postStratify             package:survey             R Documentation

_P_o_s_t-_s_t_r_a_t_i_f_y _a _r_e_p_l_i_c_a_t_e _w_e_i_g_h_t _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. The advantage of
     post-stratification is that sampling frames need not be available
     for the strata.

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

     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

_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.

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

     A new survey design object.

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

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

     'as.svrepdesign', 'svrepdesign', 'rake'

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

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

     svrepmean(~api00, rclus1)
     svreptotal(~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)
     svrepmean(~api00, rclus1p)
     svreptotal(~enroll, rclus1p)

