R语言与统计分析第四章答案

1、#第四章习题#4.1x-rbinom(1000,100,0.3)hist(xzmain=c(1000个参数为0.3的伯努利分布随机数”)#4.2x-rnorm(1000,10z4) hist(x/probability=Xxlim=c(min(x)/max(x),nclass=max(x)-min(x)+lzcohlightbluemaincflOOO 个正态分布随机数)lines(density(x/bw=l)/col=blue,lwd=3)#43xsamplefcfrtflOALrtflOJjtf 10,10),1000,replace=T) hist(x/xlim=c(min(x)/ma

2、x(x),probability=l；n class=max(x)-min(x)+l/col=,lightblue,/ main=c(3个t分布混合样本直方图) lines(density(x/bw=l)/col=blue,lwd=2)#方法二k-matrix(,3,100)klJ=rt(100zl)k2j=rt(100z2) k3j=rt(100,10) x=c(kl,k2,k3,) #3个t分布混合成一个样本 hist(x/xlim=c(min(x)/max(x),probability=l；n class=max(x)-min(x)+l/col=,lightblue,/ main=c(3

3、个t分布混合样本直方图) lines(density(x/bw=l),col=,blue,lwd=2)#4.4install.packages(nDAAGH) library(DAAG) data(possum)par(mfrow=c(2,2)hist(possum$age,breaks=l+(0:8)*l)hist(possum$age,breaks=0+(0:9)*l)hist(possum$age,breaks=l+(0:5)*2) hist(possum$age,breaks=0+(0:5)*2) summary(possum$age)age-possum$age!is.na(poss

4、um$age) summary(age)sd(age)#4.5install.packages(nDAAGH)library(DAAG)data(tinti ng)ts-table(tinting$sex,tinting$tint) # 列联表 barplot(ts)#联合柱状图windows()# 新图op-par()layout(matrix(c(2/lz0,3)/2/2/byrow=T)/c(l/6)/c(4,l) par(mar=c(l/l,5/l) plot(tinting$age,tinting$it)lines(lowess(tinting$age,tinting$it)Jwd=

5、2) #拟合线rug(side=2,jitter(tinting$age,5)#细小刻度rug(side=l/jitter(tinting$it/5) par(mar=c(l/2,5/l)boxplot(tinting$age,axes=F)par(mar=c(5/l,l/2)boxplot(ti ntin g$it,horizontal=Taxes=F) windows()coplot(ti nting$agetinting$it | tintin g$ti nt) windows()#因子为tint#因子为tint与sexcoplot(ti nting$agetinti ng$it| ti

6、ntin g$ti nt*tinti ng$sex)windows()#等高线图library(MASS)z-kde2d(tinti ng$it,ti nt in g$csoa) contour(z/col=red,drawlabels=FALSE)windows()#matplot 图d-data.frame(yl=tinting$age/y2=ti ntin g$it,y3=ti ntin g$csoa) matplot(d,type=T,mai n=matplot11) #4.6data(l nsectSprays)cs-table(l nsectSprays$countnsectSpr

7、ays$spray) #歹 ij 联表barplot(cs)windows ()mysc( 123,4,5,6 川 nsectSprays$spray# 分类图plot(lnsectSprays$count/col=mys,pch=mys) legend(x=40/y=26Jegend=c(,A,I；,B,；,C,I；,D,；,E,；,F,)/col=c(l/2/3A5,6),pch=c(l/2,3A5/6) c.s-data.frame(A=l nsectSprays$countl:12，#分类归纳B=lnsectSprays$count13:24,C=ln sectSprays$co un

8、 t25:36,D=lnsectSprays$count37:48,E=lnsectSprays$count49:60,F=lnsectSprays$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)printf变异系数”)sd(db)/mean(db)in stall.packages(,fBasicsH)libr

9、ary(fBasics)print(“偏度”)skew ness(db)print(“峰度”)kurtosis(db)print(”五数概括“)fivenum(db)hist(db,xlim=c(mi n(db),max(db)，probability=rn class=max(db)min(db)+l，col=lightblue：main=直方图”)lines(density(db)/col=,red,Jwd=3)windows()qqnorm(dbzmain=,QQ 图”)qqline(db,col=,red,)windows()x-sort(db)n-length(x)y-(l：n)/n

10、m-mean(db)s-sd(db)plot(x,y,type=s,main=经验分布图)curve(p norm(x，rTbs),colrecfwd=2,add=T)print(”茎叶图“)stem(db)windows()boxplot(db,mai n=框须图”) #4.8install.packages(nRODBCn) #从 Excel 读入数据 library(RODBC) z-odbcConnectExcel(,C:/Users/Tang/Desktop/R/第四章数据xls) data-sqlFetch(z/,SheetlH)close(z)plot(data$体重data$身高,mai