svyratio               package:survey               R Documentation

_R_a_t_i_o _e_s_t_i_m_a_t_i_o_n

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

     Ratio estimation and estimates of totals based on ratios for
     complex survey samples.

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

     ## S3 method for class 'survey.design2':
     svyratio(numerator, denominator, design,separate=FALSE,...)
     ## S3 method for class 'svyrep.design':
     svyratio(numerator, denominator, design,...)
     ## S3 method for class 'svyratio':
     predict(object, total, se=TRUE,...)
     ## S3 method for class 'svyratio_separate':
     predict(object, total, se=TRUE,...)

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

numerator: formula, expression, or data frame giving numerator
          variable(s)

denominator: formula, expression, or data frame giving denominator
          variable(s) 

  design: from 'svydesign' for 'svyratio', from 'svrepdesign' for
          'svrepratio'

  object: result of 'svyratio'

   total: vector of population totals for the denominator variables in
          'object', or list of vectors of  population stratum totals if
          'separate=TRUE'

      se: Return standard errors?

separate: Estimate ratio separately for strata

     ...: Other unused arguments for other methods

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

     The separate ratio estimate of a total is the sum of ratio
     estimates in each stratum.  If the stratum totals supplied in the
     'total' argument and the strata in the design object both have
     names these names will be matched. If they do not have names it is
     important that the sample totals are supplied in the correct
     order, the same order as shown in the output of 'summary(design)'.

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

     'svyratio' returns an object of class 'svyratio'. The 'predict'
     method returns a matrix of population totals and optionally a
     matrix of standard errors.

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

     Thomas Lumley

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

     Levy and Lemeshow. "Sampling of Populations" (3rd edition). Wiley

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

     'svydesign'

     'svymean' for estimating proportions and domain means

     'calibrate' for estimators related to the separate ratio
     estimator.

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

     data(scd)

     ## survey design objects
     scddes<-svydesign(data=scd, prob=~1, id=~ambulance, strata=~ESA,
     nest=TRUE, fpc=rep(5,6))
     scdnofpc<-svydesign(data=scd, prob=~1, id=~ambulance, strata=~ESA,
     nest=TRUE)

     # convert to BRR replicate weights
     scd2brr <- as.svrepdesign(scdnofpc, type="BRR")

     # use BRR replicate weights from Levy and Lemeshow
     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)

     # ratio estimates
     svyratio(~alive, ~arrests, design=scddes)
     svyratio(~alive, ~arrests, design=scdnofpc)
     svyratio(~alive, ~arrests, design=scd2brr)
     svyratio(~alive, ~arrests, design=scdrep)

     data(api)
     dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)

     ## domain means are ratio estimates, but available directly
     svyratio(~I(api.stu*(comp.imp=="Yes")), ~as.numeric(comp.imp=="Yes"), dstrat)
     svymean(~api.stu, subset(dstrat, comp.imp=="Yes"))

     ## separate and combined ratio estimates of total
     (sep<-svyratio(~api.stu,~enroll, dstrat,separate=TRUE))
     (com<-svyratio(~api.stu, ~enroll, dstrat))

     stratum.totals<-list(E=1877350, H=1013824, M=920298)

     predict(sep, total=stratum.totals)
     predict(com, total=sum(unlist(stratum.totals)))

