# Survey sample analysis. Specify a complex survey design. ```r 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,calibrate.formula=NULL,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, calibrate.formula=NULL,...) ## 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, calibrate.formula=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 . - `pps`: `"brewer"` to use Brewer's approximation for PPS sampling without replacement. `"overton"` to use Overton's approximation. An object of class `HR` to use the Hartley-Rao approximation. An object of class `ppsmat` to use the Horvitz-Thompson estimator. - `calibrate.formula`: model formula specifying how the weights are *already* calibrated (raked, post-stratified). - `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 ## 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. 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. ## Returns An object of class `survey.design`. ## Author(s) Thomas Lumley ## See Also `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` for details of variance estimation `election` for examples of PPS sampling without replacement. ## Examples ```r 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) ## pre-calibrated weights cdclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc, calibration.formula=~1) ```

svydesign function