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Data and Code to Accompany Generalized Linear Models, 2nd Edition

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campaign: Data on campaign contributions in California in the 2014...

campaign: Data on campaign contributions in California in the 2014...
In GLMpack: Data and Code to Accompany Generalized Linear Models, 2nd Edition

Description Usage Format Examples

Description

Data for the campaign contributions example used in chapter 6

Usage

1
data(campaign)

Format

A data frame with 180 rows and 16 variables:

DTRCT

California district

CANDID

FEC ID

CYCLE

Election cycle

NAME

Name of the candidate

INCUMCHALL

Incumbency status

CFSCORE

CFscore

CANDGENDER

Gender of the candidate

PARTY

Party of the candidate

TOTCONTR

Contributions to the 2014 electoral campaigns in the 53 districts of California in the U.S. House of Representatives

TOTPOP

Total state population

FEMALE

Number of female citizens in the state

WHITE

Number of white citizens in the state

HISP

Number of Hispanic citizens in the state

FEMALEPCT

Percentage of female citizens in the state

WHITEPCT

Percentage of white citizens in the state

HISPPCT

Percentage of Hispanic citizens in the state

...

Examples

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data(campaign)
attach(campaign)
library(pBrackets)

## Gamma model
cmpgn.out <- glm(TOTCONTR ~ CANDGENDER + PARTY + INCUMCHALL + HISPPCT,
             family=Gamma(link = 'log'), data=campaign)

## Linear model
cmpgn.out_lm <- lm(TOTCONTR ~ CANDGENDER + PARTY + INCUMCHALL + HISPPCT, data=campaign)

## Table 6.8
round(glm.summary(cmpgn.out),4)
cmpgn.out$null.deviance
cmpgn.out$df.null
cmpgn.out$deviance
cmpgn.out$df.residual
logLik(cmpgn.out)
cmpgn.out$aic

## Table 6.9
summary(cmpgn.out_lm)
confint(cmpgn.out_lm)

## Figure 6.4
opar = par(mfrow=c(1,1), mar=c(5.1,4.1,4.1,2.1), oma=c(0,0,0,0))
par(mar=c(4,3,3,0),oma=c(1,1,1,1))
hist(campaign$TOTCONTR,xlab="",ylab="", yaxt="n", xaxt="n",
     xlim=c(0,9000), ylim=c(0, 130), main="",
     col = "gray40")
axis(1, tck=0.03, cex.axis=0.9, mgp=c(0.3, 0.3, 0), lty=1, lwd=0, lwd.ticks = 1)
axis(2, tck=0.03, cex.axis=0.9, mgp=c(0.3, 0.3, 0), lty=1, lwd=1, lwd.ticks = 1, las=2)
title(xlab = 'Total campaign contributions (thousands of dollars)',
      ylab= "Frequency",
      line = 1.7, cex.lab=1)
title(line = 1, main="Distribution of campaign contributions", font.main=1)
par(opar)

## Figure 6.5
campaign.mu <- predict(cmpgn.out_lm)
campaign.y <- campaign$TOTCONTR
par(mfrow=c(1,3), mar=c(3,3,2,1),oma=c(1,1,1,1))
plot(campaign.mu,campaign.y,xlab="",ylab="", yaxt='n', xaxt="n", pch="+")
axis(1, tck=0.02, cex.axis=0.9, mgp=c(0.3, 0.3, 0), lty=1, lwd=0, lwd.ticks = 1)
axis(2, tck=0.02, cex.axis=0.9, mgp=c(0.3, 0.3, 0), lty=1, lwd=0, lwd.ticks = 1, las=2)
title(xlab = "Fitted values", ylab="Observed values",
      line = 1.7, cex.lab=1.3)
title(main="Model Fit Plot",
      line = 1, cex.main=1.7, font.main=1)
abline(lm(campaign.y~campaign.mu)$coefficients, lwd=2)
plot(fitted(cmpgn.out_lm),resid(cmpgn.out_lm,type="pearson"),xlab="",ylab="",
     yaxt='n', xaxt="n", pch="+")
axis(1, tck=0.02, cex.axis=0.9, mgp=c(0.3, 0.3, 0), lty=1, lwd=0, lwd.ticks = 1)
axis(2, tck=0.02, cex.axis=0.9, mgp=c(0.3, 0.3, 0), lty=1, lwd=0, lwd.ticks = 1, las=2)
title(xlab = "Fitted values", ylab="Pearson Residuals",
      line = 1.7, cex.lab=1.3)
title(main="Residual Dependence Plot",
      line = 1, cex.main=1.7, font.main=1)
abline(0,0, lwd=2)
plot(cmpgn.out_lm,which=2, pch="+",
     sub.caption = "", caption = "", mgp=c(1.5, 0.3, 0),
     tck=0.02, cex.axis=0.9, cex.lab=1.3, lty=1)
title(main="Normal-Quantile Plot",
      line = 1, cex.main=1.7, font.main=1)
par(opar)

## Figure 6.6
mygray = rgb(153, 153, 153, alpha = 200, maxColorValue = 255)
newdat_gender <- data.frame(CANDGENDER = c('F','M'), PARTY= rep('Democrat',2),
                            INCUMCHALL=rep("C", 2), HISPPCT=rep(mean(campaign$HISPPCT),2))
newdat_party <- data.frame(CANDGENDER = rep('M', 3), PARTY= c('Democrat','Republican',
                           'Independent'), INCUMCHALL=rep("C", 3),
                           HISPPCT=rep(mean(campaign$HISPPCT),3))
newdat_incumchall <- data.frame(CANDGENDER = rep('M', 3), PARTY= rep('Democrat',3),
                                INCUMCHALL=c('C', 'I', 'O'),
                                HISPPCT=rep(mean(campaign$HISPPCT),3))
newdat_hisiq <- data.frame(CANDGENDER = rep('M', 2), PARTY= rep('Democrat',2),
                           INCUMCHALL=rep("C", 2),
                           HISPPCT=as.numeric(summary(campaign$HISPPCT)[c(2,5)]))
newdat_hispf <- data.frame(CANDGENDER = rep('M', 200), PARTY= rep('Democrat',200),
                           INCUMCHALL=rep("C", 200), HISPPCT=seq(.1, .9, length.out = 200))
preds_gender <- predict(cmpgn.out, newdata = newdat_gender, se.fit = TRUE)
preds_party <- predict(cmpgn.out, newdata = newdat_party, se.fit = TRUE)
preds_incumchall <- predict(cmpgn.out, newdata = newdat_incumchall, se.fit = TRUE)
preds_hispiq <- predict(cmpgn.out, newdata = newdat_hisiq, se.fit = TRUE)
preds_hispf <- predict(cmpgn.out, newdata = newdat_hispf, se.fit = TRUE)
cis_gender <- round(glm.cis(preds_gender$fit, preds_gender$se.fit, 0.95,cmpgn.out$df.residual),4)
cis_party <- round(glm.cis(preds_party$fit, preds_party$se.fit, 0.95,cmpgn.out$df.residual),4)
cis_incumchall <- round(glm.cis(preds_incumchall$fit, preds_incumchall$se.fit, 0.95,
                                cmpgn.out$df.residual),4)
cis_hispiq <- round(glm.cis(preds_hispiq$fit, preds_hispiq$se.fit, 0.95,cmpgn.out$df.residual),4)
cis_hispf <- round(glm.cis(preds_hispf$fit, preds_hispf$se.fit, 0.95,cmpgn.out$df.residual),4)
iqrange = cbind(summary(campaign$HISPPCT)[c(2,5)],cis_hispiq[2,4] - cis_hispf[1,4])
par(mfrow=c(2,2), mar=c(4,3,3,0),oma=c(1,1,1,1))
plot(1:2, cis_gender[,4], type="n",xlab="",ylab="",  yaxt="n", xaxt="n",
     xlim=c(0,3), ylim=c(100, 700))
segments(1:2, cis_gender[,5], 1:2, cis_gender[,6], lwd=2, col="gray60")
points(1:2, cis_gender[,4], pch=16, cex=2.5)
text(1:2, cis_gender[,4], labels = c("F", "M"), col="white", cex=0.9)
segments(1.05, cis_gender[1,4], 1.95, cis_gender[2,4], lty=2)
brackets(1, cis_gender[1,4]+20, 2, cis_gender[1,4]+20, h = 45,  ticks = 0.5, lwd=2)
text(1.5, cis_gender[1,4]+100, bquote(hat(y)['F']-hat(y)['M'] ~ '='
     ~ .(cis_gender[2,4]-cis_gender[1,4])), cex=0.9)
axis(1, at=1:2, labels = c("Female", "Male"), tck=0.03, cex.axis=0.9, mgp=c(0.3, 0.3, 0),
     lty=1, lwd=0, lwd.ticks = 1)
axis(2, tck=0.03, cex.axis=0.9, mgp=c(0.3, 0.3, 0), lty=1, lwd=0, lwd.ticks = 1, las=2)
title(xlab = 'Gender of candidate',
      ylab="Total campaign contributions",
      line = 1.7, cex.lab=1)
title(line = 1, main="Gender of candidate", font.main=3)
plot(1:3, cis_party[,4], type="n",xlab="",ylab="",  yaxt="n", xaxt="n",
     xlim=c(0.5,3.5), ylim=c(0, 600))
segments(1:3, cis_party[,5], 1:3, cis_party[,6], lwd=2, col="gray60")
points(1:3, cis_party[,4], pch=15:17, cex=2.5)
text(1:3, cis_party[,4], labels = c("D", "R", "I"), col="white", cex=0.8)
segments(c(1.05,2.05), cis_party[1:2,4], c(1.95,2.95), cis_party[2:3,4], lty=2)
brackets(1, cis_party[2,4]+20, 2, cis_party[2,4]+20, h = 45,  ticks = 0.5, lwd=2)
brackets(3, cis_party[3,4]+20, 2, cis_party[3,4]+20, h = 45,  ticks = 0.5, lwd=2)
text(1.5, cis_party[1,4]+160, bquote(hat(y)['R']-hat(y)['D'] ~ '='
     ~ .(cis_party[2,4]-cis_party[1,4])), cex=0.9)
text(2.5, cis_party[3,4]-40, bquote(hat(y)['I']-hat(y)['R'] ~ '='
     ~ .(cis_party[3,4]-cis_party[2,4])), cex=0.9)
axis(1, at=1:3, labels = c("Democrat", "Republican", "Independent"), tck=0.03, cex.axis=0.9,
     mgp=c(0.3, 0.3, 0), lty=1, lwd=0, lwd.ticks = 1)
axis(2, tck=0.03, cex.axis=0.9, mgp=c(0.3, 0.3, 0), lty=1, lwd=0, lwd.ticks = 1, las=2)
title(xlab = 'Party of candidate',
      ylab="Total campaign contributions",
      line = 1.7, cex.lab=1)
title(line = 1, main="Party of candidate", font.main=3)
plot(1:3, cis_incumchall[,4], type="n",xlab="",ylab="",  yaxt="n", xaxt="n",
     xlim=c(0.5,3.5), ylim=c(0, 3200))
segments(1:3, cis_incumchall[,5], 1:3, cis_incumchall[,6], lwd=2, col="gray60")
points(1:3, cis_incumchall[,4], pch=15:17, cex=1.5)
segments(c(1.05,2.05), cis_incumchall[1:2,4], c(1.95,2.95), cis_incumchall[2:3,4], lty=2)
brackets(1, cis_incumchall[2,4]+20, 2, cis_incumchall[2,4]+20, h = 105,  ticks = 0.5, lwd=2)
brackets(3, cis_incumchall[3,4]+20, 2, cis_incumchall[3,4]+20, h = 105,  ticks = 0.5, lwd=2)
text(1.5, cis_incumchall[2,4]+285, bquote(hat(y)['I']-hat(y)['C'] ~ '='
     ~ .(cis_incumchall[2,4]-cis_incumchall[1,4])), cex=0.9)
text(2.5, cis_incumchall[3,4]-200, bquote(hat(y)['O']-hat(y)['I'] ~ '='
     ~ .(cis_incumchall[3,4]-cis_incumchall[2,4])), cex=0.9)
axis(1, at=1:3, labels = c("Challenger", "Incumbent", "Open seat"), tck=0.03, cex.axis=0.9,
     mgp=c(0.3, 0.3, 0), lty=1, lwd=0, lwd.ticks = 1)
axis(2, tck=0.03, cex.axis=0.9, mgp=c(0.3, 0.3, 0), lty=1, lwd=0, lwd.ticks = 1, las=2)
title(xlab = 'Status of candidate',
      ylab="Total campaign contributions",
      line = 1.7, cex.lab=1)
title(line = 1, main="Status of candidate", font.main=3)
plot(newdat_hispf$HISPPCT, cis_hispf[,4], type="n",xlab="",ylab="",  yaxt="n", xaxt="n",
     ylim = c(0,1100))
polygon(x = c(newdat_hispf$HISPPCT, rev(newdat_hispf$HISPPCT)),
        y = c(cis_hispf[,5], rev(cis_hispf[,6])), col = mygray, border = NA)
lines(newdat_hispf$HISPPCT, cis_hispf[,4], lwd=2)
rug(campaign$HISPPCT)
segments(iqrange[,1], cis_hispiq[,4], iqrange[,1], rep(500,2), lty=2)
segments(iqrange[1,1], 500, iqrange[2,1], 500, lty = 2)
brackets(iqrange[1,1], 510, iqrange[2,1], 510, h = 75,  ticks = 0.5, lwd=2)
text(abs((iqrange[2,1]-iqrange[1,1])/2)+iqrange[1,1], 450, 'Interquartile range', cex=0.8)
text(iqrange[,1], cis_hispiq[,4]-50, round(iqrange[,1],3), cex=0.8)
text(abs((iqrange[2,1]-iqrange[1,1])/2)+iqrange[1,1], 655,
          labels=bquote(hat(y)['Q3']-hat(y)['Q1'] ~ '=' ~ .(iqrange[1,2])), cex=0.9)
axis(1, tck=0.03, cex.axis=0.9, mgp=c(0.3, 0.3, 0), lty=1, lwd=0, lwd.ticks = 1)
axis(2, tck=0.03, cex.axis=0.9, mgp=c(0.3, 0.3, 0), lty=1, lwd=0, lwd.ticks = 1, las=2)
title(xlab = '% Hispanic population in Congressional District',
      ylab="Total campaign contributions",
      line = 1.7, cex.lab=1)
title(line = 1, main="Hispanic constituency", font.main=3)
par(opar)

GLMpack documentation built on July 19, 2019, 5:05 p.m.

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