svrepdesign {survey} | R Documentation |
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,...) ## Default S3 method: svrepdesign(variables = NULL, repweights = NULL, weights = NULL, data = NULL, type = c("BRR", "Fay", "JK1","JKn","bootstrap","other"), combined.weights=FALSE, rho = NULL, bootstrap.average=NULL, scale=NULL, rscales=NULL,fpc=NULL, fpctype=c("fraction","correction"),...) ## S3 method for class 'imputationList': svrepdesign(variables=NULL, repweights,weights,data,...) ## S3 method for class 'svyrep.design': image(x, ..., col=grey(seq(.5,1,length=30)), type.=c("rep","total"))
variables |
formula or data frame specifying variables to include in the design (default is all) |
repweights |
formula or data frame specifying replication weights |
weights |
sampling weights |
data |
data frame to look up variables in formulas |
type |
Type of replication weights |
combined.weights |
TRUE if the repweights already
include the sampling weights |
rho |
Shrinkage factor for weights in Fay's method |
bootstrap.average |
For type="bootstrap" , if the bootstrap
weights have been averaged, gives the number of iterations averaged over |
scale, rscales |
Scaling constant for variance, see Details below |
fpc,fpctype |
Finite population correction information |
x |
survey design with replicate weights |
... |
Other arguments to image |
col |
Colors |
type. |
"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 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 rscale
is a vector of
replicate-specific multipliers for the squared deviations. If the
replication 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"
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.
Object of class svyrep.design
, with methods for print
,
summary
, weights
, image
.
To use replication-weight analyses on a survey specified by
sampling design, use as.svrepdesign
to convert it.
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. 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) svyratio(~alive, ~arrests, scdrep)