dp: Data on capital punishment
In GLMpack: Data and Code to Accompany Generalized Linear Models, 2nd Edition
Description
Usage
Format
Examples
Description
Data for the capital punishment example used in chapters 4, 5, and 6
Usage
1
Format
A data frame with 17 rows and 7 variables:
- EXECUTIONS
The number of times that capital punishment is implemented on a state level in the United States for the year 1997
- INCOME
Median per capita income in dollars
- PERPOVERTY
Percent of the population classified as living in poverty
- PERBLACK
Percent of Black citizens in the population
- VC100k96
Rate of violent crimes per 100,000 residents for the year before (1996)
- SOUTH
Dummy variable to indicate whether the state is in the South
- PROPDEGREE
Proportion of the population with a college degree
...
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opar = par(mfrow=c(1,1), mar=c(5.1,4.1,4.1,2.1), oma=c(0,0,0,0))
data(dp)
attach(dp)
## Table 4.2
dp
## Table 5.1
dp.out <- glm(EXECUTIONS ~ INCOME+PERPOVERTY+PERBLACK+log(VC100k96)+
SOUTH+PROPDEGREE, family=poisson)
dp.cis <- round(glm.summary(dp.out, alpha = 0.05),4)
round(cbind(summary(dp.out)$coef[,1:2], dp.cis),4)
round(dp.out$null.deviance,4);round(dp.out$df.null,4)
round(dp.out$deviance,4);round(dp.out$df.residual,4)
round(logLik(dp.out),4)
round(dp.out$aic,4)
round(vcov(dp.out),4) # variance covariance matrix
## Table 5.2
k <- 200
b5 <- seq(0.1, 5.4,length=k)
w <- rep(0,k)
for(i in 1:k){
mm <- glm(EXECUTIONS ~ INCOME+PERPOVERTY+PERBLACK+log(VC100k96)+PROPDEGREE,
offset=b5[i]*SOUTH,family=poisson)
w[i] <- logLik(mm)
}
f <- function(b5,x,y,maxloglik){
mm <- glm(EXECUTIONS ~ INCOME+PERPOVERTY+PERBLACK+log(VC100k96)+PROPDEGREE,
offset=b5*x,family=poisson)
logLik(mm) - maxloglik + qchisq(.95,1)/2
}
low.pll <- uniroot(f,interval=c(1.5,2), x=SOUTH, y=EXECUTIONS, maxloglik=logLik(dp.out))$root
high.pll <- uniroot(f,interval=c(3,4), x=SOUTH, y=EXECUTIONS, maxloglik=logLik(dp.out))$root
w[which.min(abs(w-low.pll))]
round(c(low.pll, high.pll),4)
cbind(round(dp.cis[,3:4],4),
round(confint(dp.out),4))
## Table 6.2
resp <- resid(dp.out,type="response")
pears <- resid(dp.out,type="pearson")
working <- resid(dp.out,type="working")
devs <- resid(dp.out,type="deviance")
dp.mat <- cbind(rep(1,nrow(dp)), as.matrix(dp[,2:4]), as.matrix(log(dp[,5])),
as.matrix(dp[,6]), as.matrix(dp[,7]))
dp.resid.mat <- cbind(resp,pears,working,devs)
dimnames(dp.resid.mat)[[2]] <- c("response","pearson","working","deviance")
dimnames(dp.resid.mat)[[1]] <- rownames(dp)
dp.resid.mat2 <- round(dp.resid.mat,4)
resid.df <- data.frame(cbind(dp.resid.mat2[,1], dp.resid.mat2[,2],
dp.resid.mat2[,3], dp.resid.mat2[,4]))
colnames(resid.df) <- dimnames(dp.resid.mat)[[2]]
resid.df
## Figure 5.1
dp.mat.0 <- cbind(dp.mat[,1:5],rep(0,length=nrow(dp.mat)),dp.mat[,7])
dimnames(dp.mat.0)[[2]] <- names(dp.out$coefficients)
dp.mat.1 <- cbind(dp.mat[,1:5],rep(1,length=nrow(dp.mat)),dp.mat[,7])
dimnames(dp.mat.1)[[2]] <- names(dp.out$coefficients)
tcks = list(seq(0,140,20), seq(0,12,2), seq(0,30,5), seq(0,10,2), seq(0,30,5))
layout(matrix(c(1,1,2,2,3,3,4,4,5,6,6,7,8,8,8,8), ncol=4, byrow = TRUE),
heights = c(0.3,0.3,0.3,0.1))
par(mar=c(3,3,2,4),oma=c(2,1,1,3))
for (i in 2:(ncol(dp.mat.0)-1)) {
j = i-1
if (i==6){
i <- i+1
plot(0,0, type = "n", axes=FALSE, xlab = "", ylab="")
}
ruler <- seq(min(dp.mat.0[,i]),max(dp.mat.0[,i]),length=1000)
xbeta0 <- exp(dp.out$coefficients[-i]%*%apply(dp.mat.0[,-i],2,mean)
+ dp.out$coefficients[i]*ruler)
xbeta1 <- exp(dp.out$coefficients[-i]%*%apply(dp.mat.1[,-i],2,mean)
+ dp.out$coefficients[i]*ruler)
plot(ruler,xbeta0,type="l", xlab="",ylab="", yaxt="n", xaxt="n",
ylim=c(min(xbeta0,xbeta1)-2,max(xbeta0,xbeta1)), lwd=3)
lines(ruler,xbeta1,lty=4, lwd=2)
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, at=tcks[[j]],
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 = paste("Levels of",dimnames(dp.mat.0)[[2]][i]), ylab="Expected executions",
line = 1.7, cex.lab=1.2)
}
plot(0,0, type = "n", axes=FALSE, xlab = "", ylab="")
par(mar=c(0,1.5,1,1))
plot(0,0, type="n", axes = FALSE, xlab = "", ylab = "")
legend("center", ncol = 2,
legend = c("South State", "Non-South State"),
cex=1.1, lty=c(2,1), bty="n", lwd=c(2,3))
par(opar)
## Figure 5.2
par(mar=c(3,3,1,0),oma=c(1,1,1,1))
plot(b5,w,type="l",lwd=3, xaxt="n", yaxt="n", xlab="", ylab="")
abline(h=logLik(dp.out)-qchisq(.95,1)/2,lty=3, col="gray40")
segments(dp.cis[6,3], -45, dp.cis[6,4], -45, lty=6, col="black", lwd=2)
segments(dp.cis[6,3:4], c(-45,-45), dp.cis[6,3:4], c(-55,-55), lty=3, col="gray40")
text(3.5, y=-45, "Wald Test", cex=0.9)
segments(low.pll, -42.5, high.pll, -42.5, lty=2, lwd=2, col="black")
segments(c(low.pll, high.pll), c(-55,-55), c(low.pll, high.pll),
rep(logLik(dp.out)-qchisq(.95,1)/2,2), lty=3, col="gray40")
text(3.25, y=-42.5, "Profile log-likelihood", cex=0.9, pos=4)
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 = 'Coefficient of SOUTH', ylab="Profile log-likelihood",
line = 1.7, cex.lab=1.2)
par(opar)
## Figure 6.1
coef.vector <- NULL
for (i in 1:length(EXECUTIONS)) {
dp.temp <- glm(EXECUTIONS[-i] ~ INCOME[-i]+PERPOVERTY[-i]+PERBLACK[-i]+log(VC100k96)[-i]+
SOUTH[-i]+PROPDEGREE[-i], family=poisson)
coef.vector <- rbind(coef.vector,dp.temp$coefficients)
}
layout(matrix(c(1,2,3,4,5,6), ncol=2, byrow = TRUE), heights = c(0.33,0.33,0.33))
par(mar=c(3,4.5,2,4),oma=c(2,1,1,3))
for(i in 2:ncol(coef.vector)) {
x=plot(coef.vector[,i],type="b",xlab="",ylab="", yaxt="n", xaxt="n", lwd=2,
ylim=c(min(coef.vector[,i])-abs(min(coef.vector[,i]))*0.25,
max(coef.vector[,i])+abs(max(coef.vector[,i]))*0.25))
abline(h=dp.out$coefficients[i])
axis(1, at =seq(2,16,2), tck=0.03, cex.axis=0.9, mgp=c(0.3, 0.3, 0), lty=1, lwd=0, lwd.ticks = 1)
if(i==2){
axis(2, at = seq(5,35,5)/100000, labels = as.expression(paste(seq(5,35,5), "e(-5)", sep="")),
tck=0.03, cex.axis=0.9, mgp=c(0.3, 0.3, 0), lty=1, lwd=0, lwd.ticks = 1, las=2)
}
else{
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 = "Index number",
line = 1.7, cex.lab=1.2)
title(ylab=dimnames(dp.mat.0)[[2]][i],
line = 3.25, cex.lab=1.2)
}
par(opar)
GLMpack documentation built on July 19, 2019, 5:05 p.m.
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Description Usage Format Examples
Description
Data for the capital punishment example used in chapters 4, 5, and 6
Usage
1 |
Format
A data frame with 17 rows and 7 variables:
- EXECUTIONS
The number of times that capital punishment is implemented on a state level in the United States for the year 1997
- INCOME
Median per capita income in dollars
- PERPOVERTY
Percent of the population classified as living in poverty
- PERBLACK
Percent of Black citizens in the population
- VC100k96
Rate of violent crimes per 100,000 residents for the year before (1996)
- SOUTH
Dummy variable to indicate whether the state is in the South
- PROPDEGREE
Proportion of the population with a college degree
...
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 | opar = par(mfrow=c(1,1), mar=c(5.1,4.1,4.1,2.1), oma=c(0,0,0,0))
data(dp)
attach(dp)
## Table 4.2
dp
## Table 5.1
dp.out <- glm(EXECUTIONS ~ INCOME+PERPOVERTY+PERBLACK+log(VC100k96)+
SOUTH+PROPDEGREE, family=poisson)
dp.cis <- round(glm.summary(dp.out, alpha = 0.05),4)
round(cbind(summary(dp.out)$coef[,1:2], dp.cis),4)
round(dp.out$null.deviance,4);round(dp.out$df.null,4)
round(dp.out$deviance,4);round(dp.out$df.residual,4)
round(logLik(dp.out),4)
round(dp.out$aic,4)
round(vcov(dp.out),4) # variance covariance matrix
## Table 5.2
k <- 200
b5 <- seq(0.1, 5.4,length=k)
w <- rep(0,k)
for(i in 1:k){
mm <- glm(EXECUTIONS ~ INCOME+PERPOVERTY+PERBLACK+log(VC100k96)+PROPDEGREE,
offset=b5[i]*SOUTH,family=poisson)
w[i] <- logLik(mm)
}
f <- function(b5,x,y,maxloglik){
mm <- glm(EXECUTIONS ~ INCOME+PERPOVERTY+PERBLACK+log(VC100k96)+PROPDEGREE,
offset=b5*x,family=poisson)
logLik(mm) - maxloglik + qchisq(.95,1)/2
}
low.pll <- uniroot(f,interval=c(1.5,2), x=SOUTH, y=EXECUTIONS, maxloglik=logLik(dp.out))$root
high.pll <- uniroot(f,interval=c(3,4), x=SOUTH, y=EXECUTIONS, maxloglik=logLik(dp.out))$root
w[which.min(abs(w-low.pll))]
round(c(low.pll, high.pll),4)
cbind(round(dp.cis[,3:4],4),
round(confint(dp.out),4))
## Table 6.2
resp <- resid(dp.out,type="response")
pears <- resid(dp.out,type="pearson")
working <- resid(dp.out,type="working")
devs <- resid(dp.out,type="deviance")
dp.mat <- cbind(rep(1,nrow(dp)), as.matrix(dp[,2:4]), as.matrix(log(dp[,5])),
as.matrix(dp[,6]), as.matrix(dp[,7]))
dp.resid.mat <- cbind(resp,pears,working,devs)
dimnames(dp.resid.mat)[[2]] <- c("response","pearson","working","deviance")
dimnames(dp.resid.mat)[[1]] <- rownames(dp)
dp.resid.mat2 <- round(dp.resid.mat,4)
resid.df <- data.frame(cbind(dp.resid.mat2[,1], dp.resid.mat2[,2],
dp.resid.mat2[,3], dp.resid.mat2[,4]))
colnames(resid.df) <- dimnames(dp.resid.mat)[[2]]
resid.df
## Figure 5.1
dp.mat.0 <- cbind(dp.mat[,1:5],rep(0,length=nrow(dp.mat)),dp.mat[,7])
dimnames(dp.mat.0)[[2]] <- names(dp.out$coefficients)
dp.mat.1 <- cbind(dp.mat[,1:5],rep(1,length=nrow(dp.mat)),dp.mat[,7])
dimnames(dp.mat.1)[[2]] <- names(dp.out$coefficients)
tcks = list(seq(0,140,20), seq(0,12,2), seq(0,30,5), seq(0,10,2), seq(0,30,5))
layout(matrix(c(1,1,2,2,3,3,4,4,5,6,6,7,8,8,8,8), ncol=4, byrow = TRUE),
heights = c(0.3,0.3,0.3,0.1))
par(mar=c(3,3,2,4),oma=c(2,1,1,3))
for (i in 2:(ncol(dp.mat.0)-1)) {
j = i-1
if (i==6){
i <- i+1
plot(0,0, type = "n", axes=FALSE, xlab = "", ylab="")
}
ruler <- seq(min(dp.mat.0[,i]),max(dp.mat.0[,i]),length=1000)
xbeta0 <- exp(dp.out$coefficients[-i]%*%apply(dp.mat.0[,-i],2,mean)
+ dp.out$coefficients[i]*ruler)
xbeta1 <- exp(dp.out$coefficients[-i]%*%apply(dp.mat.1[,-i],2,mean)
+ dp.out$coefficients[i]*ruler)
plot(ruler,xbeta0,type="l", xlab="",ylab="", yaxt="n", xaxt="n",
ylim=c(min(xbeta0,xbeta1)-2,max(xbeta0,xbeta1)), lwd=3)
lines(ruler,xbeta1,lty=4, lwd=2)
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, at=tcks[[j]],
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 = paste("Levels of",dimnames(dp.mat.0)[[2]][i]), ylab="Expected executions",
line = 1.7, cex.lab=1.2)
}
plot(0,0, type = "n", axes=FALSE, xlab = "", ylab="")
par(mar=c(0,1.5,1,1))
plot(0,0, type="n", axes = FALSE, xlab = "", ylab = "")
legend("center", ncol = 2,
legend = c("South State", "Non-South State"),
cex=1.1, lty=c(2,1), bty="n", lwd=c(2,3))
par(opar)
## Figure 5.2
par(mar=c(3,3,1,0),oma=c(1,1,1,1))
plot(b5,w,type="l",lwd=3, xaxt="n", yaxt="n", xlab="", ylab="")
abline(h=logLik(dp.out)-qchisq(.95,1)/2,lty=3, col="gray40")
segments(dp.cis[6,3], -45, dp.cis[6,4], -45, lty=6, col="black", lwd=2)
segments(dp.cis[6,3:4], c(-45,-45), dp.cis[6,3:4], c(-55,-55), lty=3, col="gray40")
text(3.5, y=-45, "Wald Test", cex=0.9)
segments(low.pll, -42.5, high.pll, -42.5, lty=2, lwd=2, col="black")
segments(c(low.pll, high.pll), c(-55,-55), c(low.pll, high.pll),
rep(logLik(dp.out)-qchisq(.95,1)/2,2), lty=3, col="gray40")
text(3.25, y=-42.5, "Profile log-likelihood", cex=0.9, pos=4)
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 = 'Coefficient of SOUTH', ylab="Profile log-likelihood",
line = 1.7, cex.lab=1.2)
par(opar)
## Figure 6.1
coef.vector <- NULL
for (i in 1:length(EXECUTIONS)) {
dp.temp <- glm(EXECUTIONS[-i] ~ INCOME[-i]+PERPOVERTY[-i]+PERBLACK[-i]+log(VC100k96)[-i]+
SOUTH[-i]+PROPDEGREE[-i], family=poisson)
coef.vector <- rbind(coef.vector,dp.temp$coefficients)
}
layout(matrix(c(1,2,3,4,5,6), ncol=2, byrow = TRUE), heights = c(0.33,0.33,0.33))
par(mar=c(3,4.5,2,4),oma=c(2,1,1,3))
for(i in 2:ncol(coef.vector)) {
x=plot(coef.vector[,i],type="b",xlab="",ylab="", yaxt="n", xaxt="n", lwd=2,
ylim=c(min(coef.vector[,i])-abs(min(coef.vector[,i]))*0.25,
max(coef.vector[,i])+abs(max(coef.vector[,i]))*0.25))
abline(h=dp.out$coefficients[i])
axis(1, at =seq(2,16,2), tck=0.03, cex.axis=0.9, mgp=c(0.3, 0.3, 0), lty=1, lwd=0, lwd.ticks = 1)
if(i==2){
axis(2, at = seq(5,35,5)/100000, labels = as.expression(paste(seq(5,35,5), "e(-5)", sep="")),
tck=0.03, cex.axis=0.9, mgp=c(0.3, 0.3, 0), lty=1, lwd=0, lwd.ticks = 1, las=2)
}
else{
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 = "Index number",
line = 1.7, cex.lab=1.2)
title(ylab=dimnames(dp.mat.0)[[2]][i],
line = 3.25, cex.lab=1.2)
}
par(opar)
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