# suicide: Data on suicides in 2009 in OECD member states In GLMpack: Data and Code to Accompany Generalized Linear Models, 2nd Edition

## Description

Data for the suicide example used in chapter 7

## Usage

 `1` ```data(suicide) ```

## Format

A data frame with 32 rows and 7 variables:

COUNTRYCODE

Country code

COUNTRYNAME

Name of the country

YEAR

Year

DEATHS

Number of suicides in the country per 100,000 individuals

GDP

GDP in thousands of dollars

SUBABUSE

Share of the population with alcohol or drug use disorder

TEMP

Average temperature

...

## 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``` ```opar = par(mfrow=c(1,1), mar=c(5.1,4.1,4.1,2.1), oma=c(0,0,0,0)) data(suicide) attach(suicide) ## Table 7.2 # Poisson model suic.out.p <- glm(DEATHS ~ GDP + TEMP + SUBABUSE, family = poisson) summary(suic.out.p) round(confint(suic.out.p),3) coefs_poisson <- summary(suic.out.p)\$coefficients[1:4,] coefs_poisson suic.out.qp <- glm(DEATHS ~ GDP + TEMP + SUBABUSE, family = quasipoisson) summary(suic.out.qp) round(confint(suic.out.qp),3) coefs_quasipoisson <- summary(suic.out.qp)\$coefficients[1:4,] coefs_quasipoisson ## Figure 7.1 layout(matrix(c(1,2,3,4), ncol=2, byrow = TRUE)) par(mar=c(4,3,2,0),oma=c(1,1,1,1)) # Histogram #1 hist(TEMP,xlab="",ylab="", yaxt="n", xaxt="n", main="", col="gray50", border = "gray30", ylim=c(0,15)) axis(1, tck=0, mgp=c(0, 0, 0), lty=1, lwd=0, lwd.ticks = 0) 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 = 'Mean temperature (in Celsius)', ylab="", line = 1.7, cex.lab=1.2) title(line = 1, main="Temperature", font.main=3) # Histogram #2 hist(GDP,xlab="",ylab="", yaxt="n", xaxt="n", main="", col="gray50", border = "gray30", ylim=c(0,15)) axis(1, tck=0, mgp=c(0, 0, 0), lty=1, lwd=0, lwd.ticks = 0) 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 = 'GDP per capita (in thousands of dollars)', ylab="", line = 1.7, cex.lab=1.2) title(line = 1, main="Economic Conditions", font.main=3) # Histogram #3 hist(SUBABUSE,xlab="",ylab="", yaxt="n", xaxt="n", main="", col="gray50", border = "gray30", ylim=c(0,15)) axis(1, tck=0, mgp=c(0, 0, 0), lty=1, lwd=0, lwd.ticks = 0) 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 = '% of population with alcohol or drug use disorders', ylab="",line = 1.7, cex.lab=1.2) title(line = 1, main="Substance abuse", font.main=3) # Histogram #4 hist(DEATHS,xlab="",ylab="", yaxt="n", xaxt="n", main="", col="gray10", border = "gray20", ylim=c(0,15)) axis(1, tck=0, mgp=c(0, 0, 0), lty=1, lwd=0, lwd.ticks = 0) 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 = 'Number of suicides per 100,000 people', ylab="", line = 1.7, cex.lab=1.2) title(line = 1, main="Suicide rate", font.main=3) par(opar) ## Figure 7.2 newdat1 <- data.frame(GDP=seq(13, 70.5, 1), TEMP=rep(mean(TEMP), 58), SUBABUSE=rep(mean(SUBABUSE), 58)) newdat2 <- data.frame(GDP=rep(mean(GDP), 61), TEMP=rep(mean(TEMP), 61), SUBABUSE=seq(0,6,0.1)) preds.qp.gdp <- predict(suic.out.qp, newdata = newdat1, type = "link", se.fit = TRUE) preds.qp.subabuse <- predict(suic.out.qp, newdata = newdat2, type = "link", se.fit = TRUE) ilink.qp <- family(suic.out.qp)\$linkinv cis.p.preds.qp.gdp <- cbind(ilink.qp(preds.qp.gdp\$fit - (2 * preds.qp.gdp\$se.fit)), ilink.qp(preds.qp.gdp\$fit + (2 * preds.qp.gdp\$se.fit))) cis.p.preds.qp.subabuse <- cbind(ilink.qp(preds.qp.subabuse\$fit - (2 * preds.qp.subabuse\$se.fit)), ilink.qp(preds.qp.subabuse\$fit + (2 * preds.qp.subabuse\$se.fit))) mygray = rgb(153, 153, 153, alpha = 200, maxColorValue = 255) par(mar=c(4,3,1,0),oma=c(1,1,1,1), mfrow=c(1,2)) plot(newdat1\$GDP, ilink.qp(preds.qp.gdp\$fit), type="n",xlab="",ylab="", yaxt="n", xaxt="n", lwd=2, ylim = c(0,37)) polygon(c(newdat1\$GDP,rev(newdat1\$GDP)), c(cis.p.preds.qp.gdp[,1],rev(cis.p.preds.qp.gdp[,2])), border = NA, col = mygray) lines(newdat1\$GDP, ilink.qp(preds.qp.gdp\$fit)) points(GDP, DEATHS, pch="+", col="gray20", cex=0.8) axis(1, tck=0.03, cex.axis=0.9, at=seq(20,70,10), labels = seq(20,70,10), 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 = 'GDP per capita (in thousands of dollars)', ylab="Number of suicides per 100,000 people", line = 1.7, cex.lab=1.2) plot(newdat2\$SUBABUSE, ilink.qp(preds.qp.subabuse\$fit), type="n",xlab="",ylab="", yaxt="n", xaxt="n", lwd=2, ylim = c(0,37)) polygon(c(newdat2\$SUBABUSE,rev(newdat2\$SUBABUSE)), c(cis.p.preds.qp.subabuse[,1], rev(cis.p.preds.qp.subabuse[,2])), border = NA, col = mygray) lines(newdat2\$SUBABUSE, ilink.qp(preds.qp.subabuse\$fit)) points(SUBABUSE, DEATHS, pch="+", col="gray20", cex=0.8) 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 = '% of population with alcohol or drug use disorders', ylab="", line = 1.7, cex.lab=1.2) par(opar) ```

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