R语言和统计分析第四章答案.docxVIP

#第四章习题 #4.1 x-rbinom(1000,100,0.3) hist(x,main=c(1000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(1000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(3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(3个t分布混合样本直方图)) lines(density(x,bw=1),col=blue,lwd=2) #4.4 install.packages(DAAG) 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$age)] summary(age) sd(age) #4.5 install.packages(DAAG) library(DAAG) data(tinting) ts-table(tinting$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=red,drawlabels=FALSE) windows() #matplot图 d-data.frame(y1=tinting$age,y2=