R语言与统计分析第四章答案

#第四章习题 #4.1

x<-rbinom(1000,100,0.3)

hist(x,main=c(\个参数为0.3的伯努利分布随机数\ #4.2

x<-rnorm(1000,10,4)

hist(x,probability=T,xlim=c(min(x),max(x)),nclass=max(x)-min(x)+1, col='lightblue',main=c(\个正态分布随机数\lines(density(x,bw=1),col='blue',lwd=3) #4.3

x<-sample(c(rt(10,1),rt(10,2),rt(10,10)),1000,replace=T) hist(x,xlim=c(min(x),max(x)),probability=T, nclass=max(x)-min(x)+1,col='lightblue', main=c(\个t分布混合样本直方图\lines(density(x,bw=1),col='blue',lwd=2) #方法二

k<-matrix(,3,100) k[1,]=rt(100,1) k[2,]=rt(100,2) k[3,]=rt(100,10)

x=c(k[1,],k[2,],k[3,]) #3个t分布混合成一个样本 hist(x,xlim=c(min(x),max(x)),probability=T, nclass=max(x)-min(x)+1,col='lightblue', main=c(\个t分布混合样本直方图\lines(density(x,bw=1),col='blue',lwd=2) #4.4

install.packages(\library(DAAG) data(possum) par(mfrow=c(2,2))

hist(possum$age,breaks=1+(0:8)*1) hist(possum$age,breaks=0+(0:9)*1) hist(possum$age,breaks=1+(0:5)*2) hist(possum$age,breaks=0+(0:5)*2) summary(possum$age)

age<-possum$age[!is.na(possum$age)] summary(age) sd(age) #4.5

install.packages(\

library(DAAG) data(tinting)

ts<-table(tinting$sex,tinting$tint) #列联表

barplot(ts) #联合柱状图 windows() #新图 op<-par()

layout(matrix(c(2,1,0,3),2,2,byrow=T),c(1,6),c(4,1)) par(mar=c(1,1,5,1))

plot(tinting$age,tinting$it)

lines(lowess(tinting$age,tinting$it),lwd=2) #拟合线 rug(side=2,jitter(tinting$age,5)) #细小刻度 rug(side=1,jitter(tinting$it,5)) par(mar=c(1,2,5,1))

boxplot(tinting$age,axes=F) par(mar=c(5,1,1,2))

boxplot(tinting$it,horizontal=T,axes=F)

windows() #因子为tint coplot(tinting$age~tinting$it|tinting$tint)

windows() #因子为tint与sex coplot(tinting$age~tinting$it|tinting$tint*tinting$sex) windows() #等高线图 library(MASS)

z<-kde2d(tinting$it,tinting$csoa)

contour(z,col=\

windows() #matplot图 d<-data.frame(y1=tinting$age,y2=tinting$it,y3=tinting$csoa) matplot(d,type='l',main=\ #4.6

data(InsectSprays)

cs<-table(InsectSprays$count,InsectSprays$spray) #列联表 barplot(cs)

windows（）

mys<-c(1,2,3,4,5,6)[InsectSprays$spray] #分类图 plot(InsectSprays$count,col=mys,pch=mys)

legend(x=40,y=26,legend=c(\c.s<-data.frame(A=InsectSprays$count[1:12], #分类归纳 B=InsectSprays$count[13:24], C=InsectSprays$count[25:36], D=InsectSprays$count[37:48], E=InsectSprays$count[49:60], F=InsectSprays$count[61:72]) summary(c.s)

#4.7

options(didits=4) db<-rnorm(100,75,9) print(\均值\mean(db) print(\方差\sd(db)

print(\标准差\sqrt(sd(db)) print(\极差\max(db)-min(db) print(\四分位极值\mad(db)

print(\变异系数\sd(db)/mean(db)

install.packages(\library(fBasics)

print(\偏度\skewness(db) print(\峰度\kurtosis(db)

print(\五数概括\fivenum(db)

hist(db,xlim=c(min(db),max(db)),probability=T,

nclass=max(db)-min(db)+1,col='lightblue',main=\直方图\lines(density(db),col='red',lwd=3) windows()

qqnorm(db,main=\图\qqline(db,col='red') windows() x<-sort(db) n<-length(x) y<-(1:n)/n m<-mean(db) s<-sd(db)

plot(x,y,type='s',main=\经验分布图\

curve(pnorm(x,m,s),col='red',lwd=2,add=T) print(\茎叶图\stem(db) windows()

boxplot(db,main=\框须图\ #4.8

install.packages(\ #从Excel读入数据

library(RODBC)

z<-odbcConnectExcel(\第四章数据.xls\data<-sqlFetch(z,\close(z)

plot(data$体重~data$身高,main=\体重对身高散点图\windows()

coplot(data$体重~data$身高|data$性别) windows()

coplot(data$体重~data$身高|data$年龄) windows()

coplot(data$体重~data$身高|data$性别*data$年龄)