postStratify             package:survey             R Documentation

_P_o_s_t-_s_t_r_a_t_i_f_y _a _s_u_r_v_e_y

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

     Post-stratification adjusts the sampling and replicate weights so
     that the joint distribution of a set of post-stratifying variables
     matches the known population joint distribution. Use 'rake' when
     the full joint distribution is not available.

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

     postStratify(design, strata, population, partial = FALSE, ...)
     ## S3 method for class 'svyrep.design':
     postStratify(design, strata, population, partial = FALSE, compress=NULL,...)
     ## S3 method for class 'survey.design':
     postStratify(design, strata, population, partial = FALSE, ...)

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

  design: A survey design with replicate weights

  strata: A formula or data frame of post-stratifying variables 

population: A 'table', 'xtabs' or 'data.frame' with population
          frequencies 

 partial: if 'TRUE', ignore population strata not present in the sample

compress: Attempt to compress the replicate weight matrix? When 'NULL'
          will attempt to compress if the original weight matrix was
          compressed

     ...: arguments for future expansion

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

     The 'population' totals can be specified as a table with the
     strata variables in the margins, or as a data frame where one
     column lists frequencies and the other columns list the unique
     combinations of strata variables (the format produced by
     'as.data.frame' acting on a 'table' object).   A table must have
     named dimnames to indicate the variable names.

     Compressing the replicate weights will take time and may even
     increase memory use if there is actually little redundancy in the
     weight matrix (in particular if the post-stratification variables
     have many values and cut  across PSUs).

     If a 'svydesign' object is to be converted to a replication design
     the post-stratification should be performed after conversion.

     The variance estimate for replication designs follows the same
     procedure as Valliant (1993) described for estimating totals. Rao
     et al (2002) describe this procedure for estimating functions (and
     also the GREG or g-calibration procedure, see 'calibrate')

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

     A new survey design object.

_N_o_t_e:

     If the sampling weights are already post-stratified there will be
     no change in point estimates after 'postStratify' but the standard
     error estimates will decrease to correctly reflect the
     post-stratification. See  <URL:
     http://www.dcs.napier.ac.uk/peas/exemplar1.htm> for an example.

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

     Valliant R (1993) Post-stratification and conditional variance
     estimation. JASA 88: 89-96  

     Rao JNK, Yung W, Hidiroglou MA (2002)   Estimating equations for
     the analysis of survey data using poststratification information.
     Sankhya 64 Series A Part 2, 364-378.

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

     'rake', 'calibrate' for other things to do with auxiliary
     information

     'compressWeights' for information on compressing weights

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

     data(api)
     dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)
     rclus1<-as.svrepdesign(dclus1)

     svymean(~api00, rclus1)
     svytotal(~enroll, rclus1)

     # post-stratify on school type
     pop.types <- data.frame(stype=c("E","H","M"), Freq=c(4421,755,1018))
     #or: pop.types <- xtabs(~stype, data=apipop)
     #or: pop.types <- table(stype=apipop$stype)

     rclus1p<-postStratify(rclus1, ~stype, pop.types)
     summary(rclus1p)
     svymean(~api00, rclus1p)
     svytotal(~enroll, rclus1p)

     ## and for svydesign objects
     dclus1p<-postStratify(dclus1, ~stype, pop.types)
     summary(dclus1p)
     svymean(~api00, dclus1p)
     svytotal(~enroll, dclus1p)

