R语言代码试题答案步骤.docx

1、R语言代码试题答案步骤R version 3.4.3 (2017-11-30) - Kite-Eating TreeCopyright (C) 2017 The R Foundation for Statistical ComputingPlatform: 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

2、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)Error in read.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 x1 x2 x3 x4 x5 x6 x7 x8 x9 y1 7535 2639 1971 1658 3696 84742

3、 87475 106.5 1.3 240462 7344 1881 1854 1556 2254 61514 93173 107.5 3.6 200243 4211 1542 1502 1047 1204 38658 36584 104.1 3.7 125314 3856 1529 1439 906 1506 44236 33628 108.8 3.3 122125 5463 2730 1584 1354 1972 46557 63886 109.6 3.7 177176 5809 2042 1433 1310 1844 41858 56649 107.7 3.6 165947 4635 20

4、45 1594 1448 1643 38407 43415 111.0 3.7 146148 4687 1807 1337 1181 1217 36406 35711 104.8 4.2 129849 9656 2111 1790 1017 3724 78673 85373 106.0 3.1 2625310 6658 1916 1437 1058 3078 50639 68347 112.6 3.1 1882511 7552 2110 1552 1228 2997 50197 63374 104.5 3.0 2154512 5815 1541 1397 1143 1933 44601 287

5、92 105.3 3.7 1501213 7317 1634 1754 773 2105 44525 52763 104.6 3.6 1859314 5072 1477 1174 671 1487 38512 28800 106.7 3.0 1277615 5201 2197 1572 1005 1656 41904 51768 106.9 3.3 1577816 4607 1886 1191 1085 1525 37338 31499 106.8 3.1 1373317 5838 1783 1371 1030 1652 39846 38572 105.6 3.8 1449618 5442 1

6、625 1302 918 1738 38971 33480 105.7 4.2 1460919 8258 1521 2100 1048 2954 50278 54095 107.9 2.5 2239620 广西 5553 1146 1377 884 1626 36386 27952 107.5 3.4 1424421 6556 865 1521 993 1320 39485 32377 107.0 2.0 1445722 6870 2229 1177 1102 1471 44498 38914 107.8 3.3 1657323 6074 1651 1284 773 1587 42339 29

7、608 105.9 4.0 1505024 4993 1399 1014 655 1396 41156 19710 105.5 3.3 1258625 5468 1760 974 939 1434 37629 22195 108.9 4.0 1388426 5518 1362 845 467 550 51705 22936 109.5 2.6 1118427 5551 1789 1322 1212 2079 43073 38564 109.4 3.2 1533328 4602 1631 1288 1050 1388 37679 21978 108.6 2.7 1284729 4667 1512

8、 1232 906 1097 46483 33181 110.6 3.4 1234630 4769 1876 1193 1063 1516 47436 36394 105.5 4.2 1406731 5239 2031 1167 1028 1281 44576 33796 114.8 3.4 13892 lm=lm(yx1+x2+x3+x4+x5+x6+x7+x8+x9,data=h) lmCall:lm(formula = y x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9, data = h)Coefficients:(Intercept) x1 x2

9、 x3 x4 x5 x6 x7 x8 x9 320.640948 1.316588 1.649859 2.178660 -0.005609 1.684283 0.010320 0.003655 -19.130576 50.515575 summary(lm)Call:lm(formula = y x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9, data = h)Residuals: Min 1Q Median 3Q Max -940.13 -195.24 3.42 239.00 476.06 Coefficients: Estimate Std. Err

10、or t value Pr(|t|) (Intercept) 3.206e+02 3.952e+03 0.081 0.936097 x1 1.317e+00 1.062e-01 12.400 3.97e-11 *x2 1.650e+00 3.008e-01 5.484 1.93e-05 *x3 2.179e+00 5.199e-01 4.190 0.000412 *x4 -5.609e-03 4.766e-01 -0.012 0.990720 x5 1.684e+00 2.142e-01 7.864 1.08e-07 *x6 1.032e-02 1.343e-02 0.769 0.450665

11、 x7 3.655e-03 1.070e-02 0.342 0.736006 x8 -1.913e+01 3.197e+01 -0.598 0.555983 x9 5.052e+01 1.502e+02 0.336 0.739986 -Signif. codes: 0 * 0.001 * 0.01 * 0.05 . 0.1 1Residual standard error: 389.4 on 21 degrees of freedomMultiple R-squared: 0.9923, Adjusted R-squared: 0.9889 F-statistic: 298.9 on 9 an

12、d 21 DF, p-value: pre=fitted.values(lm) res=residuals(lm) sd(res)1 325.7967 res=residuals(lm) dy=step(lm)Start: AIC=377.73y x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 Df Sum of Sq RSS AIC- x4 1 21 3184326 375.73- x9 1 17149 3201454 375.90- x7 1 17700 3202005 375.90- x8 1 54295 3238599 376.26- x6 1 8

13、9586 3273891 376.59 3184305 377.73- x3 1 2662593 5846898 394.57- x2 1 4561056 7745361 403.29- x5 1 9377500 12561805 418.28- x1 1 23314547 26498852 441.42Step: AIC=375.73y x1 + x2 + x3 + x5 + x6 + x7 + x8 + x9 Df Sum of Sq RSS AIC- x9 1 17428 3201754 373.90- x7 1 18563 3202889 373.91- x8 1 54437 3238

14、763 374.26- x6 1 91813 3276139 374.61 3184326 375.73- x3 1 2936130 6120456 393.99- x2 1 5467941 8652267 404.72- x5 1 9393345 12577671 416.32- x1 1 25886086 29070412 442.29Step: AIC=373.9y x1 + x2 + x3 + x5 + x6 + x7 + x8 Df Sum of Sq RSS AIC- x7 1 34634 3236387 372.24- x6 1 74800 3276554 372.62- x8

15、1 82150 3283904 372.69 3201754 373.90- x3 1 3055353 6257107 392.67- x2 1 5725836 8927590 403.69- x5 1 9382624 12584378 414.33- x1 1 25868832 29070586 440.29Step: AIC=372.24y x1 + x2 + x3 + x5 + x6 + x8 Df Sum of Sq RSS AIC- x8 1 70813 3307201 370.91- x6 1 152777 3389165 371.67 3236387 372.24- x3 1 5

16、501284 8737672 401.02- x2 1 8895049 12131436 411.20- x5 1 9458098 12694485 412.60- x1 1 27733098 30969486 440.25Step: AIC=370.91y x1 + x2 + x3 + x5 + x6 Df Sum of Sq RSS AIC- x6 1 137540 3444741 370.17 3307201 370.91- x3 1 5771063 9078264 400.21- x2 1 8871193 12178394 409.32- x5 1 9473521 12780722 4

17、10.81- x1 1 28248162 31555363 438.83Step: AIC=370.17y x1 + x2 + x3 + x5 Df Sum of Sq RSS AIC 3444741 370.17- x3 1 5717883 9162624 398.50- x2 1 10249815 13694556 410.95- x5 1 10998313 14443054 412.60- x1 1 33258637 36703378 441.52 summary(dy)Call:lm(formula = y x1 + x2 + x3 + x5, data = h)Residuals:

18、Min 1Q Median 3Q Max -943.18 -161.05 12.74 250.93 566.25 Coefficients: Estimate Std. Error t value Pr(|t|) (Intercept) -1694.6269 562.9773 -3.010 0.00574 * x1 1.3642 0.0861 15.844 7.11e-15 *x2 1.7679 0.2010 8.796 2.86e-09 *x3 2.2894 0.3485 6.569 5.76e-07 *x5 1.7424 0.1912 9.111 1.42e-09 *-Signif. co

19、des: 0 * 0.001 * 0.01 * 0.05 . 0.1 1Residual standard error: 364 on 26 degrees of freedomMultiple R-squared: 0.9916, Adjusted R-squared: 0.9903 F-statistic: 769.2 on 4 and 26 DF, p-value: 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

20、,interval=confidence) fit lwr upr1 13718.67 13468.98 13968.36 h=ts(read.csv(F:/3.csv,header=TRUE) hTime Series:Start = 1 End = 56 Frequency = 1 X78 1, -58 2, 53 3, -63 4, 13 5, -6 6, -16 7, -14 8, 3 9, -7410, 8911, -4812, -1413, 3214, 5615, -8616, -6617, 5018, 2619, 5920, -4721, -8322, 223, -124, 12

21、425, -10626, 11327, -7628, -4729, -3230, 3931, -3032, 633, -7334, 1835, 236, -2437, 2338, -3839, 9140, -5641, -5842, 143, 1444, -445, 7746, -12747, 9748, 1049, -2850, -1751, 2352, -253, 4854, -13155, 6556, -17 plot(h,type=o) local(pkg adf=ur.df(as.vector(h),type=c(drift),selectlags=c(AIC) summary(ad

22、f)# # Augmented Dickey-Fuller Test Unit Root Test # # Test regression drift Call:lm(formula = z.diff z.lag.1 + 1 + z.diff.lag)Residuals: Min 1Q Median 3Q Max -96.191 -23.390 -0.581 18.446 133.241 Coefficients: Estimate Std. Error t value Pr(|t|) (Intercept) -9.4381 7.0489 -1.339 0.187 z.lag.1 -1.783

23、7 0.2386 -7.476 9.65e-10 *z.diff.lag 0.1956 0.1379 1.418 0.162 -Signif. codes: 0 * 0.001 * 0.01 * 0.05 . 0.1 1Residual standard error: 50.89 on 51 degrees of freedomMultiple R-squared: 0.7589, Adjusted R-squared: 0.7494 F-statistic: 80.25 on 2 and 51 DF, p-value: acf(h) pacf(h) ar=sarima(h,1,0,4,det

24、ails=F) ar$fitCall: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: ar1 ma1 ma2 ma3 ma4 xmean -0.0957 -0.7605 -0.051 -0.2591 0.0706 -5.0886s.e. 0.73

25、18 0.7244 0.637 0.2013 0.1939 0.4252sigma2 estimated as 1850: log likelihood = -291.97, aic = 597.95$degrees_of_freedom1 50$ttable Estimate SE t.value p.valuear1 -0.0957 0.7318 -0.1308 0.8965ma1 -0.7605 0.7244 -1.0498 0.2988ma2 -0.0510 0.6370 -0.0800 0.9365ma3 -0.2591 0.2013 -1.2875 0.2038ma4 0.0706

26、 0.1939 0.3641 0.7173xmean -5.0886 0.4252 -11.9668 0.0000$AIC1 8.73734$AICc1 8.814721$BIC1 7.954342 ma=sarima(h,0,1,1,details=F) ma$fitCall: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, relto

27、l = tol)Coefficients: ma1 constant -1.0000 0.1275s.e. 0.0452 0.4833sigma2 estimated as 3412: log likelihood = -303.77, aic = 613.53$degrees_of_freedom1 53$ttable Estimate SE t.value p.valuema1 -1.0000 0.0452 -22.1390 0.000constant 0.1275 0.4833 0.2638 0.793$AIC1 9.206399$AICc1 9.250355$BIC1 8.278733

28、 arma=sarima(h,1,1,1,details=F) arma$fitCall:stats:arima(x = xdata, order = c(p, d, q), seasonal = list(order = c(P, D, Q), period = S), xreg = constant, optim.control = list(trace = trc, REPORT = 1, reltol = tol)Coefficients: ar1 ma1 constant -0.4893 -1.0000 0.1052s.e. 0.1161 0.0469 0.2858sigma2 estimated as 2548: log likelihood = -296.27, aic = 600.53$degrees_of_freedom1 52$ttable Estimate SE t.value p.value