#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(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)#4.4install.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$age)]summary(age)sd(age)#4.5install.packages("DAAG")library(DAAG)data(tinting)ts<-table(tinting$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$it),lwd=2)#拟合线rug(side=2,jitter(tinting$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$it,horizontal=T,axes=F)windows()#因子为tintcoplot(tinting$age~tinting$it|tinting$tint)windows()#因子为tint与sexcoplot(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$age,y2=tinting$it,y3=tinting$csoa)matplot(d,type='l',main="matplot")#4.6data(InsectSprays)cs<-table(InsectSprays$count,InsectSprays$spray)#列联表barplot(cs)windows（）mys<-c(1,2,3,4,5,6)[InsectSprays$spray]#分类图plot(InsectSprays$count,col=mys,pch=mys)legend(x=40,y=26,legend=c("A","B","C","D","E","F"),col=c(1,2,3,4,5,6),pch=c(1,2,3,4,5,6))c.s<-data.frame(A=InsectSprays$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$count[61:72])summary(c.s)#4.7options(didits=4)db<-rnorm(100,75,9)print("均值")mean(db)print("方差")sd(db)print("标准差")sqrt(sd(db))print("极差")max(db)-min(db)print("四分位极值")mad(db)print("变异系数")sd(db)/mean(db)install.packages("fBasics")library(fBasics)print("偏度")skewness(db)print("峰度")kurtosis(db)print("五数概括")fivenum(db)hist(db,xlim=c(min(db),max(db)),probability=T,nclass=max(db)-min(db)+1,col='lightblue',main="直方图")lines(density(db),col='red',lwd=3)windows()qqnorm(db,main="QQ图")qqline(db,col='red')windows()x<-sort(db)n<-length(x)y<-(1:n)/nm<-mean(db)s<-sd(db)plot(x,y,type='s',main="经验分布图")curve(pnorm(x,m,s),col='red',lwd=2,add=T)print("茎叶图")stem(db)windows()boxplot(db,main="框须图")#4.8install.packages("RODBC")#从Excel读入数据library(RODBC)z<-odbcConnectExcel("C:/Users/Tang/Desktop/R/第四章数据.xls")data<-sqlFetch(z,"Sheet1")close(z)plot(data$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)
