Some recent large-scale surveys specify replication weights rather than the sampling design (partly for privacy reasons). This function specifies the data structure for such a survey.
svrepdesign(variables , repweights , weights, data, degf=NULL,...)
# S3 method for default
svrepdesign(variables = NULL, repweights = NULL, weights = NULL,
data = NULL, degf=NULL, type = c("BRR", "Fay", "JK1","JKn","bootstrap",
"ACS","successive-difference","JK2","other"),
combined.weights=TRUE, rho = NULL, bootstrap.average=NULL,
scale=NULL, rscales=NULL,fpc=NULL, fpctype=c("fraction","correction"),
mse=getOption("survey.replicates.mse"),...)
# S3 method for imputationList
svrepdesign(variables=NULL,
repweights,weights,data, degf=NULL,
mse=getOption("survey.replicates.mse"),...)
# S3 method for character
svrepdesign(variables=NULL,repweights=NULL,
weights=NULL,data=NULL, degf=NULL,
type=c("BRR","Fay","JK1", "JKn","bootstrap","ACS","successive-difference","JK2","other"),
combined.weights=TRUE, rho=NULL, bootstrap.average=NULL, scale=NULL,rscales=NULL,
fpc=NULL,fpctype=c("fraction","correction"),mse=getOption("survey.replicates.mse"),
dbtype="SQLite", dbname,...) # S3 method for svyrep.design
image(x, ...,
col=grey(seq(.5,1,length=30)), type.=c("rep","total"))
Object of class svyrep.design, with methods for print,
summary, weights, image.
formula or data frame specifying variables to include in the design (default is all)
formula or data frame specifying replication weights, or character string specifying a regular expression that matches the names of the replication weight variables
sampling weights
data frame to look up variables in formulas, or character string giving name of database table
Design degrees of freedom; use NULL to have the
function work this out for you
Type of replication weights
TRUE if the repweights already
include the sampling weights. This is usually the case.
Shrinkage factor for weights in Fay's method
For type="bootstrap", if the bootstrap
weights have been averaged, gives the number of iterations averaged over
Scaling constant for variance, see Details below
Finite population correction information
If TRUE, compute variances based on sum of squares
around the point estimate, rather than the mean of the replicates
name of database, passed to DBI::dbConnect()
Database driver: see Details
survey design with replicate weights
Other arguments to image
Colors
"rep" for only the replicate weights, "total" for the replicate and sampling weights combined.
In the BRR method, the dataset is split into halves, and the
difference between halves is used to estimate the variance. In Fay's
method, rather than removing observations from half the sample they
are given weight rho in one half-sample and 2-rho in the
other. The ideal BRR analysis is restricted to a design where each
stratum has two PSUs, however, it has been used in a much wider class
of surveys. The scale and rscales
arguments will be ignored (with a warning) if they are specified.
The JK1 and JKn types are both jackknife estimators deleting one cluster at a time. JKn is designed for stratified and JK1 for unstratified designs.
The successive-difference weights in the American Community Survey
automatically use scale = 4/ncol(repweights) and rscales=rep(1,
ncol(repweights)). This can be specified as type="ACS" or
type="successive-difference". The scale and rscales
arguments will be ignored (with a warning) if they are specified.
JK2 weights (type="JK2"), as in the California Health Interview
Survey, automatically use scale=1, rscales=rep(1, ncol(repweights)).
The scale and rscales
arguments will be ignored (with a warning) if they are specified.
Averaged bootstrap weights ("mean bootstrap") are used for some surveys from Statistics Canada. Yee et al (1999) describe their construction and use for one such survey.
The variance is computed as the sum of squared deviations of the
replicates from their mean. This may be rescaled: scale is an
overall multiplier and rscales is a vector of
replicate-specific multipliers for the squared deviations. That is,
rscales should have one entry for each column of repweights
If thereplication weights incorporate the sampling weights
(combined.weights=TRUE) or for type="other" these must
be specified, otherwise they can be guessed from the weights.
A finite population correction may be specified for type="other",
type="JK1" and type="JKn". fpc must be a vector
with one entry for each replicate. To specify sampling fractions use
fpctype="fraction" and to specify the correction directly use
fpctype="correction"
The design degrees of freedom are returned by degf. By
default they are computed from the numerical rank of the
repweights. This is slow for very large data sets and you can specify
a value instead.
repweights may be a character string giving a regular expression
for the replicate weight variables. For example, in the
California Health Interview Survey public-use data, the sampling weights are
"rakedw0" and the replicate weights are "rakedw1" to
"rakedw80". The regular expression "rakedw[1-9]"
matches the replicate weight variables (and not the sampling weight
variable).
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
interface. 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 the replicate weights, but actual variables will
be loaded from the database only 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.
To generate your own replicate weights either use
as.svrepdesign on a survey.design object, or see
brrweights, bootweights,
jk1weights and jknweights
The model.frame method extracts the observed data.
Levy and Lemeshow. "Sampling of Populations". Wiley.
Shao and Tu. "The Jackknife and Bootstrap." Springer.
Yee et al (1999). Bootstrat Variance Estimation for the National Population Health Survey. Proceedings of the ASA Survey Research Methodology Section. https://web.archive.org/web/20151110170959/http://www.amstat.org/sections/SRMS/Proceedings/papers/1999_136.pdf
as.svrepdesign, svydesign,
brrweights, bootweights
data(scd)
# use BRR replicate weights from Levy and Lemeshow
repweights<-2*cbind(c(1,0,1,0,1,0), c(1,0,0,1,0,1), c(0,1,1,0,0,1),
c(0,1,0,1,1,0))
scdrep<-svrepdesign(data=scd, type="BRR", repweights=repweights, combined.weights=FALSE)
svyratio(~alive, ~arrests, scdrep)
if (FALSE) {
## Needs RSQLite
library(RSQLite)
db_rclus1<-svrepdesign(weights=~pw, repweights="wt[1-9]+", type="JK1", scale=(1-15/757)*14/15,
data="apiclus1rep",dbtype="SQLite", dbname=system.file("api.db",package="survey"), combined=FALSE)
svymean(~api00+api99,db_rclus1)
summary(db_rclus1)
## closing and re-opening a connection
close(db_rclus1)
db_rclus1
try(svymean(~api00+api99,db_rclus1))
db_rclus1<-open(db_rclus1)
svymean(~api00+api99,db_rclus1)
}
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