############################missing data############################
library(survey)
## generate data
set.seed(100)
x<-rnorm(20)
ex.data<-as.data.frame(x)
ex.data[5,]<-NA

## calculate mean and total
#5% sample is missing, so presumably about 5% of the population
#has missing data. Expect the total over the non-missing part of the population
#to be 5% smaller than the total over the whole population
des <- svydesign(ids=~1, data=ex.data,fpc=rep(100,20))
smean<-svymean(~x, design=des, na.rm=TRUE)
stotal<-svytotal(~x, design=des, na.rm=TRUE)
smean
stotal

##want to do reweighting, so that the totals estimate the whole 
##population,missing and non-missing
rwt<-calibrate(subset(des,!is.na(x)),formula=~1,population=100)
svymean(~x,rwt)
svytotal(~x,rwt)