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Type 'q()' to quit R. > x <- array(list(1 + ,162556 + ,807 + ,213118 + ,6282154 + ,1 + ,29790 + ,444 + ,81767 + ,4321023 + ,1 + ,87550 + ,412 + ,153198 + ,4111912 + ,0 + ,84738 + ,428 + ,-26007 + ,223193 + ,1 + ,54660 + ,315 + ,126942 + ,1491348 + ,1 + ,42634 + ,168 + ,157214 + ,1629616 + ,0 + ,40949 + ,263 + ,129352 + ,1398893 + ,1 + ,45187 + ,267 + ,234817 + ,1926517 + ,1 + ,37704 + ,228 + ,60448 + ,983660 + ,1 + ,16275 + ,129 + ,47818 + ,1443586 + ,0 + ,25830 + ,104 + ,245546 + ,1073089 + ,0 + ,12679 + ,122 + ,48020 + ,984885 + ,1 + ,18014 + ,393 + ,-1710 + ,1405225 + ,0 + ,43556 + ,190 + ,32648 + ,227132 + ,1 + ,24811 + ,280 + ,95350 + ,929118 + ,0 + ,6575 + ,63 + ,151352 + ,1071292 + ,0 + ,7123 + ,102 + ,288170 + ,638830 + ,1 + ,21950 + ,265 + ,114337 + ,856956 + ,1 + ,37597 + ,234 + ,37884 + ,992426 + ,0 + ,17821 + ,277 + ,122844 + ,444477 + ,1 + ,12988 + ,73 + ,82340 + ,857217 + ,1 + ,22330 + ,67 + ,79801 + ,711969 + ,0 + ,13326 + ,103 + ,165548 + ,702380 + ,0 + ,16189 + ,290 + ,116384 + ,358589 + ,0 + ,7146 + ,83 + ,134028 + ,297978 + ,0 + ,15824 + ,56 + ,63838 + ,585715 + ,1 + ,27664 + ,236 + ,74996 + ,657954 + ,0 + ,11920 + ,73 + ,31080 + ,209458 + ,0 + ,8568 + ,34 + ,32168 + ,786690 + ,0 + ,14416 + ,139 + ,49857 + ,439798 + ,1 + ,3369 + ,26 + ,87161 + ,688779 + ,1 + ,11819 + ,70 + ,106113 + ,574339 + ,1 + ,6984 + ,40 + ,80570 + ,741409 + ,1 + ,4519 + ,42 + ,102129 + ,597793 + ,0 + ,2220 + ,12 + ,301670 + ,644190 + ,0 + ,18562 + ,211 + ,102313 + ,377934 + ,0 + ,10327 + ,74 + ,88577 + ,640273 + ,1 + ,5336 + ,80 + ,112477 + ,697458 + ,1 + ,2365 + ,83 + ,191778 + ,550608 + ,0 + ,4069 + ,131 + ,79804 + ,207393 + ,0 + ,8636 + ,203 + ,128294 + ,301607 + ,0 + ,13718 + ,56 + ,96448 + ,345783 + ,0 + ,4525 + ,89 + ,93811 + ,501749 + ,0 + ,6869 + ,88 + ,117520 + ,379983 + ,0 + ,4628 + ,39 + ,69159 + ,387475 + ,1 + ,3689 + ,25 + ,101792 + ,377305 + ,1 + ,4891 + ,49 + ,210568 + ,370837 + ,1 + ,7489 + ,149 + ,136996 + ,430866 + ,0 + ,4901 + ,58 + ,121920 + ,469107 + ,0 + ,2284 + ,41 + ,76403 + ,194493 + ,1 + ,3160 + ,90 + ,108094 + ,530670 + ,1 + ,4150 + ,136 + ,134759 + ,518365 + ,1 + ,7285 + ,97 + ,188873 + ,491303 + ,1 + ,1134 + ,63 + ,146216 + ,527021 + ,1 + ,4658 + ,114 + ,156608 + ,233773 + ,0 + ,2384 + ,77 + ,61348 + ,405972 + ,0 + ,3748 + ,6 + ,50350 + ,652925 + ,0 + ,5371 + ,47 + ,87720 + ,446211 + ,0 + ,1285 + ,51 + ,99489 + ,341340 + ,1 + ,9327 + ,85 + ,87419 + ,387699 + ,1 + ,5565 + ,43 + ,94355 + ,493408 + ,0 + ,1528 + ,32 + ,60326 + ,146494 + ,1 + ,3122 + ,25 + ,94670 + ,414462 + ,1 + ,7561 + ,77 + ,82425 + ,364304 + ,0 + ,2675 + ,54 + ,59017 + ,355178 + ,0 + ,13253 + ,251 + ,90829 + ,357760 + ,0 + ,880 + ,15 + ,80791 + ,261216 + ,1 + ,2053 + ,44 + ,100423 + ,397144 + ,0 + ,1424 + ,73 + ,131116 + ,374943 + ,1 + ,4036 + ,85 + ,100269 + ,424898 + ,1 + ,3045 + ,49 + ,27330 + ,202055 + ,0 + ,5119 + ,38 + ,39039 + ,378525 + ,0 + ,1431 + ,35 + ,106885 + ,310768 + ,0 + ,554 + ,9 + ,79285 + ,325738 + ,0 + ,1975 + ,34 + ,118881 + ,394510 + ,1 + ,1765 + ,20 + ,77623 + ,247060 + ,0 + ,1012 + ,29 + ,114768 + ,368078 + ,0 + ,810 + ,11 + ,74015 + ,236761 + ,0 + ,1280 + ,52 + ,69465 + ,312378 + ,1 + ,666 + ,13 + ,117869 + ,339836 + ,0 + ,1380 + ,29 + ,60982 + ,347385 + ,1 + ,4677 + ,66 + ,90131 + ,426280 + ,0 + ,876 + ,33 + ,138971 + ,352850 + ,0 + ,814 + ,15 + ,39625 + ,301881 + ,0 + ,514 + ,15 + ,102725 + ,377516 + ,1 + ,5692 + ,68 + ,64239 + ,357312 + ,0 + ,3642 + ,100 + ,90262 + ,458343 + ,0 + ,540 + ,13 + ,103960 + ,354228 + ,0 + ,2099 + ,45 + ,106611 + ,308636 + ,0 + ,567 + ,14 + ,103345 + ,386212 + ,0 + ,2001 + ,36 + ,95551 + ,393343 + ,1 + ,2949 + ,40 + ,82903 + ,378509 + ,0 + ,2253 + ,68 + ,63593 + ,452469 + ,1 + ,6533 + ,29 + ,126910 + ,364839 + ,0 + ,1889 + ,43 + ,37527 + ,358649 + ,1 + ,3055 + ,30 + ,60247 + ,376641 + ,0 + ,272 + ,9 + ,112995 + ,429112 + ,1 + ,1414 + ,22 + ,70184 + ,330546 + ,0 + ,2564 + ,19 + ,130140 + ,403560 + ,1 + ,1383 + ,9 + ,73221 + ,317892) + ,dim=c(5 + ,100) + ,dimnames=list(c('Group' + ,'Costs' + ,'Orders' + ,'Dividends' + ,'Wealth ') + ,1:100)) > y <- array(NA,dim=c(5,100),dimnames=list(c('Group','Costs','Orders','Dividends','Wealth '),1:100)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '5' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Wealth\r Group Costs Orders Dividends 1 6282154 1 162556 807 213118 2 4321023 1 29790 444 81767 3 4111912 1 87550 412 153198 4 223193 0 84738 428 -26007 5 1491348 1 54660 315 126942 6 1629616 1 42634 168 157214 7 1398893 0 40949 263 129352 8 1926517 1 45187 267 234817 9 983660 1 37704 228 60448 10 1443586 1 16275 129 47818 11 1073089 0 25830 104 245546 12 984885 0 12679 122 48020 13 1405225 1 18014 393 -1710 14 227132 0 43556 190 32648 15 929118 1 24811 280 95350 16 1071292 0 6575 63 151352 17 638830 0 7123 102 288170 18 856956 1 21950 265 114337 19 992426 1 37597 234 37884 20 444477 0 17821 277 122844 21 857217 1 12988 73 82340 22 711969 1 22330 67 79801 23 702380 0 13326 103 165548 24 358589 0 16189 290 116384 25 297978 0 7146 83 134028 26 585715 0 15824 56 63838 27 657954 1 27664 236 74996 28 209458 0 11920 73 31080 29 786690 0 8568 34 32168 30 439798 0 14416 139 49857 31 688779 1 3369 26 87161 32 574339 1 11819 70 106113 33 741409 1 6984 40 80570 34 597793 1 4519 42 102129 35 644190 0 2220 12 301670 36 377934 0 18562 211 102313 37 640273 0 10327 74 88577 38 697458 1 5336 80 112477 39 550608 1 2365 83 191778 40 207393 0 4069 131 79804 41 301607 0 8636 203 128294 42 345783 0 13718 56 96448 43 501749 0 4525 89 93811 44 379983 0 6869 88 117520 45 387475 0 4628 39 69159 46 377305 1 3689 25 101792 47 370837 1 4891 49 210568 48 430866 1 7489 149 136996 49 469107 0 4901 58 121920 50 194493 0 2284 41 76403 51 530670 1 3160 90 108094 52 518365 1 4150 136 134759 53 491303 1 7285 97 188873 54 527021 1 1134 63 146216 55 233773 1 4658 114 156608 56 405972 0 2384 77 61348 57 652925 0 3748 6 50350 58 446211 0 5371 47 87720 59 341340 0 1285 51 99489 60 387699 1 9327 85 87419 61 493408 1 5565 43 94355 62 146494 0 1528 32 60326 63 414462 1 3122 25 94670 64 364304 1 7561 77 82425 65 355178 0 2675 54 59017 66 357760 0 13253 251 90829 67 261216 0 880 15 80791 68 397144 1 2053 44 100423 69 374943 0 1424 73 131116 70 424898 1 4036 85 100269 71 202055 1 3045 49 27330 72 378525 0 5119 38 39039 73 310768 0 1431 35 106885 74 325738 0 554 9 79285 75 394510 0 1975 34 118881 76 247060 1 1765 20 77623 77 368078 0 1012 29 114768 78 236761 0 810 11 74015 79 312378 0 1280 52 69465 80 339836 1 666 13 117869 81 347385 0 1380 29 60982 82 426280 1 4677 66 90131 83 352850 0 876 33 138971 84 301881 0 814 15 39625 85 377516 0 514 15 102725 86 357312 1 5692 68 64239 87 458343 0 3642 100 90262 88 354228 0 540 13 103960 89 308636 0 2099 45 106611 90 386212 0 567 14 103345 91 393343 0 2001 36 95551 92 378509 1 2949 40 82903 93 452469 0 2253 68 63593 94 364839 1 6533 29 126910 95 358649 0 1889 43 37527 96 376641 1 3055 30 60247 97 429112 0 272 9 112995 98 330546 1 1414 22 70184 99 403560 0 2564 19 130140 100 317892 1 1383 9 73221 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Group Costs Orders Dividends -1.192e+05 1.948e+05 1.905e+01 1.974e+03 2.423e+00 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -2053625 -155094 4637 173837 2603268 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.192e+05 1.147e+05 -1.039 0.30146 Group 1.948e+05 9.782e+04 1.991 0.04930 * Costs 1.905e+01 4.504e+00 4.229 5.4e-05 *** Orders 1.974e+03 8.010e+02 2.465 0.01550 * Dividends 2.423e+00 9.023e-01 2.686 0.00855 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 474700 on 95 degrees of freedom Multiple R-squared: 0.691, Adjusted R-squared: 0.678 F-statistic: 53.11 on 4 and 95 DF, p-value: < 2.2e-16 > if (n > n25) { + kp3 <- k + 3 + nmkm3 <- n - k - 3 + gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) + numgqtests <- 0 + numsignificant1 <- 0 + numsignificant5 <- 0 + numsignificant10 <- 0 + for (mypoint in kp3:nmkm3) { + j <- 0 + numgqtests <- numgqtests + 1 + for (myalt in c('greater', 'two.sided', 'less')) { + j <- j + 1 + gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value + } + if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 + } + gqarr + } [,1] [,2] [,3] [1,] 1.0000000 3.188291e-12 1.594146e-12 [2,] 1.0000000 1.445316e-11 7.226581e-12 [3,] 1.0000000 1.422800e-14 7.114001e-15 [4,] 1.0000000 4.736008e-15 2.368004e-15 [5,] 1.0000000 4.465912e-18 2.232956e-18 [6,] 1.0000000 4.752790e-24 2.376395e-24 [7,] 1.0000000 1.070465e-26 5.352324e-27 [8,] 1.0000000 1.135616e-28 5.678079e-29 [9,] 1.0000000 4.888335e-32 2.444167e-32 [10,] 1.0000000 1.159319e-32 5.796595e-33 [11,] 1.0000000 4.431863e-34 2.215931e-34 [12,] 1.0000000 1.777156e-33 8.885778e-34 [13,] 1.0000000 1.908190e-33 9.540950e-34 [14,] 1.0000000 7.049539e-34 3.524769e-34 [15,] 1.0000000 5.057740e-33 2.528870e-33 [16,] 1.0000000 1.506711e-32 7.533556e-33 [17,] 1.0000000 2.865691e-32 1.432846e-32 [18,] 1.0000000 9.711276e-32 4.855638e-32 [19,] 1.0000000 1.107949e-31 5.539746e-32 [20,] 1.0000000 1.865000e-31 9.325000e-32 [21,] 1.0000000 1.102194e-31 5.510969e-32 [22,] 1.0000000 5.240546e-34 2.620273e-34 [23,] 1.0000000 3.523963e-33 1.761982e-33 [24,] 1.0000000 1.607923e-33 8.039617e-34 [25,] 1.0000000 8.842436e-33 4.421218e-33 [26,] 1.0000000 1.016900e-33 5.084500e-34 [27,] 1.0000000 1.572701e-33 7.863507e-34 [28,] 1.0000000 7.866485e-33 3.933243e-33 [29,] 1.0000000 3.238139e-32 1.619070e-32 [30,] 1.0000000 1.228866e-32 6.144328e-33 [31,] 1.0000000 8.360904e-34 4.180452e-34 [32,] 1.0000000 1.842082e-33 9.210411e-34 [33,] 1.0000000 3.688351e-33 1.844176e-33 [34,] 1.0000000 9.325898e-33 4.662949e-33 [35,] 1.0000000 4.388534e-32 2.194267e-32 [36,] 1.0000000 1.182747e-31 5.913737e-32 [37,] 1.0000000 8.481695e-31 4.240847e-31 [38,] 1.0000000 4.831213e-30 2.415606e-30 [39,] 1.0000000 3.512854e-29 1.756427e-29 [40,] 1.0000000 8.410415e-29 4.205208e-29 [41,] 1.0000000 3.988873e-28 1.994436e-28 [42,] 1.0000000 2.184765e-27 1.092382e-27 [43,] 1.0000000 2.997141e-27 1.498570e-27 [44,] 1.0000000 4.400488e-27 2.200244e-27 [45,] 1.0000000 5.566117e-27 2.783059e-27 [46,] 1.0000000 2.928824e-26 1.464412e-26 [47,] 1.0000000 1.166038e-26 5.830189e-27 [48,] 1.0000000 2.094244e-26 1.047122e-26 [49,] 1.0000000 8.770881e-26 4.385440e-26 [50,] 1.0000000 1.234581e-28 6.172903e-29 [51,] 1.0000000 5.906857e-28 2.953429e-28 [52,] 1.0000000 5.424628e-27 2.712314e-27 [53,] 1.0000000 4.849997e-26 2.424998e-26 [54,] 1.0000000 8.773622e-26 4.386811e-26 [55,] 1.0000000 8.071086e-27 4.035543e-27 [56,] 1.0000000 4.904336e-26 2.452168e-26 [57,] 1.0000000 5.171989e-25 2.585995e-25 [58,] 1.0000000 4.951253e-24 2.475627e-24 [59,] 1.0000000 1.394709e-24 6.973547e-25 [60,] 1.0000000 5.915708e-24 2.957854e-24 [61,] 1.0000000 4.657761e-23 2.328881e-23 [62,] 1.0000000 3.142255e-22 1.571128e-22 [63,] 1.0000000 3.308446e-21 1.654223e-21 [64,] 1.0000000 2.156317e-21 1.078158e-21 [65,] 1.0000000 2.194576e-20 1.097288e-20 [66,] 1.0000000 9.681695e-20 4.840847e-20 [67,] 1.0000000 1.121448e-18 5.607242e-19 [68,] 1.0000000 1.289507e-17 6.447536e-18 [69,] 1.0000000 2.910542e-17 1.455271e-17 [70,] 1.0000000 3.433684e-16 1.716842e-16 [71,] 1.0000000 3.282798e-16 1.641399e-16 [72,] 1.0000000 8.653895e-16 4.326947e-16 [73,] 1.0000000 1.097634e-14 5.488168e-15 [74,] 1.0000000 1.395901e-13 6.979505e-14 [75,] 1.0000000 1.371864e-12 6.859322e-13 [76,] 1.0000000 6.605460e-12 3.302730e-12 [77,] 1.0000000 5.747628e-11 2.873814e-11 [78,] 1.0000000 7.583006e-10 3.791503e-10 [79,] 1.0000000 8.534733e-09 4.267367e-09 [80,] 1.0000000 9.675454e-08 4.837727e-08 [81,] 0.9999995 9.709159e-07 4.854580e-07 [82,] 0.9999999 1.509184e-07 7.545918e-08 [83,] 0.9999985 2.980863e-06 1.490432e-06 [84,] 0.9999797 4.052477e-05 2.026239e-05 [85,] 0.9996236 7.527618e-04 3.763809e-04 > postscript(file="/var/www/html/rcomp/tmp/1fw2c1291216634.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2fw2c1291216634.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/385kx1291216634.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/485kx1291216634.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/585kx1291216634.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 100 Frequency = 1 1 2 3 4 5 1000593.8580 2603268.0642 1184070.5633 -2053625.2042 -554894.3964 6 7 8 9 10 29306.2774 -94568.6500 -105927.6053 -406720.7226 687433.1069 11 12 13 14 15 -100042.6567 505355.3482 214744.6226 -937525.3218 -402925.9541 16 17 18 19 20 574116.0414 -277322.5262 -436988.4486 -353085.0820 -620328.9841 21 22 23 24 25 190580.3178 -114609.0351 -36757.2287 -685144.0058 -207580.5859 26 27 28 29 30 138256.4761 -592239.4450 -117823.5585 597617.1407 -110831.7627 31 32 33 34 35 286469.1387 -121715.6149 258574.0677 105718.8214 -33597.9619 36 37 38 39 40 -520929.5537 202031.6183 89722.4071 -198624.7630 -202927.9415 41 42 43 44 45 -455356.1565 -140583.0998 131722.0507 -90168.5977 173939.5653 46 47 48 49 50 -64579.7952 -404914.5230 -413521.6707 85008.0271 4101.9811 51 52 53 54 55 -44740.9406 -231336.7968 -372243.5432 -48871.3469 -535114.6232 56 57 58 59 60 179082.1676 566882.2001 157751.4598 94291.5522 -245204.9428 61 62 63 64 65 -1725.8676 27229.7482 635.1905 -207066.2696 173803.3724 66 67 68 69 70 -491126.1486 138266.7145 -47774.1793 5172.6989 -138365.0631 71 72 73 74 75 -94509.3434 230599.0333 74605.6948 224493.4435 120891.3494 76 77 78 79 80 -89739.3887 132640.2159 139462.0542 136205.2031 -59735.0302 81 82 83 84 85 235273.0872 -87113.6332 53456.1755 279943.0843 208387.1317 86 87 88 89 90 -116621.2913 92017.2594 185559.8734 40670.8260 216545.5526 91 92 93 94 95 171814.1801 -33123.4596 240403.1488 -199980.0264 266038.0921 96 97 98 99 100 37633.2213 251552.0566 14509.8941 121054.3194 20753.2505 > postscript(file="/var/www/html/rcomp/tmp/6ie101291216634.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 100 Frequency = 1 lag(myerror, k = 1) myerror 0 1000593.8580 NA 1 2603268.0642 1000593.8580 2 1184070.5633 2603268.0642 3 -2053625.2042 1184070.5633 4 -554894.3964 -2053625.2042 5 29306.2774 -554894.3964 6 -94568.6500 29306.2774 7 -105927.6053 -94568.6500 8 -406720.7226 -105927.6053 9 687433.1069 -406720.7226 10 -100042.6567 687433.1069 11 505355.3482 -100042.6567 12 214744.6226 505355.3482 13 -937525.3218 214744.6226 14 -402925.9541 -937525.3218 15 574116.0414 -402925.9541 16 -277322.5262 574116.0414 17 -436988.4486 -277322.5262 18 -353085.0820 -436988.4486 19 -620328.9841 -353085.0820 20 190580.3178 -620328.9841 21 -114609.0351 190580.3178 22 -36757.2287 -114609.0351 23 -685144.0058 -36757.2287 24 -207580.5859 -685144.0058 25 138256.4761 -207580.5859 26 -592239.4450 138256.4761 27 -117823.5585 -592239.4450 28 597617.1407 -117823.5585 29 -110831.7627 597617.1407 30 286469.1387 -110831.7627 31 -121715.6149 286469.1387 32 258574.0677 -121715.6149 33 105718.8214 258574.0677 34 -33597.9619 105718.8214 35 -520929.5537 -33597.9619 36 202031.6183 -520929.5537 37 89722.4071 202031.6183 38 -198624.7630 89722.4071 39 -202927.9415 -198624.7630 40 -455356.1565 -202927.9415 41 -140583.0998 -455356.1565 42 131722.0507 -140583.0998 43 -90168.5977 131722.0507 44 173939.5653 -90168.5977 45 -64579.7952 173939.5653 46 -404914.5230 -64579.7952 47 -413521.6707 -404914.5230 48 85008.0271 -413521.6707 49 4101.9811 85008.0271 50 -44740.9406 4101.9811 51 -231336.7968 -44740.9406 52 -372243.5432 -231336.7968 53 -48871.3469 -372243.5432 54 -535114.6232 -48871.3469 55 179082.1676 -535114.6232 56 566882.2001 179082.1676 57 157751.4598 566882.2001 58 94291.5522 157751.4598 59 -245204.9428 94291.5522 60 -1725.8676 -245204.9428 61 27229.7482 -1725.8676 62 635.1905 27229.7482 63 -207066.2696 635.1905 64 173803.3724 -207066.2696 65 -491126.1486 173803.3724 66 138266.7145 -491126.1486 67 -47774.1793 138266.7145 68 5172.6989 -47774.1793 69 -138365.0631 5172.6989 70 -94509.3434 -138365.0631 71 230599.0333 -94509.3434 72 74605.6948 230599.0333 73 224493.4435 74605.6948 74 120891.3494 224493.4435 75 -89739.3887 120891.3494 76 132640.2159 -89739.3887 77 139462.0542 132640.2159 78 136205.2031 139462.0542 79 -59735.0302 136205.2031 80 235273.0872 -59735.0302 81 -87113.6332 235273.0872 82 53456.1755 -87113.6332 83 279943.0843 53456.1755 84 208387.1317 279943.0843 85 -116621.2913 208387.1317 86 92017.2594 -116621.2913 87 185559.8734 92017.2594 88 40670.8260 185559.8734 89 216545.5526 40670.8260 90 171814.1801 216545.5526 91 -33123.4596 171814.1801 92 240403.1488 -33123.4596 93 -199980.0264 240403.1488 94 266038.0921 -199980.0264 95 37633.2213 266038.0921 96 251552.0566 37633.2213 97 14509.8941 251552.0566 98 121054.3194 14509.8941 99 20753.2505 121054.3194 100 NA 20753.2505 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 2603268.0642 1000593.8580 [2,] 1184070.5633 2603268.0642 [3,] -2053625.2042 1184070.5633 [4,] -554894.3964 -2053625.2042 [5,] 29306.2774 -554894.3964 [6,] -94568.6500 29306.2774 [7,] -105927.6053 -94568.6500 [8,] -406720.7226 -105927.6053 [9,] 687433.1069 -406720.7226 [10,] -100042.6567 687433.1069 [11,] 505355.3482 -100042.6567 [12,] 214744.6226 505355.3482 [13,] -937525.3218 214744.6226 [14,] -402925.9541 -937525.3218 [15,] 574116.0414 -402925.9541 [16,] -277322.5262 574116.0414 [17,] -436988.4486 -277322.5262 [18,] -353085.0820 -436988.4486 [19,] -620328.9841 -353085.0820 [20,] 190580.3178 -620328.9841 [21,] -114609.0351 190580.3178 [22,] -36757.2287 -114609.0351 [23,] -685144.0058 -36757.2287 [24,] -207580.5859 -685144.0058 [25,] 138256.4761 -207580.5859 [26,] -592239.4450 138256.4761 [27,] -117823.5585 -592239.4450 [28,] 597617.1407 -117823.5585 [29,] -110831.7627 597617.1407 [30,] 286469.1387 -110831.7627 [31,] -121715.6149 286469.1387 [32,] 258574.0677 -121715.6149 [33,] 105718.8214 258574.0677 [34,] -33597.9619 105718.8214 [35,] -520929.5537 -33597.9619 [36,] 202031.6183 -520929.5537 [37,] 89722.4071 202031.6183 [38,] -198624.7630 89722.4071 [39,] -202927.9415 -198624.7630 [40,] -455356.1565 -202927.9415 [41,] -140583.0998 -455356.1565 [42,] 131722.0507 -140583.0998 [43,] -90168.5977 131722.0507 [44,] 173939.5653 -90168.5977 [45,] -64579.7952 173939.5653 [46,] -404914.5230 -64579.7952 [47,] -413521.6707 -404914.5230 [48,] 85008.0271 -413521.6707 [49,] 4101.9811 85008.0271 [50,] -44740.9406 4101.9811 [51,] -231336.7968 -44740.9406 [52,] -372243.5432 -231336.7968 [53,] -48871.3469 -372243.5432 [54,] -535114.6232 -48871.3469 [55,] 179082.1676 -535114.6232 [56,] 566882.2001 179082.1676 [57,] 157751.4598 566882.2001 [58,] 94291.5522 157751.4598 [59,] -245204.9428 94291.5522 [60,] -1725.8676 -245204.9428 [61,] 27229.7482 -1725.8676 [62,] 635.1905 27229.7482 [63,] -207066.2696 635.1905 [64,] 173803.3724 -207066.2696 [65,] -491126.1486 173803.3724 [66,] 138266.7145 -491126.1486 [67,] -47774.1793 138266.7145 [68,] 5172.6989 -47774.1793 [69,] -138365.0631 5172.6989 [70,] -94509.3434 -138365.0631 [71,] 230599.0333 -94509.3434 [72,] 74605.6948 230599.0333 [73,] 224493.4435 74605.6948 [74,] 120891.3494 224493.4435 [75,] -89739.3887 120891.3494 [76,] 132640.2159 -89739.3887 [77,] 139462.0542 132640.2159 [78,] 136205.2031 139462.0542 [79,] -59735.0302 136205.2031 [80,] 235273.0872 -59735.0302 [81,] -87113.6332 235273.0872 [82,] 53456.1755 -87113.6332 [83,] 279943.0843 53456.1755 [84,] 208387.1317 279943.0843 [85,] -116621.2913 208387.1317 [86,] 92017.2594 -116621.2913 [87,] 185559.8734 92017.2594 [88,] 40670.8260 185559.8734 [89,] 216545.5526 40670.8260 [90,] 171814.1801 216545.5526 [91,] -33123.4596 171814.1801 [92,] 240403.1488 -33123.4596 [93,] -199980.0264 240403.1488 [94,] 266038.0921 -199980.0264 [95,] 37633.2213 266038.0921 [96,] 251552.0566 37633.2213 [97,] 14509.8941 251552.0566 [98,] 121054.3194 14509.8941 [99,] 20753.2505 121054.3194 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 2603268.0642 1000593.8580 2 1184070.5633 2603268.0642 3 -2053625.2042 1184070.5633 4 -554894.3964 -2053625.2042 5 29306.2774 -554894.3964 6 -94568.6500 29306.2774 7 -105927.6053 -94568.6500 8 -406720.7226 -105927.6053 9 687433.1069 -406720.7226 10 -100042.6567 687433.1069 11 505355.3482 -100042.6567 12 214744.6226 505355.3482 13 -937525.3218 214744.6226 14 -402925.9541 -937525.3218 15 574116.0414 -402925.9541 16 -277322.5262 574116.0414 17 -436988.4486 -277322.5262 18 -353085.0820 -436988.4486 19 -620328.9841 -353085.0820 20 190580.3178 -620328.9841 21 -114609.0351 190580.3178 22 -36757.2287 -114609.0351 23 -685144.0058 -36757.2287 24 -207580.5859 -685144.0058 25 138256.4761 -207580.5859 26 -592239.4450 138256.4761 27 -117823.5585 -592239.4450 28 597617.1407 -117823.5585 29 -110831.7627 597617.1407 30 286469.1387 -110831.7627 31 -121715.6149 286469.1387 32 258574.0677 -121715.6149 33 105718.8214 258574.0677 34 -33597.9619 105718.8214 35 -520929.5537 -33597.9619 36 202031.6183 -520929.5537 37 89722.4071 202031.6183 38 -198624.7630 89722.4071 39 -202927.9415 -198624.7630 40 -455356.1565 -202927.9415 41 -140583.0998 -455356.1565 42 131722.0507 -140583.0998 43 -90168.5977 131722.0507 44 173939.5653 -90168.5977 45 -64579.7952 173939.5653 46 -404914.5230 -64579.7952 47 -413521.6707 -404914.5230 48 85008.0271 -413521.6707 49 4101.9811 85008.0271 50 -44740.9406 4101.9811 51 -231336.7968 -44740.9406 52 -372243.5432 -231336.7968 53 -48871.3469 -372243.5432 54 -535114.6232 -48871.3469 55 179082.1676 -535114.6232 56 566882.2001 179082.1676 57 157751.4598 566882.2001 58 94291.5522 157751.4598 59 -245204.9428 94291.5522 60 -1725.8676 -245204.9428 61 27229.7482 -1725.8676 62 635.1905 27229.7482 63 -207066.2696 635.1905 64 173803.3724 -207066.2696 65 -491126.1486 173803.3724 66 138266.7145 -491126.1486 67 -47774.1793 138266.7145 68 5172.6989 -47774.1793 69 -138365.0631 5172.6989 70 -94509.3434 -138365.0631 71 230599.0333 -94509.3434 72 74605.6948 230599.0333 73 224493.4435 74605.6948 74 120891.3494 224493.4435 75 -89739.3887 120891.3494 76 132640.2159 -89739.3887 77 139462.0542 132640.2159 78 136205.2031 139462.0542 79 -59735.0302 136205.2031 80 235273.0872 -59735.0302 81 -87113.6332 235273.0872 82 53456.1755 -87113.6332 83 279943.0843 53456.1755 84 208387.1317 279943.0843 85 -116621.2913 208387.1317 86 92017.2594 -116621.2913 87 185559.8734 92017.2594 88 40670.8260 185559.8734 89 216545.5526 40670.8260 90 171814.1801 216545.5526 91 -33123.4596 171814.1801 92 240403.1488 -33123.4596 93 -199980.0264 240403.1488 94 266038.0921 -199980.0264 95 37633.2213 266038.0921 96 251552.0566 37633.2213 97 14509.8941 251552.0566 98 121054.3194 14509.8941 99 20753.2505 121054.3194 > plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') > lines(lowess(z)) > abline(lm(z)) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/7t50l1291216634.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8t50l1291216634.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9t50l1291216634.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10mfz61291216634.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/117xgt1291216634.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,mysum$coefficients[i,1]) + a<-table.element(a, round(mysum$coefficients[i,2],6)) + a<-table.element(a, round(mysum$coefficients[i,3],4)) + a<-table.element(a, round(mysum$coefficients[i,4],6)) + a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/12byeh1291216634.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$r.squared)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, mysum$r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, mysum$adj.r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, mysum$fstatistic[1]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[2]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[3]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, mysum$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/13p8c81291216634.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/14aqbe1291216634.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,gqarr[mypoint-kp3+1,1]) + a<-table.element(a,gqarr[mypoint-kp3+1,2]) + a<-table.element(a,gqarr[mypoint-kp3+1,3]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/www/html/rcomp/tmp/15dr9k1291216634.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,numsignificant1) + a<-table.element(a,numsignificant1/numgqtests) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,numsignificant5) + a<-table.element(a,numsignificant5/numgqtests) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,numsignificant10) + a<-table.element(a,numsignificant10/numgqtests) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/www/html/rcomp/tmp/16hr8p1291216634.tab") + } > > try(system("convert tmp/1fw2c1291216634.ps tmp/1fw2c1291216634.png",intern=TRUE)) character(0) > try(system("convert tmp/2fw2c1291216634.ps tmp/2fw2c1291216634.png",intern=TRUE)) character(0) > try(system("convert tmp/385kx1291216634.ps tmp/385kx1291216634.png",intern=TRUE)) character(0) > try(system("convert tmp/485kx1291216634.ps tmp/485kx1291216634.png",intern=TRUE)) character(0) > try(system("convert tmp/585kx1291216634.ps tmp/585kx1291216634.png",intern=TRUE)) character(0) > try(system("convert tmp/6ie101291216634.ps tmp/6ie101291216634.png",intern=TRUE)) character(0) > try(system("convert tmp/7t50l1291216634.ps tmp/7t50l1291216634.png",intern=TRUE)) character(0) > try(system("convert tmp/8t50l1291216634.ps tmp/8t50l1291216634.png",intern=TRUE)) character(0) > try(system("convert tmp/9t50l1291216634.ps tmp/9t50l1291216634.png",intern=TRUE)) character(0) > try(system("convert tmp/10mfz61291216634.ps tmp/10mfz61291216634.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.076 1.726 8.876