R语言与统计分析第四章答案

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1、 #第四章习题#4.1x-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)#4.2x-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) #4.3x-sample(c(rt(10,1),rt(10,2),rt(10,10),1000,replace=T)hist(x,xlim=c(

2、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)k1,=rt(100,1)k2,=rt(100,2)k3,=rt(100,10)x=c(k1,k2,k3,) #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、3个t分布混合样本直方图)lines(density(x,bw=1),col=blue,lwd=2)#4.4install.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)

4、summary(age)sd(age)#4.5install.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(si

5、de=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tintcoplot(tinting$agetinting$it|tinting$tint)windows() #因子为tint与sexcoplot(tinting$agetinting$it|tinting$tint*tinting$sex

6、)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$age,y2=tinting$it,y3=tinting$csoa) matplot(d,type=l,main=matplot)#4.6data(InsectSprays)cs-table(InsectSprays$count,InsectSprays$spray) #列联表barplot(cs)windows（）my

7、s-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(A,B,C,D,E,F),col=c(1,2,3,4,5,6),pch=c(1,2,3,4,5,6)c.s-data.frame(A=InsectSprays$count1:12, #分类归纳 B=InsectSprays$count13:24, C=InsectSprays$count25:36, D=InsectSprays$count37:48, E=InsectSprays$co

8、unt49:60, F=InsectSprays$count61:72)summary(c.s)#4.7options(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(fBasics)library(fBasics)print(偏度)skewness(db)print(峰度)kurtosis(db)print(五数

9、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=QQ图)qqline(db,col=red)windows()x-sort(db)n-length(x)y-(1:n)/nm-mean(db)s-sd(db)plot(x,y,type=s,main=经验分布图)curve(pnorm(x,m,s),col=red,lwd

10、=2,add=T)print(茎叶图)stem(db)windows()boxplot(db,main=框须图)#4.8install.packages(RODBC) #从Excel读入数据library(RODBC)z-odbcConnectExcel(C:/Users/Tang/Desktop/R/第四章数据.xls)data-sqlFetch(z,Sheet1)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)