committee: Data on bills assigned to House committees in the 103rd and...

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

Description

Data for the committees example used in chapters 6

Usage

data(committee)

Format

A data frame with 20 rows and 6 variables:

SIZE

Number of members on the committee

SUBS

Number of subcommittees

STAFF

Number of staff assigned to the committee

PRESTIGE

Dummy variable indicating whether or not it is a high-prestige committee

BILLS103

Number of bills in the 103rd House

BILLS104

Number of bills in the 104th House

...

Examples

data(committee)
attach(committee)
library(AER)
library(MASS)
library(pscl)

## Table 6.6
committee

## Table 6.7
committee.out <- glm.nb(BILLS104 ~ SIZE + SUBS * (log(STAFF)) + PRESTIGE + BILLS103)
summary.glm(committee.out)
data.frame(cbind(round(cbind(summary(committee.out)$coef[,1:2], confint(committee.out)),4)[,1],
       round(cbind(summary(committee.out)$coef[,1:2], confint(committee.out)),4)[,2],
       round(cbind(summary(committee.out)$coef[,1:2], confint(committee.out)),4)[,3],
       round(cbind(summary(committee.out)$coef[,1:2], confint(committee.out)),4)[,4]))

## Figure 6.3
z.matrix <- matrix(0,200,200)
for(i in 1:200)  {
        for(j in 1:200)  {
                if(j < 70)    z.matrix[i,j] <- 1
                if(j < 40)    z.matrix[i,j] <- 2
                if(j < 10)    z.matrix[i,j] <- 3
                if(j == 1)    z.matrix[i,j] <- 3.001
                if(j > 130)   z.matrix[i,j] <- 1
                if(j > 160)   z.matrix[i,j] <- 2
                if(j > 190)   z.matrix[i,j] <- 3
                if(j == 200)  z.matrix[i,j] <- 3.001
        }
}
pears <- resid(committee.out,type="pearson")
devs <- resid(committee.out,type="deviance")
x = seq(-2000,2000,length=200)
opar = par(mfrow=c(1,1), mar=c(5.1,4.1,4.1,2.1), oma=c(0,0,0,0))
layout(matrix(c(1,2), ncol = 1), heights = c(0.9,0.1))
par(mar=c(3,4,2,4),oma=c(2,2,1,3))
image(seq(0,51,length=200), seq(-2000,2000,length=200),z.matrix,xlim=c(0,51),ylim=c(-2000,2000),
	xaxt="n",yaxt="n",xlab="",ylab="", col=rev(c("white", "gray40", "gray60", "gray80")))
points(seq(1,50,length=20),(2000/3)*pears[order(BILLS104)],pch=15)
lines(seq(1,50,length=20),(2000/3)*devs[order(BILLS104)],type="h")
abline(0,0, lwd=2)
abline(h=c((x[10]+x[9])/2,(x[40]+x[39])/2,(x[70]+x[69])/2,(x[130]+x[131])/2,
           (x[160]+x[161])/2,(x[191]+x[190])/2), lty=2)
title(xlab = "Order of Fitted Outcome Variable", ylab="Residual Effect",
      line = 1.3, cex.lab=1.3)
title(main="Model Fit Plot",
      line = 1, cex.main=1.7, font.main=1)
par(mar=c(0,1.5,1,1))
plot(0,0, type="n", axes = FALSE, xlab = "", ylab = "")
legend("center", ncol = 2,
       legend = c("Pearson", "Deviances"),
       cex=1, lty=c(0,1), pch = c(15,NA))
par(opar)

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