| Title: | USA Presidential Elections Data |
|---|---|
| Description: | This includes a dataset on the outcomes of the USA presidential elections since 1920, and various predictors, as used in <https://www.vanderwalresearch.com/blog/15-elections>. |
| Authors: | Willem M. van der Wal <[email protected]> |
| Maintainer: | Willem M. van der Wal <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.0.1 |
| Built: | 2024-12-06 06:28:12 UTC |
| Source: | CRAN |
This is a dataset with the outcomes of the USA presidential elections since 1920. I have used this dataset in my blog describing predictive models for the 2020 election. The data include not only the winner and loser of each election, but also the popular vote margin, turnout and information on the development of the Dow Jones index and the per capita disposable income in the four years before each election. Willem M. van der Wal, PhD (vanderwalresearch.com).
data(eldat)data(eldat)
A data frame with observations on the following variables:
electionyearCalendar year in which the election was held.
presel.DateDate at which the election was held.
winnerName of the winner.
winnerpartyParty of the winner.
winnerparty.tmin1Party of the winner, one election earlier.
winnerparty.tmin2Party of the winner, two elections earlier.
winnerparty.tmin3 Party of the winner, three elections earlier.
winnerparty.tmin4Party of the winner, four elections earlier.
runnerupName of the runner up.
runneruppartyParty of the runner up.
popvotepercmargin.repPopular vote margin (%) of the republican party as compared to the democratic party.
popvotepercmargin.rep.tmin1Popular vote margin (%) of the republican party as compared to the democratic party, one election earlier.
turnoutpercTurnout (%).
turnoutperc.tmin1 Turnout (%), one election earlier.
djia.reldiffThe relative change (%) of the Dow Jones index in the four years before the election.
dispincomePer capita disposable income (2009 dollars) in the calendar year of the election.
dispincchangeRelative change (%) of the per capita disposable income over the four years before the election.
The "tmin..." variables, djia.reldiff and dispincchange could be used as possible predictors in models that predict the outcome of the election.
Willem M. van der Wal [email protected], vanderwalresearch.com.
I used the following sources for these data: Complete List Of All The Presidents Of The United States, List of Presidents of the United States, List of United States presidential elections by popular vote margin, Dow Jones Industrial Average and Bureau of Economic Analysis - National Data - GDP & Personal Income.
#Example 1: fit model for probability that the winner is a republican, #using only the outcomes of the last two elections. #Load data data(eldat) #Fit model for probability that the winner is a republican elmod <- glm(winnerparty == "Rep." ~ winnerparty.tmin1*winnerparty.tmin2, data = eldat, family = binomial(link = logit)) summary(elmod) #ok, coefficients clearly illustrate "pendulum" effect, #don't mind the p-values because of small sample size #Prediction from elmod, with cutoff 0.5 eldat$p.elmod <- predict.glm(elmod, type = "response") #predicted probability eldat$pred.elmod <- ifelse(eldat$p.elmod > 0.5, "Rep.", "Dem.") #predicted outcome with(eldat, table(pred.elmod, winnerparty)) #crosstable 100*sum(with(eldat, winnerparty == pred.elmod))/nrow(eldat) #% correctly predicted #76% correct #indicator wrong/right prediction eldat$ind.elmod <- with(eldat, ifelse(winnerparty == pred.elmod, "OK", "WRONG!")) #show prediction eldat[, c("electionyear", "winner", "winnerparty", "pred.elmod", "p.elmod", "ind.elmod")] #25-fold crossvalidation with 1-24 split #(leave out one, fit model, predict for the observation left out) eldat$p.elmod.CV <- NA #predicted cross-validated probability (first fill with NAs) for(i in 1:25){ tempmod <- glm(winnerparty == "Rep." ~ winnerparty.tmin1*winnerparty.tmin2, data = eldat[-i,], family = binomial(link = logit)) #fit model on training data eldat$p.elmod.CV[i] <- predict.glm(tempmod, type = "response", newdata = eldat[i,])[[1]] #predicted probability for test data } #Evaluate the predictions from the crossvalidation eldat$pred.elmod.CV <- ifelse(eldat$p.elmod.CV > 0.5, "Rep.", "Dem.") #predicted outcome with(eldat, table(pred.elmod.CV, winnerparty)) #crosstable 100*sum(with(eldat, winnerparty == pred.elmod.CV))/nrow(eldat) #% correctly predicted #still 76% correct eldat$ind.elmod.CV <- with(eldat, ifelse(winnerparty == pred.elmod.CV, "OK", "WRONG!")) eldat[,c("electionyear", "winner", "winnerparty", "pred.elmod.CV", "p.elmod.CV", "ind.elmod.CV")] #Overview 100*sum(with(eldat, winnerparty == pred.elmod))/nrow(eldat) #Without CV: 76% correct 100*sum(with(eldat, winnerparty == pred.elmod.CV))/nrow(eldat) #With CV: 76% correct#Example 1: fit model for probability that the winner is a republican, #using only the outcomes of the last two elections. #Load data data(eldat) #Fit model for probability that the winner is a republican elmod <- glm(winnerparty == "Rep." ~ winnerparty.tmin1*winnerparty.tmin2, data = eldat, family = binomial(link = logit)) summary(elmod) #ok, coefficients clearly illustrate "pendulum" effect, #don't mind the p-values because of small sample size #Prediction from elmod, with cutoff 0.5 eldat$p.elmod <- predict.glm(elmod, type = "response") #predicted probability eldat$pred.elmod <- ifelse(eldat$p.elmod > 0.5, "Rep.", "Dem.") #predicted outcome with(eldat, table(pred.elmod, winnerparty)) #crosstable 100*sum(with(eldat, winnerparty == pred.elmod))/nrow(eldat) #% correctly predicted #76% correct #indicator wrong/right prediction eldat$ind.elmod <- with(eldat, ifelse(winnerparty == pred.elmod, "OK", "WRONG!")) #show prediction eldat[, c("electionyear", "winner", "winnerparty", "pred.elmod", "p.elmod", "ind.elmod")] #25-fold crossvalidation with 1-24 split #(leave out one, fit model, predict for the observation left out) eldat$p.elmod.CV <- NA #predicted cross-validated probability (first fill with NAs) for(i in 1:25){ tempmod <- glm(winnerparty == "Rep." ~ winnerparty.tmin1*winnerparty.tmin2, data = eldat[-i,], family = binomial(link = logit)) #fit model on training data eldat$p.elmod.CV[i] <- predict.glm(tempmod, type = "response", newdata = eldat[i,])[[1]] #predicted probability for test data } #Evaluate the predictions from the crossvalidation eldat$pred.elmod.CV <- ifelse(eldat$p.elmod.CV > 0.5, "Rep.", "Dem.") #predicted outcome with(eldat, table(pred.elmod.CV, winnerparty)) #crosstable 100*sum(with(eldat, winnerparty == pred.elmod.CV))/nrow(eldat) #% correctly predicted #still 76% correct eldat$ind.elmod.CV <- with(eldat, ifelse(winnerparty == pred.elmod.CV, "OK", "WRONG!")) eldat[,c("electionyear", "winner", "winnerparty", "pred.elmod.CV", "p.elmod.CV", "ind.elmod.CV")] #Overview 100*sum(with(eldat, winnerparty == pred.elmod))/nrow(eldat) #Without CV: 76% correct 100*sum(with(eldat, winnerparty == pred.elmod.CV))/nrow(eldat) #With CV: 76% correct