Survey sample analysis.
Specify a complex survey design.
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svydesign(ids, probs=NULL, strata = NULL, variables = NULL, fpc=NULL, data = NULL, nest = FALSE, check.strata = !nest, weights=NULL,pps=FALSE,...) ## Default S3 method: 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 class 'imputationList' svydesign(ids, probs = NULL, strata = NULL, variables = NULL, fpc = NULL, data, nest = FALSE, check.strata = !nest, weights = NULL, pps=FALSE, ...) ## S3 method for class 'character' svydesign(ids, probs = NULL, strata = NULL, variables = NULL, fpc = NULL, data, nest = FALSE, check.strata = !nest, weights = NULL, pps=FALSE, dbtype = "SQLite", dbname, ...)
Formula or data frame specifying cluster ids from largest
level to smallest level,
Formula or data frame specifying cluster sampling probabilities
Formula or vector specifying strata, use
Formula or data frame specifying the variables
measured in the survey. If
Finite population correction: see Details below
Formula or vector specifying sampling weights as an
Data frame to look up variables in the formula
arguments, or database table name, or
name of database driver to pass to
name of database (eg file name for SQLite)
for future expansion
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
probs arguments are
optional. If these variables are specified they must not have any
svydesign assumes that all PSUs, even those in
different strata, have a unique value of the
variable. This allows some data errors to be detected. If your PSUs
reuse the same identifiers across strata then set
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 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
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
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].
"[<-" 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
model.frame method extracts the observed data.
If the strata with only one PSU are not self-representing (or they are,
svydesign cannot tell based on
fpc) then the handling
of these strata for variance computation is determined by
data may be a character string giving the name of a table or view
in a relational database that can be accessed through the
interfaces. For DBI interfaces
dbtype should be the name of the database
dbname should be the name by which the driver identifies
the specific database (eg file name for SQLite). For ODBC databases
dbtype should be
dbname should be the
registed DSN for the database. On the Windows GUI,
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.
The database interface does not attempt to modify the underlying database and so can be used with read-only permissions on the database.
data is an
imputationList object (from the "mitools"
svydesign will return a
containing a set of designs. Use
do analyses on these designs and
MIcombine to combine the results.
An object of class
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
svyCprod for details of
election for examples of PPS sampling without replacement.
http://faculty.washington.edu/tlumley/survey/ for examples of database-backed objects.
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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")) ## End(Not run)
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