svrepdesign(variables = NULL, repweights = NULL, weights = NULL, data =
NULL, type = c("BRR", "Fay", "JK1","JKn","other"),
combined.weights=FALSE, rho = NULL,
scale=NULL, rscales=NULL,fpc=NULL, fpctype=c("fraction","correction"))
TRUE
if the repweights
already
include the sampling weightssvyrep.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.
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"
as.svrepdesign
, svydesign
, brrweights