svyquantile              package:survey              R Documentation

_Q_u_a_n_t_i_l_e_s _f_o_r _s_a_m_p_l_e _s_u_r_v_e_y_s

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

     Compute quantiles for data from complex surveys.

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

     svyquantile(x, design, quantiles, alpha=0.05, ci=FALSE,method = "linear", f = 1)
     svrepquantile(x, design, quantiles, method = "linear", f = 1, return.replicates=FALSE)

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

       x: A formula, vector or matrix

  design: 'survey.design' or 'svyrep.design' object

quantiles: Quantiles to estimate

  method: see 'approxfun'

       f: see 'approxfun'

      ci: Compute a confidence interval (relatively slow)?

   alpha: Level for confidence interval

return.replicates: Return the replicate means?

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

     Interval estimation for quantiles is complicated, because the
     influence function is not continuous.  Linearisation cannot be
     used, and only some replication weight designs give valid results.

     For 'svyrepquantile' we use the method of Francisco-Fuller, which
     corresponds to inverting a robust score test.  At the upper and
     lower limits of the confidence interval, a test of the null
     hypothesis that the cumulative distribution function is equal to
     the target quantile just rejects.

     For 'svrepquantile' ordinary replication-based standard errors are
     computed.  These are not valid for the JK1 and JKn jackknife
     designs. They are valid for BRR and Fay's method, and for some
     bootstrap-based designs.

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

     'svyquantile' returns a list whose first component is the
     quantiles and second component is the confidence intervals.
     'svrepquantile' returns an object of class 'svyrepstat'.

_A_u_t_h_o_r(_s):

     Thomas Lumley

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

     Binder DA (1991) Use of estimating functions for interval
     estimation from complex surveys. _Journal of Official Statistics_ 
     1991: 34-42 Shao J, Tu D (1995) _The Jackknife and Bootstrap_.
     Springer.

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

     'svydesign', 'svymean', 'as.svrepdesign', 'svrepdesign'

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

       data(api)
       ## population
       quantile(apipop$api00,c(.25,.5,.75))

       ## one-stage cluster sample
       dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)
       svyquantile(~api00, dclus1, c(.25,.5,.75),ci=TRUE)

       #stratified sample
       dstrat<-svydesign(id=~1, strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)
       svyquantile(~api00, dstrat, c(.25,.5,.75),ci=TRUE)

       # BRR method
       data(scd)
       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)
       svrepquantile(~arrests+alive, design=scdrep, quantile=0.5)

      

