R語言與統(tǒng)計分析第四章答案

#第四章習(xí)題#4.1x-rbinom(1000,100,0.3)hist(x,main=c(1000個參數(shù)為0.3的伯努利分布隨機(jī)數(shù))#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個正態(tài)分布隨機(jī)數(shù))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個t分布混合樣本直方圖)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個t分布混合成一個樣本hist(x,xlim=c(min(x),max(x),probability=T, nclass=max(x)-min(x)+1,col=lightblue, main=c(3個t分布混合樣本直方圖)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$tint) #列聯(lián)表barplot(ts) #聯(lián)合柱狀圖windows() #新圖op-par()layout(matrix(c(2,1,0,3),2,2,byrow=T),c(1,6),c(4,1)par(mar=c(1,1,5,1)plot(tinting$age,tinting$it)lines(lowess(tinting$age,tinting$it),lwd=2) #擬合線rug(side=2,jitter(tinting$age,5) #細(xì)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$it,horizontal=T,axes=F)windows() #因子為tintcoplot(tinting$agetinting$it|tinting$tint)windows() #因子為tint與sexcoplot(tinting$agetinting$it|tinting$tint*tinting$sex)windows() #等高線圖library(MASS)z-kde2d(tinting$it,tinting$csoa)contour(z,col=red,drawlabels=FALSE)windows() #matplot圖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$spray) #列聯(lián)表barplot(cs)windows（）mys-c(1,2,3,4,5,6)InsectSprays$spray #分類圖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, #分類歸納 B=InsectSprays$count13:24, C=InsectSprays$count25:36, D=InsectSprays$count37:48, E=InsectSprays$count49:60, 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(標(biāo)準(zhǔn)差)sqrt(sd(db)print(極差)max(db)-min(db)print(四分位極值)mad(db)print(變異系數(shù))sd(db)/mean(db)install.packages(fBasics)library(fBasics)print(偏度)skewness(db)print(峰度)kurtosis(db)print(五數(shù)概括)fivenum(db)hist(db,xlim=c(min(db),max(db),probability=T, nclass=max(db)-min(db)+1,col=lightblue,main=直方圖)lines(density(db),col=red,lwd=3)windows()qqnorm(db,main=QQ圖)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=經(jīng)驗分布圖)curve(pnorm(x,m,s),col=red,lwd=2,add=T)print(莖葉圖)stem(db)windows()boxplot(db,main=框須圖)#4.8install.packages(RODBC) #從Excel讀入數(shù)據(jù)library(RODBC)z-odbcConnectExcel(C:/Users/Tang/Desktop/R/第四章數(shù)據(jù).xls)data-sqlFetch(z,Sheet1)close(z)plot(da