)

...

for future expansion

Value

• An object of class survey.design.

Details

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. 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. 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 one only 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 or ODBC 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). For ODBC databases dbtype should be "ODBC" and dbname should be the registed DSN for the database. On the Windows GUI, dbname="" will produce a dialog box for interactive selection.

The appropriate database interface package must already be loaded (eg RSQLite for SQLite, RODBC for ODBC). 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.

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.

See Also

postStratify for post-stratification, 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

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

Examples

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)

##database example: requires RSQLite
library(RSQLite)
dbclus1<-svydesign(id=~dnum, weights=~pw, fpc=~fpc,
data="apiclus1",dbtype="SQLite", dbname=system.file("api.db",package="survey"))