svrepdesign(variables , repweights , weights, data,...)
## S3 method for class 'default':
svrepdesign(variables = NULL, repweights = NULL, weights = NULL, data =
NULL, type = c("BRR", "Fay", "JK1","JKn","bootstrap","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 class 'imputationList':
svrepdesign(variables=NULL, repweights,weights,data,mse=getOption("survey.replicates.mse"),...)
## S3 method for class 'character':
svrepdesign(variables=NULL,repweights=NULL, weights=NULL,data=NULL,
type=c("BRR","Fay","JK1", "JKn","bootstrap","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 class 'svyrep.design':
image(x, ..., col=grey(seq(.5,1,length=30)), type.=c("rep","total"))
TRUE if the repweights already
include the sampling weights. This is usually the case.type="bootstrap", if the bootstrap
weights have been averaged, gives the number of iterations averaged overTRUE, compute variances based on sum of squares
around the point estimate, rather than the mean of the replicatesDBI::dbConnect()image"rep" for only the replicate weights, "total" for the replicate and sampling weights combined.svyrep.design, with methods for print,
summary, weights, image.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 JK1 and JKn types are both jackknife estimators deleting one
cluster at a time. JKn is designed for stratified and JK1 for
unstratified designs. 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"
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 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 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.
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.
as.svrepdesign, svydesign,
brrweights, bootweightsdata(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)
## 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)Run the code above in your browser using DataLab