R语言代码试题答案步骤.docx

R语言代码试题答案步骤.docx

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R语言代码试题答案步骤

Rversion3.4.3（2017-11-30）--"Kite-EatingTree"

Copyright（C）2017TheRFoundationforStatisticalComputing

Platform:

x86_64-w64-mingw32/x64（64-bit）

R是自由软件，不带任何担保。

在某些条件下你可以将其自由散布。

用'license（）'或'licence（）'来看散布的详细条件。

R是个合作计划，有许多人为之做出了贡献.

用'contributors（）'来看合作者的详细情况

用'citation（）'会告诉你如何在出版物中正确地引用R或R程序包。

用'demo（）'来看一些示程序，用'help（）'来阅读在线帮助文件，或

用'help.start（）'通过HTML浏览器来看帮助文件。

用'q（）'退出R.

[原来保存的工作空间已还原]

>h=read.csv（"F:

//1.csv",header=true）

Errorinread.table（file=file,header=header,sep=sep,quote=quote,:

找不到对象'true'

>h=read.csv（"F:

//1.csv",header=TRUE）

>h

地区x1x2x3x4x5x6x7x8x9y

1753526391971165836968474287475106.51.324046

2734418811854155622546151493173107.53.620024

3421115421502104712043865836584104.13.712531

438561529143990615064423633628108.83.312212

5546327301584135419724655763886109.63.717717

6580920421433131018444185856649107.73.616594

7463520451594144816433840743415111.03.714614

8468718071337118112173640635711104.84.212984

9965621111790101737247867385373106.03.126253

10665819161437105830785063968347112.63.118825

11755221101552122829975019763374104.53.021545

12581515411397114319334460128792105.33.715012

1373171634175477321054452552763104.63.618593

1450721477117467114873851228800106.73.012776

15520121971572100516564190451768106.93.315778

16460718861191108515253733831499106.83.113733

17583817831371103016523984638572105.63.814496

1854421625130291817383897133480105.74.214609

19825815212100104829545027854095107.92.522396

20广西55531146137788416263638627952107.53.414244

216556865152199313203948532377107.02.014457

22687022291177110214714449838914107.83.316573

2360741651128477315874233929608105.94.015050

2449931399101465513964115619710105.53.312586

255468176097493914343762922195108.94.013884

26551813628454675505170522936109.52.611184

27555117891322121220794307338564109.43.215333

28460216311288105013883767921978108.62.712847

2946671512123290610974648333181110.63.412346

30476918761193106315164743636394105.54.214067

31523920311167102812814457633796114.83.413892

>lm=lm（y~x1+x2+x3+x4+x5+x6+x7+x8+x9,data=h）

>lm

Call:

lm（formula=y~x1+x2+x3+x4+x5+x6+x7+x8+x9,

data=h）

Coefficients:

（Intercept）x1x2x3x4x5x6x7x8x9

320.6409481.3165881.6498592.178660-0.0056091.6842830.0103200.003655-19.13057650.515575

>summary（lm）

Call:

lm（formula=y~x1+x2+x3+x4+x5+x6+x7+x8+x9,

data=h）

Residuals:

Min1QMedian3QMax

-940.13-195.243.42239.00476.06

Coefficients:

EstimateStd.ErrortvaluePr（>|t|）

（Intercept）3.206e+023.952e+030.0810.936097

x11.317e+001.062e-0112.4003.97e-11***

x21.650e+003.008e-015.4841.93e-05***

x32.179e+005.199e-014.1900.000412***

x4-5.609e-034.766e-01-0.0120.990720

x51.684e+002.142e-017.8641.08e-07***

x61.032e-021.343e-020.7690.450665

x73.655e-031.070e-020.3420.736006

x8-1.913e+013.197e+01-0.5980.555983

x95.052e+011.502e+020.3360.739986

---

Signif.codes:

0‘***’0.001‘**’0.01‘*’0.05‘.’0.1‘’1

Residualstandarderror:

389.4on21degreesoffreedom

MultipleR-squared:

0.9923,AdjustedR-squared:

0.9889

F-statistic:

298.9on9and21DF,p-value:

<2.2e-16

>pre=fitted.values（lm）

>res=residuals（lm）

>sd（res）

[1]325.7967

>res=residuals（lm）

>dy=step（lm）

Start:

AIC=377.73

y~x1+x2+x3+x4+x5+x6+x7+x8+x9

DfSumofSqRSSAIC

-x41213184326375.73

-x91171493201454375.90

-x71177003202005375.90

-x81542953238599376.26

-x61895863273891376.59

3184305377.73

-x3126625935846898394.57

-x2145610567745361403.29

-x51937750012561805418.28

-x112331454726498852441.42

Step:

AIC=375.73

y~x1+x2+x3+x5+x6+x7+x8+x9

DfSumofSqRSSAIC

-x91174283201754373.90

-x71185633202889373.91

-x81544373238763374.26

-x61918133276139374.61

3184326375.73

-x3129361306120456393.99

-x2154679418652267404.72

-x51939334512577671416.32

-x112588608629070412442.29

Step:

AIC=373.9

y~x1+x2+x3+x5+x6+x7+x8

DfSumofSqRSSAIC

-x71346343236387372.24

-x61748003276554372.62

-x81821503283904372.69

3201754373.90

-x3130553536257107392.67

-x2157258368927590403.69

-x51938262412584378414.33

-x112586883229070586440.29

Step:

AIC=372.24

y~x1+x2+x3+x5+x6+x8

DfSumofSqRSSAIC

-x81708133307201370.91

-x611527773389165371.67

3236387372.24

-x3155012848737672401.02

-x21889504912131436411.20

-x51945809812694485412.60

-x112773309830969486440.25

Step:

AIC=370.91

y~x1+x2+x3+x5+x6

DfSumofSqRSSAIC

-x611375403444741370.17

3307201370.91

-x3157710639078264400.21

-x21887119312178394409.32

-x51947352112780722410.81

-x112824816231555363438.83

Step:

AIC=370.17

y~x1+x2+x3+x5

DfSumofSqRSSAIC

3444741370.17

-x3157178839162624398.50

-x211024981513694556410.95

-x511099831314443054412.60

-x113325863736703378441.52

>summary（dy）

Call:

lm（formula=y~x1+x2+x3+x5,data=h）

Residuals:

Min1QMedian3QMax

-943.18-161.0512.74250.93566.25

Coefficients:

EstimateStd.ErrortvaluePr（>|t|）

（Intercept）-1694.6269562.9773-3.0100.00574**

x11.36420.086115.8447.11e-15***

x21.76790.20108.7962.86e-09***

x32.28940.34856.5695.76e-07***

x51.74240.19129.1111.42e-09***

---

Signif.codes:

0‘***’0.001‘**’0.01‘*’0.05‘.’0.1‘’1

Residualstandarderror:

364on26degreesoffreedom

MultipleR-squared:

0.9916,AdjustedR-squared:

0.9903

F-statistic:

769.2on4and26DF,p-value:

<2.2e-16

>newdata=data.frame（x1=5200,x2=2000,x3=1100,x4=1000,x5=1300,x6=45000,x7=34000,x8=115.0,x9=3.8）

>predict（dy,newdata,interval="confidence"）

fitlwrupr

113718.6713468.9813968.36

>

>h=ts（read.csv（"F:

//3.csv",header=TRUE））

>h

TimeSeries:

Start=1

End=56

Frequency=1

X78

[1,]-58

[2,]53

[3,]-63

[4,]13

[5,]-6

[6,]-16

[7,]-14

[8,]3

[9,]-74

[10,]89

[11,]-48

[12,]-14

[13,]32

[14,]56

[15,]-86

[16,]-66

[17,]50

[18,]26

[19,]59

[20,]-47

[21,]-83

[22,]2

[23,]-1

[24,]124

[25,]-106

[26,]113

[27,]-76

[28,]-47

[29,]-32

[30,]39

[31,]-30

[32,]6

[33,]-73

[34,]18

[35,]2

[36,]-24

[37,]23

[38,]-38

[39,]91

[40,]-56

[41,]-58

[42,]1

[43,]14

[44,]-4

[45,]77

[46,]-127

[47,]97

[48,]10

[49,]-28

[50,]-17

[51,]23

[52,]-2

[53,]48

[54,]-131

[55,]65

[56,]-17

>plot（h,type="o"）

>local（{pkg<-select.list（sort（.packages（all.available=TRUE））,graphics=TRUE）

+if（nchar（pkg））library（pkg,character.only=TRUE）}）

Warningmessage:

程辑包‘urca’是用R版本3.4.4来建造的

>adf=ur.df（as.vector（h）,type=c（"drift"）,selectlags=c（"AIC"））

>summary（adf）

###############################################

#AugmentedDickey-FullerTestUnitRootTest#

###############################################

Testregressiondrift

Call:

lm（formula=z.diff~z.lag.1+1+z.diff.lag）

Residuals:

Min1QMedian3QMax

-96.191-23.390-0.58118.446133.241

Coefficients:

EstimateStd.ErrortvaluePr（>|t|）

（Intercept）-9.43817.0489-1.3390.187

z.lag.1-1.78370.2386-7.4769.65e-10***

z.diff.lag0.19560.13791.4180.162

---

Signif.codes:

0‘***’0.001‘**’0.01‘*’0.05‘.’0.1‘’1

Residualstandarderror:

50.89on51degreesoffreedom

MultipleR-squared:

0.7589,AdjustedR-squared:

0.7494

F-statistic:

80.25on2and51DF,p-value:

<2.2e-16

Valueoftest-statisticis:

-7.476127.9471

Criticalvaluesforteststatistics:

1pct5pct10pct

tau2-3.51-2.89-2.58

phi16.704.713.86

>acf（h）

>pacf（h）

>ar=sarima（h,1,0,4,details=F）

>ar

$fit

Call:

stats:

:

arima（x=xdata,order=c（p,d,q）,seasonal=list（order=c（P,D,

Q）,period=S）,xreg=xmean,include.mean=FALSE,optim.control=list（trace=trc,

REPORT=1,reltol=tol））

Coefficients:

ar1ma1ma2ma3ma4xmean

-0.0957-0.7605-0.051-0.25910.0706-5.0886

s.e.0.73180.72440.6370.20130.19390.4252

sigma^2estimatedas1850:

loglikelihood=-291.97,aic=597.95

$degrees_of_freedom

[1]50

$ttable

EstimateSEt.valuep.value

ar1-0.09570.7318-0.13080.8965

ma1-0.76050.7244-1.04980.2988

ma2-0.05100.6370-0.08000.9365

ma3-0.25910.2013-1.28750.2038

ma40.07060.19390.36410.7173

xmean-5.08860.4252-11.96680.0000

$AIC

[1]8.73734

$AICc

[1]8.814721

$BIC

[1]7.954342

>ma=sarima（h,0,1,1,details=F）

>ma

$fit

Call:

stats:

:

arima（x=xdata,order=c（p,d,q）,seasonal=list（order=c（P,D,

Q）,period=S）,xreg=constant,optim.control=list（trace=trc,REPORT=1,

reltol=tol））

Coefficients:

ma1constant

-1.00000.1275

s.e.0.04520.4833

sigma^2estimatedas3412:

loglikelihood=-303.77,aic=613.53

$degrees_of_freedom

[1]53

$ttable

EstimateSEt.valuep.value

ma1-1.00000.0452-22.13900.000

constant0.12750.48330.26380.793

$AIC

[1]9.206399

$AICc

[1]9.250355

$BIC

[1]8.278733

>arma=sarima（h,1,1,1,details=F）

>arma

$fit

Call:

stats:

:

arima（x=xdata,order=c（p,d,q）,seasonal=list（order=c（P,D,

Q）,period=S）,xreg=constant,optim.control=list（trace=trc,REPORT=1,

reltol=tol））

Coefficients:

ar1ma1constant

-0.4893-1.00000.1052

s.e.0.11610.04690.2858

sigma^2estimatedas2548:

loglikelihood=-296.27,aic=600.53

$degrees_of_freedom

[1]52

$ttable

EstimateSEt.valuep.value