pscl (version 1.5.2)

AustralianElections: elections to Australian House of Representatives, 1949-2016

Description

Aggregate data on the 24 elections to Australia's House of Representatives, 1949 to 2016.

Usage

data(AustralianElections)

Format

A data frame with the following variables:

date

date of election, stored using the Date class

Seats

numeric, number of seats in the House of Representatives

Uncontested

numeric, number of uncontested seats

ALPSeats

numeric, number of seats won by the Australian Labor Party

LPSeats

numeric, number of seats won by the Liberal Party

NPSeats

numeric, number of seats won by the National Party (previously known as the Country Party)

OtherSeats

numeric, number of seats won by other parties and/or independent candidates

ALP

numeric, percentage of first preference votes cast for Australian Labor Party candidates

ALP2PP

numeric, percentage of the two-party preferred vote won by Australian Labor Party candidates

LP

numeric, percent of first preference votes cast for Liberal Party candidates

NP

numeric, percent of first preference votes cast for National Party (Country Party) candidates

DLP

numeric, percent of first preference votes cast for Democratic Labor Party candidates

Dem

numeric, percent of first preference votes cast for Australian Democrat candidates

Green

numeric, percent of first preference votes cast for Green Party candidates

Hanson

numeric, percent of first preference votes cast for candidates from Pauline Hanson's One Nation party

Com

numeric, percent of first preference votes cast for Communist Party candidates

AP

numeric, percent of first preference votes cast for Australia Party candidates

Informal

numeric, percent of ballots cast that are spoiled, blank, or otherwise uncountable (usually because of errors in enumerating preferences)

Turnout

numeric, percent of enrolled voters recorded as having turned out to vote (Australia has compulsory voting)

References

Jackman, Simon. 2009. Bayesian Analysis for the Social Sciences. Wiley: Hoboken, New Jersey. Example 3.5.

Examples

# NOT RUN {
data(AustralianElections)
attach(AustralianElections)
alpSeatShare <- ALPSeats/Seats
alpVoteShare <- ALP2PP/100

## log-odds transforms
x <- log(alpVoteShare/(1-alpVoteShare))
y <- log(alpSeatShare/(1-alpSeatShare))

ols <- lm(y~x)   ## Tufte-style seats-votes regression

xseq <- seq(-4.5,4.5,length=500)
yhat <- coef(ols)[1] + coef(ols)[2]*xseq
yhat <- exp(yhat)/(1+exp(yhat))
xseq <- exp(xseq)/(1+exp(xseq))

## seats vote curve
plot(x=alpVoteShare,
     y=alpSeatShare,
     xlab="ALP Vote Share",
     ylab="ALP Seat Share")
lines(xseq,yhat,lwd=2)
abline(h=.5,lty=2)
abline(v=.5,lty=2)
# }