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Type 'q()' to quit R. > x <- array(list(1 + ,162556 + ,162556 + ,1081 + ,1081 + ,213118 + ,213118 + ,230380558 + ,6282929 + ,1 + ,29790 + ,29790 + ,309 + ,309 + ,81767 + ,81767 + ,25266003 + ,4324047 + ,1 + ,87550 + ,87550 + ,458 + ,458 + ,153198 + ,153198 + ,70164684 + ,4108272 + ,0 + ,84738 + ,0 + ,588 + ,0 + ,-26007 + ,0 + ,-15292116 + ,-1212617 + ,1 + ,54660 + ,54660 + ,299 + ,299 + ,126942 + ,126942 + ,37955658 + ,1485329 + ,1 + ,42634 + ,42634 + ,156 + ,156 + ,157214 + ,157214 + ,24525384 + ,1779876 + ,0 + ,40949 + ,0 + ,481 + ,0 + ,129352 + ,0 + ,62218312 + ,1367203 + ,1 + ,42312 + ,42312 + ,323 + ,323 + ,234817 + ,234817 + ,75845891 + ,2519076 + ,1 + ,37704 + ,37704 + ,452 + ,452 + ,60448 + ,60448 + ,27322496 + ,912684 + ,1 + ,16275 + ,16275 + ,109 + ,109 + ,47818 + ,47818 + ,5212162 + ,1443586 + ,0 + ,25830 + ,0 + ,115 + ,0 + ,245546 + ,0 + ,28237790 + ,1220017 + ,0 + ,12679 + ,0 + ,110 + ,0 + ,48020 + ,0 + ,5282200 + ,984885 + ,1 + ,18014 + ,18014 + ,239 + ,239 + ,-1710 + 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,73221 + ,73221 + ,805431 + ,329118) + ,dim=c(9 + ,100) + ,dimnames=list(c('Group' + ,'Costs' + ,'GrCosts' + ,'Trades' + ,'GrTrades' + ,'Dividends' + ,'GrDiv' + ,'TrDiv' + ,'Wealth ') + ,1:100)) > y <- array(NA,dim=c(9,100),dimnames=list(c('Group','Costs','GrCosts','Trades','GrTrades','Dividends','GrDiv','TrDiv','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 = '9' > #'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 > 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 GrCosts Trades GrTrades Dividends GrDiv TrDiv 1 6282929 1 162556 162556 1081 1081 213118 213118 230380558 2 4324047 1 29790 29790 309 309 81767 81767 25266003 3 4108272 1 87550 87550 458 458 153198 153198 70164684 4 -1212617 0 84738 0 588 0 -26007 0 -15292116 5 1485329 1 54660 54660 299 299 126942 126942 37955658 6 1779876 1 42634 42634 156 156 157214 157214 24525384 7 1367203 0 40949 0 481 0 129352 0 62218312 8 2519076 1 42312 42312 323 323 234817 234817 75845891 9 912684 1 37704 37704 452 452 60448 60448 27322496 10 1443586 1 16275 16275 109 109 47818 47818 5212162 11 1220017 0 25830 0 115 0 245546 0 28237790 12 984885 0 12679 0 110 0 48020 0 5282200 13 1457425 1 18014 18014 239 239 -1710 -1710 -408690 14 -572920 0 43556 0 247 0 32648 0 8064056 15 929144 1 24524 24524 497 497 95350 95350 47388950 16 1151176 0 6532 0 103 0 151352 0 15589256 17 790090 0 7123 0 109 0 288170 0 31410530 18 774497 1 20813 20813 502 502 114337 114337 57397174 19 990576 1 37597 37597 248 248 37884 37884 9395232 20 454195 0 17821 0 373 0 122844 0 45820812 21 876607 1 12988 12988 119 119 82340 82340 9798460 22 711969 1 22330 22330 84 84 79801 79801 6703284 23 702380 0 13326 0 102 0 165548 0 16885896 24 264449 0 16189 0 295 0 116384 0 34333280 25 450033 0 7146 0 105 0 134028 0 14072940 26 541063 0 15824 0 64 0 63838 0 4085632 27 588864 1 26088 26088 267 267 74996 74996 20023932 28 -37216 0 11326 0 129 0 31080 0 4009320 29 783310 0 8568 0 37 0 32168 0 1190216 30 467359 0 14416 0 361 0 49857 0 17998377 31 688779 1 3369 3369 28 28 87161 87161 2440508 32 608419 1 11819 11819 85 85 106113 106113 9019605 33 696348 1 6620 6620 44 44 80570 80570 3545080 34 597793 1 4519 4519 49 49 102129 102129 5004321 35 821730 0 2220 0 22 0 301670 0 6636740 36 377934 0 18562 0 155 0 102313 0 15858515 37 651939 0 10327 0 91 0 88577 0 8060507 38 697458 1 5336 5336 81 81 112477 112477 9110637 39 700368 1 2365 2365 79 79 191778 191778 15150462 40 225986 0 4069 0 145 0 79804 0 11571580 41 348695 0 7710 0 816 0 128294 0 104687904 42 373683 0 13718 0 61 0 96448 0 5883328 43 501709 0 4525 0 226 0 93811 0 21201286 44 413743 0 6869 0 105 0 117520 0 12339600 45 379825 0 4628 0 62 0 69159 0 4287858 46 336260 1 3653 3653 24 24 101792 101792 2443008 47 636765 1 1265 1265 26 26 210568 210568 5474768 48 481231 1 7489 7489 322 322 136996 136996 44112712 49 469107 0 4901 0 84 0 121920 0 10241280 50 211928 0 2284 0 33 0 76403 0 2521299 51 563925 1 3160 3160 108 108 108094 108094 11674152 52 511939 1 4150 4150 150 150 134759 134759 20213850 53 521016 1 7285 7285 115 115 188873 188873 21720395 54 543856 1 1134 1134 162 162 146216 146216 23686992 55 329304 1 4658 4658 158 158 156608 156608 24744064 56 423262 0 2384 0 97 0 61348 0 5950756 57 509665 0 3748 0 9 0 50350 0 453150 58 455881 0 5371 0 66 0 87720 0 5789520 59 367772 0 1285 0 107 0 99489 0 10645323 60 406339 1 9327 9327 101 101 87419 87419 8829319 61 493408 1 5565 5565 47 47 94355 94355 4434685 62 232942 0 1528 0 38 0 60326 0 2292388 63 416002 1 3122 3122 34 34 94670 94670 3218780 64 337430 1 7317 7317 84 84 82425 82425 6923700 65 361517 0 2675 0 79 0 59017 0 4662343 66 360962 0 13253 0 947 0 90829 0 86015063 67 235561 0 880 0 74 0 80791 0 5978534 68 408247 1 2053 2053 53 53 100423 100423 5322419 69 450296 0 1424 0 94 0 131116 0 12324904 70 418799 1 4036 4036 63 63 100269 100269 6316947 71 247405 1 3045 3045 58 58 27330 27330 1585140 72 378519 0 5119 0 49 0 39039 0 1912911 73 326638 0 1431 0 34 0 106885 0 3634090 74 328233 0 554 0 11 0 79285 0 872135 75 386225 0 1975 0 35 0 118881 0 4160835 76 283662 1 1286 1286 17 17 77623 77623 1319591 77 370225 0 1012 0 47 0 114768 0 5394096 78 269236 0 810 0 43 0 74015 0 3182645 79 365732 0 1280 0 117 0 69465 0 8127405 80 420383 1 666 666 171 171 117869 117869 20155599 81 345811 0 1380 0 26 0 60982 0 1585532 82 431809 1 4608 4608 73 73 90131 90131 6579563 83 418876 0 876 0 59 0 138971 0 8199289 84 297476 0 814 0 18 0 39625 0 713250 85 416776 0 514 0 15 0 102725 0 1540875 86 357257 1 5692 5692 72 72 64239 64239 4625208 87 458343 0 3642 0 86 0 90262 0 7762532 88 388386 0 540 0 14 0 103960 0 1455440 89 358934 0 2099 0 64 0 106611 0 6823104 90 407560 0 567 0 11 0 103345 0 1136795 91 392558 0 2001 0 52 0 95551 0 4968652 92 373177 1 2949 2949 41 41 82903 82903 3399023 93 428370 0 2253 0 99 0 63593 0 6295707 94 369419 1 6533 6533 75 75 126910 126910 9518250 95 358649 0 1889 0 45 0 37527 0 1688715 96 376641 1 3055 3055 43 43 60247 60247 2590621 97 467427 0 272 0 8 0 112995 0 903960 98 364885 1 1414 1414 198 198 70184 70184 13896432 99 436230 0 2564 0 22 0 130140 0 2863080 100 329118 1 1383 1383 11 11 73221 73221 805431 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Group Costs GrCosts Trades GrTrades 2.472e+05 1.824e+05 -3.390e+00 3.661e+01 -9.509e+02 -6.481e+01 Dividends GrDiv TrDiv 2.256e+00 -2.694e+00 8.814e-03 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -735059 -157487 -70014 84406 3031952 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.472e+05 1.378e+05 1.794 0.0762 . Group 1.824e+05 2.212e+05 0.824 0.4119 Costs -3.390e+00 5.733e+00 -0.591 0.5558 GrCosts 3.661e+01 8.427e+00 4.344 3.63e-05 *** Trades -9.509e+02 6.782e+02 -1.402 0.1643 GrTrades -6.481e+01 7.606e+02 -0.085 0.9323 Dividends 2.256e+00 1.192e+00 1.892 0.0616 . GrDiv -2.694e+00 1.791e+00 -1.504 0.1361 TrDiv 8.814e-03 5.171e-03 1.705 0.0917 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 427500 on 91 degrees of freedom Multiple R-squared: 0.7807, Adjusted R-squared: 0.7614 F-statistic: 40.5 on 8 and 91 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 1.033658e-23 5.168288e-24 [2,] 1.0000000 4.397215e-26 2.198608e-26 [3,] 1.0000000 1.143513e-29 5.717566e-30 [4,] 1.0000000 7.555857e-30 3.777929e-30 [5,] 1.0000000 6.450887e-33 3.225443e-33 [6,] 1.0000000 1.016877e-32 5.084385e-33 [7,] 1.0000000 2.891019e-32 1.445510e-32 [8,] 1.0000000 9.960913e-33 4.980457e-33 [9,] 1.0000000 2.217084e-32 1.108542e-32 [10,] 1.0000000 2.956038e-33 1.478019e-33 [11,] 1.0000000 3.071784e-33 1.535892e-33 [12,] 1.0000000 1.239624e-32 6.198118e-33 [13,] 1.0000000 1.474330e-32 7.371650e-33 [14,] 1.0000000 9.695610e-32 4.847805e-32 [15,] 1.0000000 5.561865e-31 2.780933e-31 [16,] 1.0000000 1.089926e-30 5.449632e-31 [17,] 1.0000000 1.644172e-32 8.220858e-33 [18,] 1.0000000 5.494118e-35 2.747059e-35 [19,] 1.0000000 3.920701e-34 1.960350e-34 [20,] 1.0000000 1.044335e-34 5.221673e-35 [21,] 1.0000000 4.471712e-34 2.235856e-34 [22,] 1.0000000 3.682900e-35 1.841450e-35 [23,] 1.0000000 5.396272e-35 2.698136e-35 [24,] 1.0000000 1.522614e-34 7.613068e-35 [25,] 1.0000000 3.570169e-34 1.785085e-34 [26,] 1.0000000 1.509830e-34 7.549152e-35 [27,] 1.0000000 5.165974e-36 2.582987e-36 [28,] 1.0000000 1.108873e-35 5.544364e-36 [29,] 1.0000000 9.072300e-36 4.536150e-36 [30,] 1.0000000 5.308463e-35 2.654231e-35 [31,] 1.0000000 5.995773e-35 2.997887e-35 [32,] 1.0000000 1.315342e-34 6.576711e-35 [33,] 1.0000000 7.240782e-34 3.620391e-34 [34,] 1.0000000 5.862044e-33 2.931022e-33 [35,] 1.0000000 2.812520e-32 1.406260e-32 [36,] 1.0000000 2.635542e-31 1.317771e-31 [37,] 1.0000000 1.523154e-30 7.615768e-31 [38,] 1.0000000 1.413838e-29 7.069189e-30 [39,] 1.0000000 4.123533e-30 2.061766e-30 [40,] 1.0000000 3.230425e-30 1.615213e-30 [41,] 1.0000000 1.450701e-29 7.253506e-30 [42,] 1.0000000 1.349946e-28 6.749730e-29 [43,] 1.0000000 7.755483e-29 3.877741e-29 [44,] 1.0000000 2.834822e-28 1.417411e-28 [45,] 1.0000000 1.507356e-27 7.536782e-28 [46,] 1.0000000 9.289898e-28 4.644949e-28 [47,] 1.0000000 1.024469e-26 5.122345e-27 [48,] 1.0000000 1.070649e-25 5.353243e-26 [49,] 1.0000000 9.611115e-25 4.805557e-25 [50,] 1.0000000 1.428948e-24 7.144740e-25 [51,] 1.0000000 1.798383e-24 8.991914e-25 [52,] 1.0000000 1.596682e-23 7.983410e-24 [53,] 1.0000000 1.320938e-22 6.604692e-23 [54,] 1.0000000 1.478523e-21 7.392613e-22 [55,] 1.0000000 1.293218e-20 6.466089e-21 [56,] 1.0000000 4.557225e-21 2.278613e-21 [57,] 1.0000000 4.839985e-20 2.419992e-20 [58,] 1.0000000 3.417178e-19 1.708589e-19 [59,] 1.0000000 3.241690e-18 1.620845e-18 [60,] 1.0000000 1.933736e-17 9.668682e-18 [61,] 1.0000000 2.138713e-16 1.069357e-16 [62,] 1.0000000 7.481749e-16 3.740874e-16 [63,] 1.0000000 7.177123e-15 3.588561e-15 [64,] 1.0000000 5.537067e-14 2.768533e-14 [65,] 1.0000000 1.980990e-13 9.904948e-14 [66,] 1.0000000 1.738649e-12 8.693246e-13 [67,] 1.0000000 1.691614e-12 8.458072e-13 [68,] 1.0000000 1.707892e-11 8.539462e-12 [69,] 1.0000000 1.896702e-11 9.483511e-12 [70,] 1.0000000 2.810031e-10 1.405015e-10 [71,] 1.0000000 7.951780e-10 3.975890e-10 [72,] 1.0000000 1.102868e-08 5.514338e-09 [73,] 0.9999999 1.650593e-07 8.252964e-08 [74,] 0.9999989 2.202584e-06 1.101292e-06 [75,] 0.9999920 1.606091e-05 8.030457e-06 [76,] 0.9999911 1.775782e-05 8.878910e-06 [77,] 0.9999348 1.303868e-04 6.519338e-05 > postscript(file="/var/www/rcomp/tmp/1dmkr1293221008.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/2oekc1293221008.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/rcomp/tmp/3oekc1293221008.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/4oekc1293221008.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/5z51f1293221008.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 6 -385354.276 3031951.861 684521.405 -419917.413 -735058.574 -54673.022 7 8 9 10 11 12 876008.912 446465.076 -524497.192 559157.609 366925.180 730410.155 13 14 15 16 17 18 675130.635 -582281.337 -186143.292 545236.147 -256257.720 -292351.579 19 20 21 22 23 24 -502111.672 -58892.047 86204.203 -398109.843 75068.519 -212510.435 25 26 27 28 29 30 -99474.932 228372.697 -579685.791 -228775.340 517314.639 341210.922 31 32 33 34 35 36 192414.104 -160414.226 95629.064 68514.934 -136059.940 -29514.627 37 38 39 40 41 42 255437.451 141881.057 222926.524 -151541.907 -308606.418 -38414.301 43 44 45 46 47 48 86268.023 -84183.615 13486.942 -167216.843 235540.637 -198863.700 49 50 51 52 53 54 -46891.407 -190703.858 83533.225 -22273.982 -142461.650 96411.623 55 56 57 58 59 60 -244021.200 85560.439 166181.431 40755.448 -91572.997 -269978.532 61 62 63 64 65 66 -71027.436 -129211.004 -69636.892 -274781.047 24301.778 96124.721 67 68 69 70 71 72 -173218.164 -38610.124 -107094.288 -92600.863 -226384.569 90369.202 73 74 75 76 77 78 -156511.488 -93148.911 -125835.323 -148983.735 -135281.252 -129326.659 79 80 81 82 83 84 5806.934 16339.721 -23503.635 -95186.842 -155009.175 -25494.132 85 86 87 88 89 90 -59713.673 -200870.123 33247.147 -90999.522 -120915.516 -70390.488 91 92 93 94 95 96 -57736.898 -106346.630 84020.761 -229284.273 61131.895 -107166.188 97 98 99 100 -34094.584 -2288.539 -100156.337 -110239.042 > postscript(file="/var/www/rcomp/tmp/6z51f1293221008.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 -385354.276 NA 1 3031951.861 -385354.276 2 684521.405 3031951.861 3 -419917.413 684521.405 4 -735058.574 -419917.413 5 -54673.022 -735058.574 6 876008.912 -54673.022 7 446465.076 876008.912 8 -524497.192 446465.076 9 559157.609 -524497.192 10 366925.180 559157.609 11 730410.155 366925.180 12 675130.635 730410.155 13 -582281.337 675130.635 14 -186143.292 -582281.337 15 545236.147 -186143.292 16 -256257.720 545236.147 17 -292351.579 -256257.720 18 -502111.672 -292351.579 19 -58892.047 -502111.672 20 86204.203 -58892.047 21 -398109.843 86204.203 22 75068.519 -398109.843 23 -212510.435 75068.519 24 -99474.932 -212510.435 25 228372.697 -99474.932 26 -579685.791 228372.697 27 -228775.340 -579685.791 28 517314.639 -228775.340 29 341210.922 517314.639 30 192414.104 341210.922 31 -160414.226 192414.104 32 95629.064 -160414.226 33 68514.934 95629.064 34 -136059.940 68514.934 35 -29514.627 -136059.940 36 255437.451 -29514.627 37 141881.057 255437.451 38 222926.524 141881.057 39 -151541.907 222926.524 40 -308606.418 -151541.907 41 -38414.301 -308606.418 42 86268.023 -38414.301 43 -84183.615 86268.023 44 13486.942 -84183.615 45 -167216.843 13486.942 46 235540.637 -167216.843 47 -198863.700 235540.637 48 -46891.407 -198863.700 49 -190703.858 -46891.407 50 83533.225 -190703.858 51 -22273.982 83533.225 52 -142461.650 -22273.982 53 96411.623 -142461.650 54 -244021.200 96411.623 55 85560.439 -244021.200 56 166181.431 85560.439 57 40755.448 166181.431 58 -91572.997 40755.448 59 -269978.532 -91572.997 60 -71027.436 -269978.532 61 -129211.004 -71027.436 62 -69636.892 -129211.004 63 -274781.047 -69636.892 64 24301.778 -274781.047 65 96124.721 24301.778 66 -173218.164 96124.721 67 -38610.124 -173218.164 68 -107094.288 -38610.124 69 -92600.863 -107094.288 70 -226384.569 -92600.863 71 90369.202 -226384.569 72 -156511.488 90369.202 73 -93148.911 -156511.488 74 -125835.323 -93148.911 75 -148983.735 -125835.323 76 -135281.252 -148983.735 77 -129326.659 -135281.252 78 5806.934 -129326.659 79 16339.721 5806.934 80 -23503.635 16339.721 81 -95186.842 -23503.635 82 -155009.175 -95186.842 83 -25494.132 -155009.175 84 -59713.673 -25494.132 85 -200870.123 -59713.673 86 33247.147 -200870.123 87 -90999.522 33247.147 88 -120915.516 -90999.522 89 -70390.488 -120915.516 90 -57736.898 -70390.488 91 -106346.630 -57736.898 92 84020.761 -106346.630 93 -229284.273 84020.761 94 61131.895 -229284.273 95 -107166.188 61131.895 96 -34094.584 -107166.188 97 -2288.539 -34094.584 98 -100156.337 -2288.539 99 -110239.042 -100156.337 100 NA -110239.042 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 3031951.861 -385354.276 [2,] 684521.405 3031951.861 [3,] -419917.413 684521.405 [4,] -735058.574 -419917.413 [5,] -54673.022 -735058.574 [6,] 876008.912 -54673.022 [7,] 446465.076 876008.912 [8,] -524497.192 446465.076 [9,] 559157.609 -524497.192 [10,] 366925.180 559157.609 [11,] 730410.155 366925.180 [12,] 675130.635 730410.155 [13,] -582281.337 675130.635 [14,] -186143.292 -582281.337 [15,] 545236.147 -186143.292 [16,] -256257.720 545236.147 [17,] -292351.579 -256257.720 [18,] -502111.672 -292351.579 [19,] -58892.047 -502111.672 [20,] 86204.203 -58892.047 [21,] -398109.843 86204.203 [22,] 75068.519 -398109.843 [23,] -212510.435 75068.519 [24,] -99474.932 -212510.435 [25,] 228372.697 -99474.932 [26,] -579685.791 228372.697 [27,] -228775.340 -579685.791 [28,] 517314.639 -228775.340 [29,] 341210.922 517314.639 [30,] 192414.104 341210.922 [31,] -160414.226 192414.104 [32,] 95629.064 -160414.226 [33,] 68514.934 95629.064 [34,] -136059.940 68514.934 [35,] -29514.627 -136059.940 [36,] 255437.451 -29514.627 [37,] 141881.057 255437.451 [38,] 222926.524 141881.057 [39,] -151541.907 222926.524 [40,] -308606.418 -151541.907 [41,] -38414.301 -308606.418 [42,] 86268.023 -38414.301 [43,] -84183.615 86268.023 [44,] 13486.942 -84183.615 [45,] -167216.843 13486.942 [46,] 235540.637 -167216.843 [47,] -198863.700 235540.637 [48,] -46891.407 -198863.700 [49,] -190703.858 -46891.407 [50,] 83533.225 -190703.858 [51,] -22273.982 83533.225 [52,] -142461.650 -22273.982 [53,] 96411.623 -142461.650 [54,] -244021.200 96411.623 [55,] 85560.439 -244021.200 [56,] 166181.431 85560.439 [57,] 40755.448 166181.431 [58,] -91572.997 40755.448 [59,] -269978.532 -91572.997 [60,] -71027.436 -269978.532 [61,] -129211.004 -71027.436 [62,] -69636.892 -129211.004 [63,] -274781.047 -69636.892 [64,] 24301.778 -274781.047 [65,] 96124.721 24301.778 [66,] -173218.164 96124.721 [67,] -38610.124 -173218.164 [68,] -107094.288 -38610.124 [69,] -92600.863 -107094.288 [70,] -226384.569 -92600.863 [71,] 90369.202 -226384.569 [72,] -156511.488 90369.202 [73,] -93148.911 -156511.488 [74,] -125835.323 -93148.911 [75,] -148983.735 -125835.323 [76,] -135281.252 -148983.735 [77,] -129326.659 -135281.252 [78,] 5806.934 -129326.659 [79,] 16339.721 5806.934 [80,] -23503.635 16339.721 [81,] -95186.842 -23503.635 [82,] -155009.175 -95186.842 [83,] -25494.132 -155009.175 [84,] -59713.673 -25494.132 [85,] -200870.123 -59713.673 [86,] 33247.147 -200870.123 [87,] -90999.522 33247.147 [88,] -120915.516 -90999.522 [89,] -70390.488 -120915.516 [90,] -57736.898 -70390.488 [91,] -106346.630 -57736.898 [92,] 84020.761 -106346.630 [93,] -229284.273 84020.761 [94,] 61131.895 -229284.273 [95,] -107166.188 61131.895 [96,] -34094.584 -107166.188 [97,] -2288.539 -34094.584 [98,] -100156.337 -2288.539 [99,] -110239.042 -100156.337 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 3031951.861 -385354.276 2 684521.405 3031951.861 3 -419917.413 684521.405 4 -735058.574 -419917.413 5 -54673.022 -735058.574 6 876008.912 -54673.022 7 446465.076 876008.912 8 -524497.192 446465.076 9 559157.609 -524497.192 10 366925.180 559157.609 11 730410.155 366925.180 12 675130.635 730410.155 13 -582281.337 675130.635 14 -186143.292 -582281.337 15 545236.147 -186143.292 16 -256257.720 545236.147 17 -292351.579 -256257.720 18 -502111.672 -292351.579 19 -58892.047 -502111.672 20 86204.203 -58892.047 21 -398109.843 86204.203 22 75068.519 -398109.843 23 -212510.435 75068.519 24 -99474.932 -212510.435 25 228372.697 -99474.932 26 -579685.791 228372.697 27 -228775.340 -579685.791 28 517314.639 -228775.340 29 341210.922 517314.639 30 192414.104 341210.922 31 -160414.226 192414.104 32 95629.064 -160414.226 33 68514.934 95629.064 34 -136059.940 68514.934 35 -29514.627 -136059.940 36 255437.451 -29514.627 37 141881.057 255437.451 38 222926.524 141881.057 39 -151541.907 222926.524 40 -308606.418 -151541.907 41 -38414.301 -308606.418 42 86268.023 -38414.301 43 -84183.615 86268.023 44 13486.942 -84183.615 45 -167216.843 13486.942 46 235540.637 -167216.843 47 -198863.700 235540.637 48 -46891.407 -198863.700 49 -190703.858 -46891.407 50 83533.225 -190703.858 51 -22273.982 83533.225 52 -142461.650 -22273.982 53 96411.623 -142461.650 54 -244021.200 96411.623 55 85560.439 -244021.200 56 166181.431 85560.439 57 40755.448 166181.431 58 -91572.997 40755.448 59 -269978.532 -91572.997 60 -71027.436 -269978.532 61 -129211.004 -71027.436 62 -69636.892 -129211.004 63 -274781.047 -69636.892 64 24301.778 -274781.047 65 96124.721 24301.778 66 -173218.164 96124.721 67 -38610.124 -173218.164 68 -107094.288 -38610.124 69 -92600.863 -107094.288 70 -226384.569 -92600.863 71 90369.202 -226384.569 72 -156511.488 90369.202 73 -93148.911 -156511.488 74 -125835.323 -93148.911 75 -148983.735 -125835.323 76 -135281.252 -148983.735 77 -129326.659 -135281.252 78 5806.934 -129326.659 79 16339.721 5806.934 80 -23503.635 16339.721 81 -95186.842 -23503.635 82 -155009.175 -95186.842 83 -25494.132 -155009.175 84 -59713.673 -25494.132 85 -200870.123 -59713.673 86 33247.147 -200870.123 87 -90999.522 33247.147 88 -120915.516 -90999.522 89 -70390.488 -120915.516 90 -57736.898 -70390.488 91 -106346.630 -57736.898 92 84020.761 -106346.630 93 -229284.273 84020.761 94 61131.895 -229284.273 95 -107166.188 61131.895 96 -34094.584 -107166.188 97 -2288.539 -34094.584 98 -100156.337 -2288.539 99 -110239.042 -100156.337 > 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/rcomp/tmp/79eih1293221008.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/89eih1293221008.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/9k5zk1293221008.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/rcomp/tmp/10k5zk1293221008.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/rcomp/tmp/11n6g81293221008.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/rcomp/tmp/1296ew1293221008.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/rcomp/tmp/13yquq1293221008.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/rcomp/tmp/148zbb1293221008.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/rcomp/tmp/15uzrh1293221008.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/rcomp/tmp/1689pp1293221008.tab") + } > > try(system("convert tmp/1dmkr1293221008.ps tmp/1dmkr1293221008.png",intern=TRUE)) character(0) > try(system("convert tmp/2oekc1293221008.ps tmp/2oekc1293221008.png",intern=TRUE)) character(0) > try(system("convert tmp/3oekc1293221008.ps tmp/3oekc1293221008.png",intern=TRUE)) character(0) > try(system("convert tmp/4oekc1293221008.ps tmp/4oekc1293221008.png",intern=TRUE)) character(0) > try(system("convert tmp/5z51f1293221008.ps tmp/5z51f1293221008.png",intern=TRUE)) character(0) > try(system("convert tmp/6z51f1293221008.ps tmp/6z51f1293221008.png",intern=TRUE)) character(0) > try(system("convert tmp/79eih1293221008.ps tmp/79eih1293221008.png",intern=TRUE)) character(0) > try(system("convert tmp/89eih1293221008.ps tmp/89eih1293221008.png",intern=TRUE)) character(0) > try(system("convert tmp/9k5zk1293221008.ps tmp/9k5zk1293221008.png",intern=TRUE)) character(0) > try(system("convert tmp/10k5zk1293221008.ps tmp/10k5zk1293221008.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.770 0.810 4.618