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.docx

R语言代码试题答案步骤

Rversion(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()'来看一些示范程序,用'help()'来阅读在线帮助文件,或

用'()'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=("",header=true)

.

Errorin(file=file,header=header,sep=sep,quote=quote,:

找不到对象'true'

>h=("",header=TRUE)

>h

地区x1x2x3x4x5x6x7x8x9y

1北京75352639197116583696847428747524046

2天津73441881185415562254615149317320024

3河北42111542150210471204386583658412531

4山西3856152914399061506442363362812212

5内蒙古54632730158413541972465576388617717

|

6辽宁58092042143313101844418585664916594

7吉林46352045159414481643384074341514614

8黑龙江46871807133711811217364063571112984

9上海96562111179010173724786738537326253

10江苏66581916143710583078506396834718825

11浙江75522110155212282997501976337421545

12安徽58151541139711431933446012879215012

13福建7317163417547732105445255276318593

14江西5072147711746711487385122880012776

15山东52012197157210051656419045176815778

-

16河南46071886119110851525373383149913733

17湖北58381783137110301652398463857214496

18湖南5442162513029181738389713348014609

19广东82581521210010482954502785409522396

20广西5553114613778841626363862795214244

21海南655686515219931320394853237714457

22重庆68702229117711021471444983891416573

23四川6074165112847731587423392960815050

24贵州4993139910146551396411561971012586

25云南546817609749391434376292219513884

26西藏55181362845467550517052293611184

27陕西55511789132212122079430733856415333

28甘肃46021631128810501388376792197812847

29青海4667151212329061097464833318112346

30宁夏47691876119310631516474363639414067

31新疆52392031116710281281445763379613892

>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

>summary(lm)

Call:

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

data=h)

Residuals:

Min1QMedian3QMax

Coefficients:

EstimateStd.ErrortvaluePr(>|t|)

(Intercept)+02+03

@

x1+00***

x2+00***

x3+00***

x4

x5+00***

x6

x7

x8+01+01

x9+01+02

---

Signif.codes:

0‘***’‘**’‘*’‘.’‘’1

Residualstandarderror:

on21degreesoffreedom

MultipleR-squared:

AdjustedR-squared:

F-statistic:

on9and21DF,p-value:

<

>pre=(lm)

>res=residuals(lm)

>sd(res)

[1]

>res=residuals(lm)

>dy=step(lm)

Start:

AIC=

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

DfSumofSqRSSAIC

-x41213184326

-x91171493201454

-x71177003202005

-x81542953238599

-x61895863273891

3184305

-x3126625935846898

-x2145610567745361

-x519377500

-x11

Step:

AIC=

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

{

DfSumofSqRSSAIC

-x91174283201754

-x71185633202889

-x81544373238763

-x61918133276139

3184326

-x3129361306120456

-x2154679418652267

-x519393345

|

-x11

Step:

AIC=

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

DfSumofSqRSSAIC

-x71346343236387

-x61748003276554

-x81821503283904

3201754

$

-x3130553536257107

-x2157258368927590

-x519382624

-x11

Step:

AIC=

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

DfSumofSqRSSAIC

-x81708133307201

-x611527773389165

3236387

-x3155012848737672

-x218895049

-x519458098

-x11

Step:

AIC=

y~x1+x2+x3+x5+x6

DfSumofSqRSSAIC

-x611375403444741

3307201

-x3157710639078264

-x218871193

-x519473521

-x11

Step:

AIC=

y~x1+x2+x3+x5

}

DfSumofSqRSSAIC

3444741

-x3157178839162624

-x21

-x51

-x11

>summary(dy)

Call:

:

lm(formula=y~x1+x2+x3+x5,data=h)

Residuals:

Min1QMedian3QMax

Coefficients:

EstimateStd.ErrortvaluePr(>|t|)

(Intercept)**

x1***

x2***

x3***

x5***

---

Signif.codes:

0‘***’‘**’‘*’‘.’‘’1

Residualstandarderror:

364on26degreesoffreedom

MultipleR-squared:

AdjustedR-squared:

F-statistic:

on4and26DF,p-value:

<

>newdata=(x1=5200,x2=2000,x3=1100,x4=1000,x5=1300,x6=45000,x7=34000,x8=,x9=

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

fitlwrupr

1

>

 

\

 

`

>h=ts("",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<-(sort(.packages=TRUE)),graphics=TRUE)

+if(nchar(pkg))library(pkg,=TRUE)})

Warningmessage:

程辑包‘urca’是用R版本来建造的

>adf=(h),type=c("drift"),selectlags=c("AIC"))

>summary(adf)

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

#AugmentedDickey-FullerTestUnitRootTest#

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

Testregressiondrift

Call:

lm(formula=~+1+Min1QMedian3QMax

>

Coefficients:

EstimateStd.ErrortvaluePr(>|t|)

(Intercept)

***

---

Signif.codes:

0‘***’‘**’‘*’‘.’‘’1

Residualstandarderror:

on51degreesoffreedom

MultipleR-squared:

AdjustedR-squared:

[

F-statistic:

on2and51DF,p-value:

<

Valueoftest-statisticis:

Criticalvaluesforteststatistics:

1pct5pct10pct

tau2

phi1

>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,=FALSE,=list(trace=trc,

REPORT=1,reltol=tol))

Coefficients:

ar1ma1ma2ma3ma4xmean

.

sigma^2estimatedas1850:

loglikelihood=,aic=

$degrees_of_freedom

[1]50

$ttable

EstimateSE

ar1

ma1

ma2

ma3

'

ma4

xmean

$AIC

[1]

$AICc

[1]

$BIC

@

[1]

>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,=list(trace=trc,REPORT=1,

reltol=tol))

>

Coefficients:

ma1constant

.

sigma^2estimatedas3412:

loglikelihood=,aic=

$degrees_of_freedom

[1]53

$ttable

EstimateSE

ma1

constant

$AIC

[1]

$AICc

>

[1]

$BIC

[1]

>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,=list(trace=trc,REPORT=1,

reltol=tol))

Coefficients:

ar1ma1constant

.

sigma^2estimatedas2548:

loglikelihood=,aic=

$degrees_of_freedom

[1]52

$ttable

EstimateSE

ar1

ma1

constant

$AIC

|

[1]

$AICc

[1]

$BIC

[1]

>res=residuals(ar$fit)

>(res)

Box-Piercetest

data:

res

X-squared=,df=1,p-value=

>plot(res*res)

>res<-residuals(ma$fit)

>res

TimeSeries:

Start=1

End=56

Frequency=1

[1]+01+01+01+00+00+00+01+01+02+01+00+01+01+01+01

[17]+01+01+01+01+01+00+00+02+02+02+01+01+01+01+01+01

[33]+01+01+00+01+01+01+01+01+01+00+01+01+02+02+01

[49]+01+01+01+01+02+01+01

>(res)#

Box-Piercetest

data:

res

X-squared=,df=1,p-value=

>yc=(h,10,1,1,1)

 

>yc$pred

TimeSeries:

Start=57

End=66

Frequency=1

[1]

 

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