survey (version 1.9-2)

svydesign: Survey sample analysis.

Description

Specify a complex survey design.

Usage

svydesign(ids, probs=NULL, strata = NULL, variables = NULL, fpc=NULL,
data = NULL, nest = FALSE, check.strata = !nest, weights=NULL)

Arguments

ids

Formula or data frame specifying cluster ids from largest level to smallest level, ~0 is a formula for no clusters.

probs

Formula or data frame specifying cluster sampling probabilities

strata

Formula or vector specifying strata, use NULL for no strata

variables

Formula or data frame specifying the variables measured in the survey. If NULL, the data argument is used.

fpc

Finite population correction: see Details below

weights

Formula or vector specifying sampling weights as an alternative to prob

data

Data frame to look up variables in the formula arguments

nest

If TRUE, relabel cluster ids to enforce nesting, eg if ids at second level of sampling are reused within first-level units

check.strata

If TRUE, check that clusters are nested in strata

Value

• An object of class survey.design.

Details

When analysing data from a complex survey, observations must be weighted inversely to their sampling probabilities, and the effects of stratification and of correlation induced by cluster sampling must be incorporated in standard errors.

The svydesign object combines a data frame and all the survey design information needed to analyse it. These objects are used by the survey modelling and summary functions.

The finite population correction is used to reduce the variance when a substantial fraction of the total population of interest has been sampled. It may not be appropriate if the target of inference is the process generating the data rather than the statistics of a particular finite population.

The finite population correction can be specified either as the total population size in each stratum or as the fraction of the total population that has been sampled. In either case the relevant population size is `primary sampling units', the largest clusters. That is, sampling 100 units from a population stratum of size 500 can be specified as 100 or as 100/500=0.2. The finite population correction can be specified by a vector with one element for each individual (in which case it is an error for it to vary within a stratum) or as a data frame with one row per stratum. The first column of the data frame should be a factor with the same levels as strata and the second column the finite population correction.

The dim, "[", "[<-" and na.action methods for survey.design objects operate on the dataframe specified by variables and ensure that the design information is properly updated to correspond to the new data frame. With the "[<-" method the new value can be a survey.design object instead of a data frame, but only the data frame is used. See also subset.survey.design for a simple way to select subpopulations.

The value of options("survey.lonely.psu") controls what happens to strata containing only one cluster (PSU).See svyCprod for details.

See Also

svyglm, svymean, svyvar, svytable, svyquantile, subset.survey.design, update.survey.design

Examples

#population
  df<-data.frame(x=rnorm(1000),z=rep(0:4,200))
  df$y<-with(df, 3+3*x*z)
  #sampling fraction
  df$p<-with(df, exp(x)/(1+exp(x)))
  #sample
  xi<-rbinom(1000,1,df$p)
  sdf<-df[xi==1,]
  
  #survey design object: independent sampling, 
  dxi<-svydesign(~0,~p,data=sdf)
  dxi
  summary(dxi)
  svymean(sdf$x,dxi)	
  svymean(~x,dxi)
  svytable(~z, dxi)

   #cluster sampling: population
   df$id<-rep(1:250,each=4)
   df$clustp<-by(df,list(df$id),function(d) min(exp(d$x*d$z)/(1+exp(d$x*d$z))))[df$id]
   xi<-rbinom(250,1,df$clustp[4*(1:250)])
   sdf<-df[xi[df$id]==1,]
   
   #cluster sampling design
   dxi<-svydesign(~id,~clustp,data=sdf)
   
   dxi
   summary(dxi)
   svymean(~x+z,dxi)

   ## stratification
   df<-data.frame(z=rep(1:4,each=200), y=rnorm(800, rep(1:4,each=200)))
   xi<-c(sample(1:200,20), sample(201:400,20), sample(401:600,20), sample(601:800,20))
   sdf<-df[xi,]
   stratdx<-svydesign(id=~0,prob=~0,strata=~z,data=sdf)
   unstrat<-svydesign(id=~0,prob=~0,data=sdf)
   stratdx
   unstrat
   summary(stratdx)

   svymean(~y, stratdx)  ##higher precision
   svymean(~y, unstrat)