surveysummary             package:survey             R Documentation

_S_u_m_m_a_r_y _s_t_a_t_i_s_t_i_c_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 means, variances, ratios and totals for data from complex
     surveys.

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

     svymean(x, design, na.rm=FALSE,deff=FALSE) 
     svrepmean(x, design, na.rm=FALSE, rho=NULL, return.replicates=FALSE, deff=FALSE) 
     svyvar(x, design, na.rm=FALSE) 
     svrepvar(x, design, na.rm=FALSE, rho=NULL, return.replicates=FALSE) 
     svytotal(x, design, na.rm=FALSE,deff=FALSE) 
     svreptotal(x, design, na.rm=FALSE, rho=NULL, return.replicates=FALSE, deff=FALSE) 

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

       x: A formula, vector or matrix

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

   na.rm: Should missing values be removed?

     rho: parameter for Fay's variance estimator in a BRR design

return.replicates: Return the replicate means?

    deff: Return the design effect

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

     These functions perform weighted estimation, with each observation
     being weighted by the inverse of its sampling probability.  Except
     for the table functions, these also give precision estimates that
     incorporate the effects of stratification and clustering.

     The 'svytotal' and 'svreptotal' functions estimate a population
     total.  Use 'predict' on 'svyratio', 'svrepratio', 'svyglm',
     'svrepglm' to get ratio or regression estimates of totals.

     The design effect compares the variance of a mean or total to the
     variance of a simple random sample of the same size.  Although
     strictly speaking this should be a simple random sample without
     replacement, we compute as if it were a simple random sample with
     replacement.

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

     Objects of class '"svystat"' or '"svrepstat"', which are vectors
     with a '"var"' attribute giving the variance and a '"statistic"'
     attribute giving the name of the statistic.

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

     Thomas Lumley

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

     'svydesign', 'as.svrepdesign', 'svrepdesign', 'svyCprod', 'mean',
     'var', 'svyquantile'

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

       data(api)
       ## population
       mean(apipop$api00)
       quantile(apipop$api00,c(.25,.5,.75))
       var(apipop$api00)
       sum(apipop$enroll)
       sum(apipop$api.stu)/sum(apipop$enroll)

       ## one-stage cluster sample
       dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)
       summary(dclus1)
       svymean(~api00, dclus1, deff=TRUE)
       svyquantile(~api00, dclus1, c(.25,.5,.75))
       svyvar(~api00, dclus1)
       svytotal(~enroll, dclus1, deff=TRUE)
       svyratio(~api.stu, ~enroll, dclus1)

       #stratified sample
       dstrat<-svydesign(id=~1, strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)
       summary(dstrat)
       svymean(~api00, dstrat)
       svyquantile(~api00, dstrat, c(.25,.5,.75))
       svyvar(~api00, dstrat)
       svytotal(~enroll, dstrat)
       svyratio(~api.stu, ~enroll, dstrat)

       # replicate weights - jackknife (this is slow)
       jkstrat<-as.svrepdesign(dstrat)
       summary(jkstrat)
       svrepmean(~api00, jkstrat)
       svrepvar(~api00,jkstrat)
       svrepquantile(~api00, jkstrat, c(.25,.5,.75))
       svreptotal(~enroll, jkstrat)
       svrepratio(~api.stu, ~enroll, jkstrat)

       # 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)
       svrepmean(~arrests+alive, design=scdrep)

      

