election {survey} R Documentation

US 2004 presidential election data at state or county level

Description

A sample of voting data from US states or counties (depending on data availability), sampled with probability proportional to number of votes. The sample was drawn using Tille's splitting method, implemented in the "sampling" package.

data(election)

Format

election is a data frame with 4600 observations on the following 8 variables.

County
A factor specifying the state or country
TotPrecincts
Number of precincts in the state or county
PrecinctsReporting
Number of precincts supplying data
Bush
Kerry
Total votes for those three candidates
p

election_pps is a sample of 40 counties or states taken with probability proportional to the number of votes. It includes the additional column wt with the sampling weights.

election_insample indicates which rows of election were sampled.

election_jointprob are the pairwise sampling probabilities and election_jointHR are approximate pairwise sampling probabilities using the Hartley-Rao approximation.

.

Examples

data(election)
## high positive correlation between totals
plot(Bush~Kerry,data=election,log="xy")
## high negative correlation between proportions

## Variances without replacement
## Horvitz-Thompson type
dpps_br<- svydesign(id=~1,  fpc=~p, data=election_pps, pps="brewer")
dpps_ov<- svydesign(id=~1,  fpc=~p, data=election_pps, pps="overton")
dpps_hr<- svydesign(id=~1,  fpc=~p, data=election_pps, pps=HR(sum(election\$p^2)/40))
dpps_hr1<- svydesign(id=~1, fpc=~p, data=election_pps, pps=HR())
dpps_ht<- svydesign(id=~1,  fpc=~p, data=election_pps, pps=ppsmat(election_jointprob))
## Yates-Grundy type
dpps_yg<- svydesign(id=~1,  fpc=~p, data=election_pps, pps=ppsmat(election_jointprob),variance="YG")
dpps_hryg<- svydesign(id=~1,  fpc=~p, data=election_pps, pps=HR(sum(election\$p^2)/40),variance="YG")

## The with-replacement approximation
dppswr <-svydesign(id=~1, probs=~p, data=election_pps)