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:

     ## S3 method for class 'survey.design':
     svymean(x, design, na.rm=FALSE,deff=FALSE,...) 
     ## S3 method for class 'svyrep.design':
     svymean(x, design, na.rm=FALSE, rho=NULL,
       return.replicates=FALSE, deff=FALSE,...) 
     ## S3 method for class 'survey.design':
     svyvar(x, design, na.rm=FALSE,...) 
     ## S3 method for class 'svyrep.design':
     svyvar(x, design, na.rm=FALSE, rho=NULL,
        return.replicates=FALSE,...) 
     ## S3 method for class 'survey.design':
     svytotal(x, design, na.rm=FALSE,deff=FALSE,...) 
     ## S3 method for class 'svyrep.design':
     svytotal(x, design, na.rm=FALSE, rho=NULL,
        return.replicates=FALSE, deff=FALSE,...)
     ## S3 method for class 'svystat':
     coef(object,...)
     ## S3 method for class 'svrepstat':
     coef(object,...)
     ## S3 method for class 'svystat':
     vcov(object,...)
     ## S3 method for class 'svrepstat':
     vcov(object,...)
     cv(object,...)
     deff(object, quietly=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 cases with missing values be dropped?

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

return.replicates: Return the replicate means?

    deff: Return the design effect (see below)

  object: The result of one of the other survey summary functions

 quietly: Don't warn when there is no design effect computed

     ...: additional arguments to 'cv' methods,not currently used

_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.

     Factor variables are converted to sets of indicator variables for
     each category in computing means and totals. Combining this with
     the 'interaction' function, allows crosstabulations. See
     'ftable.svystat' for formatting the output.

     With 'na.rm=TRUE', all cases with missing data are removed. With
     'na.rm=FALSE' cases with missing data are not removed and so will
     produce missing results.  When using replicate weights and
     'na.rm=FALSE' it may be useful to set
     'options(na.action="na.pass")', otherwise all replicates with any
     missing results will be discarded.

     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 from a study of the same size using simple random
     sampling without replacement. Note that the design effect will be
     incorrect if the weights have been rescaled so that they are not
     reciprocals of sampling probabilities.  To obtain an estimate of
     the design effect comparing to simple random sampling with
     replacement, which does not have this requirement, use
     'deff="replace"'. This with-replacement design effect is the
     square of Kish's "deft".

     The 'cv' function computes the coefficient of variation of a
     statistic such as ratio, mean or total.

_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',  'svyquantile' , 
     'ftable.svystat'

_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)
       svymean(~factor(stype),dclus1)
       svymean(~interaction(stype, comp.imp), dclus1)
       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)
       svymean(~api00, jkstrat)
       svymean(~factor(stype),jkstrat)
       svyvar(~api00,jkstrat)
       svyquantile(~api00, jkstrat, c(.25,.5,.75))
       svytotal(~enroll, jkstrat)
       svyratio(~api.stu, ~enroll, jkstrat)

       # coefficients of variation
       cv(svytotal(~enroll,dstrat))
       cv(svyratio(~api.stu, ~enroll, jkstrat))

       # extracting statistic and variance
       coef(svytotal(~enroll,dstrat))
       vcov(svymean(~api00+api99,jkstrat))

       # Design effect
       svymean(~api00, dstrat, deff=TRUE)
       svymean(~api00, dstrat, deff="replace")
       svymean(~api00, jkstrat, deff=TRUE)
       svymean(~api00, jkstrat, deff="replace")
      (a<-svytotal(~enroll, dclus1, deff=TRUE))
       deff(a)

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

      

