brrweights              package:survey              R Documentation

_C_o_m_p_u_t_e _r_e_p_l_i_c_a_t_e _w_e_i_g_h_t_s

_D_e_s_c_r_i_p_t_i_o_n:

     Compute replicate weights from a survey design. These functions
     are usually called from 'as.svrepdesign' rather than directly by
     the user.

_U_s_a_g_e:

     brrweights(strata, psu, match = NULL,
                   small = c("fail","split","merge"),
                   large = c("split", "merge", "fail"),
                   fay.rho=0, only.weights=FALSE,
                   compress=TRUE, hadamard.matrix=NULL)
     jk1weights(psu,fpc=NULL,
                   fpctype=c("population","fraction","correction"),
                   compress=TRUE)
     jknweights(strata,psu, fpc=NULL,
                   fpctype=c("population","fraction","correction"),
                   compress=TRUE,
                   lonely.psu=getOption("survey.lonely.psu"))

_A_r_g_u_m_e_n_t_s:

  strata: Stratum identifiers 

     psu: PSU (cluster) identifier 

   match: Optional variable to use in matching. 

   small: How to handle strata with only one PSU

   large: How to handle strata with more than two PSUs

     fpc: Optional population (stratum) size or finite population
          correction 

 fpctype: How 'fpc' is coded.

 fay.rho: Parameter for Fay's extended BRR method

only.weights: If 'TRUE' return only the matrix of replicate weights

compress: If 'TRUE', store the replicate weights in compressed form

hadamard.matrix: Optional user-supplied Hadamard matrix for
          'brrweights'

lonely.psu: Handling of non-certainty single-PSU strata

_D_e_t_a_i_l_s:

     JK1 and JKn are  jackknife schemes for unstratified and stratified
     designs respectively.  The finite population correction may be
     specified as a single number, a vector with one entry per stratum,
     or a vector with one entry per observation (constant within
     strata).  When 'fpc' is a vector with one entry per stratum it may
     not have names that differ from the stratum identifiers (it may
     have no names, in which case it must be in the same order as
     'unique(strata)'). To specify population stratum sizes use
     'fpctype="population"', to specify sampling fractions use
     'fpctype="fraction"' and to specify the correction directly use
     'fpctype="correction"'

     The only reason not to use 'compress=TRUE' is that it is new and
     there is a greater possibility of bugs.  It reduces the number of
     rows of the replicate weights matrix from the number of
     observations to the number of PSUs.

     In BRR variance estimation each stratum is split in two to give
     half-samples. Balanced replicated weights are needed, where
     observations in two different strata end up in the same half
     stratum as often as in different half-strata.BRR, strictly
     speaking, is defined only when each stratum has exactly two PSUs. 
     A stratum with one PSU can be merged with another such stratum, or
     can be split to appear in both half samples with half weight.  The
     latter approach is appropriate for a PSU that was
     deterministically sampled.

     A stratum with more than two PSUs can be split into multiple
     smaller strata each with two PSUs or the PSUs can be merged to
     give two superclusters within the stratum.

     When merging small strata or grouping PSUs in large strata the
     'match' variable is used to sort PSUs before merging, to give
     approximate matching on this variable.

     If you want more control than this you should probably construct
     your own weights using the Hadamard matrices produced by
     'hadamard'

_V_a_l_u_e:

     For 'brrweights' with 'only.weights=FALSE' a list with elements 

 weights: two-column matrix indicating the weight for each half-stratum
          in one particular set  of split samples

 wstrata: New stratum variable incorporating merged or split strata

  strata: Original strata for distinct PSUs

     psu: Distinct PSUs

  npairs: Dimension of Hadamard matrix used in BRR construction

 sampler: function returning replicate weights

compress: Indicates whether the 'sampler' returns per PSU or per
          observation weights

     For 'jk1weights' and 'jknweights' a data frame of replicate
     weights and the 'scale' and 'rscale' arguments to 'svrVar'.

_R_e_f_e_r_e_n_c_e_s:

     Levy and Lemeshow "Sampling of Populations". Wiley.

     Shao and Tu "The Jackknife and Bootstrap". Springer.

_S_e_e _A_l_s_o:

     'hadamard', 'as.svrepdesign', 'svrVar', 'surveyoptions'

_E_x_a_m_p_l_e_s:

     data(scd)
     scdnofpc<-svydesign(data=scd, prob=~1, id=~ambulance, strata=~ESA,
     nest=TRUE)

     ## convert to BRR replicate weights
     scd2brr <- as.svrepdesign(scdnofpc, type="BRR")
     svymean(~alive, scd2brr)
     svyratio(~alive, ~arrests, scd2brr)

     ## with user-supplied hadamard matrix
     scd2brr1 <- as.svrepdesign(scdnofpc, type="BRR", hadamard.matrix=paley(11))
     svymean(~alive, scd2brr1)
     svyratio(~alive, ~arrests, scd2brr1)

