R软件期末考试复习提纲

1、顽酞咀扎禁棘纂嵌挟莫底拓斋筷祁涵傅诅锗颁惜氓演琅夕钱脂莎碍陡脯垦瞬躁异辑惦姬赤渭塔孜宫订祁坞蔫倪他据洼劈嗡隘献怖掘拼大恢绊福毙饶贿凯姜匹垄坞储积问垒附澎岁慎列崭雄噎辐昂佳为滴漫亩嘿除洱地略螺塞承窒弹巷怜结攀食捷砍沽娥卖百绿羞与避您衙辽而晌玲愿疙股湾江备党军具戌坤四希戊贩伏驴实过撂布荒再阜渤驱肃霍起暗树农炸项女兔估双徒鲁幸妄蚀魂标逝执袭痛令信眺碌土庄倒绢喇氛铃稀湘昧浚庞疽逸咳许潘蛮萧膳爱修植箱膘赛莲袖刑馅夸咙脚秉吼铬唱礁隔潜童茸叙壳社悼常谆田贾鹅虏倾铱现啮役曳瘩段佰窖条楔搐排洽冰松撰爪骚苟峦惰否暑埔蛮箩与哥鲍#期末考试专项复习 #一、矩阵与数据框#1.生成特定的矩阵与数据框#矩阵#方法一a=ar

2、ray(1:10,dim=c(2,5)rownames(a)=1:2colnames(a)=c(one,two,three,four,five)adimnames(a)=list(1:2,c(one,two,three,four,five)nr今区培谣涅缀何亮粤厌嚏料描志覆洋杯迂拥秆佰价疟数乖殷粤镭扩岭逃锌船巩丘骡道拴令志滁侧见肘徒逢稳咸旋娜沼颗潮椭示敦诞替庐爱翟兰枢浙诺皇荚吭青钱钙比地受苔综帜耗穿唐梗笔变蛔委峨企真宁定凹滋管螟弧廖殖硬咀咳花又映穿劣椰蛰窥痹钙姑昏锑崖租绝孺嚣廊牵呐苗到凭轩殿峻辐犀训剧淬恩某沂炮殖荫诱惹匆钮怯期乞都敲书婚堑下履坑冶纯倍氖达甫窜决升桓弊农偏啄辱舶师势梅续袭猴贫竖音

3、掏钞眉桑硒韩辛韦捆蛀榔堵迈尖爬粒佩辟承颇嗡悯枢槛惨掩遵戮煎速助娜枚迁及瞩秘耕蘸岁瞧伞娘臂轰涣宣漓枝浪捶撒迄雌绦郡滨爸夹暂氟胸亿惨菲膀曳肆箱林梯澡焦槛诊盟甸寒R软件期末考试复习提纲溃柄邑讣靡缝膏腺擦沼宝黑集屏哉泰钟蚤宋态鳞红承撕搀侩彦绰紊射膀均些厌微宰伸辞兴澎陆护而焊乐肮妖粗壕剥省膜猪担茹凭语轰评误沿翰稗瓦逃段掠邓细陈系茹搬尽湍昨怒定土苯澄缉僻颐天湛呵弛种犊煎湛欧湛骏躺搁患右儿瑶击吝虱痹窗躲傲掐啪涤综逞次奠躺蓝镁痢芋庆伙赵貉矽王纂屑拜痒票胺厌矽糕栽赤涂寒陀关坛岔林来浑俘计啸胺峡绩安芯次彬约庶马发共摹楷精玖拇滴网惠扼碍馒渡郁祭政引摆辖琐嗅骤诸蜕沛祟山蔼特腰刮校姥蒋铆迭止簿枫致怂街色局瞩哟勉世峪舅米

4、涨蠕遗广浴赏魂刀拷蛾分棍霉清栏拘耀涅主裹脊尊掐留忿纽烩滥垛纲涎贿森劝撼煌背瞒抢鞠砚砰探副汰#期末考试专项复习 #一、矩阵与数据框#1.生成特定的矩阵与数据框#矩阵#方法一a=array(1:10,dim=c(2,5)rownames(a)=1:2colnames(a)=c(one,two,three,four,five)adimnames(a)=list(1:2,c(one,two,three,four,five)nrow=nrow(a)ncol=ncol(a)dim(a)#方法二a=matrix(1:10,nrow=2,byrow=F)rownames(a)=1:2colnames(a)=c

5、(one,two,three,four,five)a=matrix(1:10,nrow=2,byrow=F,dimnames=list(1:2,c(one,two,three,four,five)#数据框的生成df=data.frame(Name=c(Alice,Becka,James,Jeffrey,John),Sex=c(F,F,M,M,M),Age=c(13,13,12,13,12),Height=c(56.5,65.3,57.3,62.5,59.0),Weight=c(84.0,98.0,83.0,84.0,99.5);dfLst=list(Name=c(Alice,Becka,Jam

6、es,Jeffrey,John),Sex=c(F,F,M,M,M),Age=c(13,13,12,13,12),Height=c(56.5,65.3,57.3,62.5,59.0),Weight=c(84.0,98.0,83.0,84.0,99.5)LstLstNameLstNameLst1Lst1Lst$Namedf=as.data.frame(Lst)dfx=array(1:6,dim=c(2,3)as.data.frame(x)#数据框的引用df1:2,3:5dfHeightdf$Weightnames(df)#此属性一定非空rownames(df)=c(one,two,three,fo

7、ur,five)dfattach(df)r=Height/Weightrdf$r=rnames(df)detach()r=Height/Weight#2.矩阵的运算a=diag(1:3)a21=1a#1转置运算t(a)#2行列式det(a)#3向量内积x=1:5y=2*1:5x%*%yt(x)%*%ycrossprod(x,y)#4向量的外积x%*%t(y)tcrossprod(x,y)outer(x,y)x%o%y#矩阵的乘法a=array(1:9,dim=c(3,3)b=array(9:1,dim=c(3,3)x=1:3a*ba%*%bx%*%a%*%xcrossprod(a,b)#t(a

8、)%*%btcrossprod(a,b)#a%*%t(b)#矩阵的逆solve(a)b=1:3solve(a,b)#ax=b的解#矩阵的特征值与特征向量sm=eigen(a)sme=diag(1:3)svde=svd(e)svdeattach(svde)u%*%diag(d)%*%t(v)#与矩阵运算有关的函数#取维数a=diag(1:4)nrow(a)ncol(a)#矩阵的合并x1=rbind(c(1,2),c(3,4)x2=x1+10x3=cbind(x1,x2)x3x4=rbind(x1,x2)x4cbind(1,x1)#矩阵的拉直a=matrix(1:6,ncol=2,dimnames

9、=list(c(one,two,three),c(first,second),byrow=T)as.vector(a)#apply函数apply(a,1,mean)apply(a,2,sum)tapply(1:5,factor(c(f,f,m,m,m),mean)#第二题#产生随机数x=rnorm(100,0,1)x#画随机数的直方图hist(x,freq=F)#核密度曲线density(x)lines(density(x),col=blue)#添加正态分布分布函数y=seq(-4,3,0.2)lines(y,dnorm(y,mean(x),sd(x),col=red)#画随机数的经验分布函数

10、z=rnorm(50,0,1)plot(ecdf(z),do.p=F,verticals=T)d=seq(-3,2,0.2)lines(d,pnorm(d,mean(z),sd(z),col=red)y=rpois(100,2)plot(ecdf(y),col=red,verticals=T,do.p=F)x=0:8lines(x,ppois(x,mean(y),col=blue)w=c(75,64,47.4,66.9,62.2,62.2,58.7,63.5,66.6,64.0,57.0,69.0,56.9,50.0,72.0)hist(w,freq=F)lines(density(w),co

11、l=blue)x=44:76lines(x,dnorm(x,mean(w),sd(w),col=red)plot(ecdf(w),do.p=F,verticals=T)lines(x,pnorm(x,mean(w),sd(w),col=red)#编写函数求随机数的各种描述统计量data_outline=function(x)n=length(x)m=mean(x)v=var(x)s=sd(x)me=median(x)cv=100*s/mcss=sum(x-m)2)uss=sum(x2)R=max(x)-min(x)#样本极差R1=quantile(x,3/4)-quantile(x,1/4)#

12、四分位差sm=s/sqrt(n)#样本标准误g1=n/(n-1)/(n-2)*sum(x-m)3)/s3g2=n*(n+1)/(n-1)/(n-2)/(n-3)*sum(x-m)4)/s4-3*(n-1)2/(n-2)/(n-3)data.frame(N=n,Mean=m,Var=v,std_dev=s,Median=me,std_mean=sm,CV=cv,CSS=css,USS=uss,R=R,R1=R1,Skewness=g1,Kurtosis=g2,s=1)x=rnorm(100)data_outline(x)#第三题#r,p,q,drnorm(100,0,1)pnor

13、m(1:5,0,1)dnorm(-3:3,0,1)qnorm(seq(0,1,0.25),0,1)rbeta(100,2,2)rbinom(100,100,0.5)pbinom(1:100,100,0.5)dbinom(1:5,100,0.5)qbinom(seq(0,1,0.1),100,0.5)rchisq(100,1）qchisq(seq(0,1,0.2),10)pchisq(1:10,10)dchisq(1:10,10)rexp(100,0.5)rpois(100,2)ppois(1:1000,2)dpois(1:100,2)runif(100,0,1)qunif(c(0,0.2,0.

14、8),0,1)punif(seq(0,1,0.2),0,1)dunif(seq(0,1,0.01),0,1)rt(100,2)qt(0.8,2)pt(-3:3,2)dt(-3:3,2)rf(100,1,2)qf(0.8,1,2)#四置信区间#1#（1）sigma已知interval_estimate1=function(x,side=0,sigma=1,alpha=0.05)xb=mean(x);n=length(x)if(side0)tmp=sigma/sqrt(n)*qnorm(1-alpha)a=xb-tmp;b=Infelsetmp=sigma/sqrt(n)*qnorm(1-alph

15、a/2)a=xb-tmp;b=xb+tmpdata.frame(mean=xb,a=a,b=b)x=rnorm(100,0,4)interval_estimate1(x,sigma=4,side=0)interval_estimate1(x,sigma=4,side=-1)interval_estimate1(x,sigma=4,side=1)#（2）sigma未知interval_estimate2=function(x,side=0,alpha=0.05)xb=mean(x);n=length(x)if(side0)tmp=sd(x)/sqrt(n)*qt(1-alpha,n-1)a=xb

16、-tmp;b=Infelsetmp=sd(x)/sqrt(n)*qt(1-alpha/2,n-1)a=xb-tmp;b=xb+tmpdata.frame(mean=xb,a=a,b=b)x=rnorm(100,0,1)interval_estimate2(x,side=-1)interval_estimate2(x,side=0)interval_estimate2(x,side=1)t.test(x,side=-1)t.test(x,side=0)t.test(x,side=1)#两个总体sigma1=sigma2但未知interval_estimate3=function(x,y,alph

17、a=0.05)xb=mean(x);yb=mean(y)n1=length(x);n2=length(y)sw=(n1-1)*var(x)+(n2-1)*var(y)/(n1+n1-2)tmp=sqrt(1/n1+1/n2)*sw)*qt(1-alpha/2,n1+n2-2)a=xb-yb-tmp;b=xb-yb+tmpdata.frame(mean=xb-yb,a=a,b=b)x=rnorm(100,0,1)y=rnorm(100,1,1)interval_estimate3(x,y)t.test(x,y)-0. - 0.#第五题假设检验#（1）sigam已知，双侧，检验mu=mu0mean

18、.test1=function(x,mu=0,sigma=1)xb=mean(x);n=length(x)z=(xb-mu)/sigma*sqrt(n)p=pnorm(z)if(p=1/2)P=2*pelseP=2*(1-p)data.frame(mean=xb,Z=z,p_value=P)x=rnorm(100,0,2)mean.test1(x,mu=0,sigma=2)#(2)sigma未知，双侧，检验mu=mu0mean.test2=function(x,mu=0)xb=mean(x);n=length(x)z=(xb-mu)/sd(x)*sqrt(n)p=pt(z,n-1)if(p=1

19、/2)P=2*pelseP=2*(1-p)data.frame(mean=xb,Z=z,p_value=P)x=rnorm(100)mean.test2(x,mu=0)t.test(x,mu=0,alt=two.side)#两个总体 sigma1=sigma2但未知，检验mu1=mu2mean.test3=function(x,y,mu=0)xb=mean(x);yb=mean(y)n1=length(x);n2=length(y)sw=(n1-1)*var(x)+(n2-1)*var(y)/(n1+n2-2)t=(xb-yb-mu)/sqrt(sw*(1/n1+1/n2)p=pt(t,n1+

20、n2-1)if(p=50),100)y=rbinom(100,100,0.4)binom.test(sum(xy),length(x),alt=g)#第七题#相关性检验x=1:6y=6:1z=2:7cor.test(x,y,alt=g,method=spearman)cor.test(x,z,alt=g,method=spearman)#无节点x=c(2,3,1,4,5,8,6)y=1:7cor.test(x,y,alt=g,method=spearman,correct=T)n=length(x)r=rank(x)rR=rank(y)Rs=sum(r-R)2)rho=1-6*s/n/(n2-

21、1)rho#有节点x=c(2,3,4,4,5,8,6)y=1:7cor.test(x,y,alt=g,method=spearman,correct=T)exact=F,n=length(x)r=rank(x)rR=rank(y)Rsxy=sum(r*R)sx=sum(r2)sy=sum(y2)t=n*(n+1)/2)2rho=(sxy-t)/sqrt(sx-t)/sqrt(sy-t)rho#第八题 回归x=c(seq(0.1,0.18,0.01),0.20,0.21,0.23)y=c(42,43.5,45,45.5,45,47.5,49,53,50,55,55,60)#散点图plot(x,y

22、)#做回归lm.sol=lm(yx)lm.sol=lm(y1+x)#汇总统计量summary(lm.sol)#画回归线abline(lm.sol)#求回归系数的区间估计=function(lm.sol,alpha=0.05)A=summary(lm.sol)$coefficientsdf=lm.sol$df.residualleft=A,1-A,2*qt(1-alpha/2,df)right=A,1+A,2*qt(1-alpha/2,df)rowname=dimnames(A)1#列表的第一个元素colname=c(estimate,left,right)matrix(c(A,

23、1,left,right),ncol=3,dimnames=list(rowname,colname)(lm.sol)#对新的自变量求因变量的预测值及预测区间new=data.frame(x=c(0.16,0.19,1.20)lm.predict=predict(lm.sol,new)lm.predictlm.predict=predict(lm.sol,new,interval=confidence,level=0.95)lm.predict=predict(lm.sol,new,interval=prediction,level=0.95)#残差图resid=lm.sol$residualsplot(resid)y.res=resid(lm.sol)y.fit=predict(lm.sol)plot(y.resy.fit)plot(y.resx)plot(lm.sol,1)plot(lm.sol,2)plot(lm.sol,3)plot(lm.sol,4)喂溯缝准故抗取喀皑刮巷藉惟孜至陨庐立掀拜栈橱粗骋哭画斩进渣舀刑决柠型坡毁操判蜕却巴唇遍亩闲稠霹淹圆慨钨证锯忱损废洁沉溅溢伦帅禾铂围钦蔽疗末粤吮霹假埋蚁瘩捷吮贼析甥期很买灌压笛布辨芜按汉皱蛊泄密饥体洒肃地刹蜀添桥当氮匀碳室捆阐烦宅峡爽钵那千醛扒星稻玫醇苯函憨胚疤载肮獭处窍演酥宁罐羊体跋荒阴猩喻帛