R version 2.12.0 (2010-10-15) Copyright (C) 2010 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i486-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- array(list(119830 + ,64507 + ,21673 + ,206010 + ,116068 + ,61865 + ,20179 + ,198112 + ,114976 + ,60844 + ,18699 + ,194519 + ,110296 + ,57604 + ,17805 + ,185705 + ,107832 + ,55672 + ,16669 + ,180173 + ,105624 + ,53636 + ,16882 + ,176142 + ,114858 + ,63487 + ,25056 + ,203401 + ,119598 + ,71468 + ,30836 + ,221902 + ,106675 + ,63200 + ,27503 + ,197378 + ,103315 + ,58166 + ,23520 + ,185001 + ,100826 + ,54664 + ,20866 + ,176356 + ,103574 + ,55860 + ,21015 + ,180449 + ,104708 + ,56190 + ,19246 + ,180144 + ,101817 + ,54300 + ,17549 + ,173666 + ,97898 + ,51362 + ,16428 + ,165688 + ,95559 + ,49802 + ,16209 + ,161570 + ,92822 + ,48088 + ,15235 + ,156145 + ,90848 + ,46696 + ,16186 + ,153730 + ,101141 + ,56586 + ,24971 + ,182698 + ,105841 + ,64148 + ,30776 + ,200765 + ,93647 + ,56449 + ,26416 + ,176512 + ,90923 + ,52538 + ,23157 + ,166618 + ,89130 + ,49359 + ,20155 + ,158644 + ,90212 + ,49583 + ,19790 + ,159585 + ,93196 + ,51050 + ,18849 + ,163095 + ,91861 + ,49610 + 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,89253 + ,56274 + ,27949 + ,173476 + ,87794 + ,54045 + ,24605 + ,166444 + ,89810 + ,55792 + ,25695 + ,171297 + ,90864 + ,55499 + ,23338 + ,169701 + ,89025 + ,53216 + ,21941 + ,164182 + ,87621 + ,52259 + ,22034 + ,161914 + ,87718 + ,51257 + ,20637 + ,159612 + ,83433 + ,48150 + ,19418 + ,151001 + ,84535 + ,51125 + ,22454 + ,158114 + ,92223 + ,61046 + ,33261 + ,186530 + ,91052 + ,61022 + ,34995 + ,187069 + ,88456 + ,56742 + ,29132 + ,174330 + ,88706 + ,54485 + ,26171 + ,169362 + ,89137 + ,53862 + ,23828 + ,166827 + ,94066 + ,58228 + ,25743 + ,178037 + ,99258 + ,61951 + ,25204 + ,186413 + ,100673 + ,62874 + ,25679 + ,189226 + ,102269 + ,64013 + ,25281 + ,191563 + ,100833 + ,62937 + ,25136 + ,188906 + ,99314 + ,61897 + ,24794 + ,186005 + ,101764 + ,65267 + ,28278 + ,195309 + ,108242 + ,75228 + ,40062 + ,223532 + ,108148 + ,76161 + ,42590 + ,226899 + ,104761 + ,71480 + ,37885 + ,214126 + ,103772 + ,69070 + ,34061 + ,206903 + ,103737 + ,68293 + ,32412 + ,204442 + ,111043 + ,74685 + ,34647 + ,220375 + ,109906 + ,72664 + ,31750 + ,214320 + ,109335 + ,71965 + ,31288 + ,212588 + ,107247 + ,69238 + ,29331 + ,205816 + ,105690 + ,67738 + ,28768 + ,202196 + ,102755 + ,65187 + ,27780 + ,195722 + ,102280 + ,66170 + ,30113 + ,198563 + ,110590 + ,77309 + ,41240 + ,229139 + ,109122 + ,77134 + ,43271 + ,229527 + ,102803 + ,70957 + ,38108 + ,211868 + ,101424 + ,67749 + ,34382 + ,203555 + ,99138 + ,65081 + ,31551 + ,195770 + ,101284 + ,66600 + ,31950 + ,199834 + ,104260 + ,68384 + ,30445 + ,203089 + ,102526 + ,66677 + ,29277 + ,198480 + ,100001 + ,64507 + ,28176 + ,192684 + ,97562 + ,62526 + ,27739 + ,187827 + ,95539 + ,60570 + ,26305 + ,182414 + ,93831 + ,60663 + ,28016 + ,182510 + ,101031 + ,72923 + ,37570 + ,211524 + ,98744 + ,72952 + ,39755 + ,211451 + ,95847 + ,68503 + ,35790 + ,200140 + ,94278 + ,65289 + ,32001 + ,191568) + ,dim=c(4 + ,154) + ,dimnames=list(c('X1' + ,'X2' + ,'X3' + ,'Totaal') + ,1:154)) > y <- array(NA,dim=c(4,154),dimnames=list(c('X1','X2','X3','Totaal'),1:154)) > 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 = '4' > #'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 Totaal X1 X2 X3 1 206010 119830 64507 21673 2 198112 116068 61865 20179 3 194519 114976 60844 18699 4 185705 110296 57604 17805 5 180173 107832 55672 16669 6 176142 105624 53636 16882 7 203401 114858 63487 25056 8 221902 119598 71468 30836 9 197378 106675 63200 27503 10 185001 103315 58166 23520 11 176356 100826 54664 20866 12 180449 103574 55860 21015 13 180144 104708 56190 19246 14 173666 101817 54300 17549 15 165688 97898 51362 16428 16 161570 95559 49802 16209 17 156145 92822 48088 15235 18 153730 90848 46696 16186 19 182698 101141 56586 24971 20 200765 105841 64148 30776 21 176512 93647 56449 26416 22 166618 90923 52538 23157 23 158644 89130 49359 20155 24 159585 90212 49583 19790 25 163095 93196 51050 18849 26 159044 91861 49610 17573 27 155511 90593 48321 16597 28 153745 89895 47692 16158 29 150569 88819 46243 15507 30 150605 87924 46248 16433 31 179612 96906 56381 26325 32 194690 101217 62329 31144 33 189917 98709 60673 30535 34 184128 98139 58393 27596 35 175335 95529 55742 24064 36 179566 98577 57135 23854 37 181140 100772 57961 22407 38 177876 100180 56571 21125 39 175041 99200 55615 20226 40 169292 96251 53494 19547 41 166070 94514 52623 18933 42 166972 93780 52820 20372 43 206348 105192 66825 34331 44 215706 107682 70695 37329 45 202108 99687 65660 36761 46 195411 99436 63238 32737 47 193111 102049 61741 29321 48 195198 102673 63642 28883 49 198770 105813 65521 27436 50 194163 105056 64006 25101 51 190420 103916 62728 23776 52 189733 103513 62438 23782 53 186029 101893 61109 23027 54 191531 102503 63422 25606 55 232571 113149 78094 41328 56 243477 116696 82030 44751 57 227247 108500 75892 42855 58 217859 107800 72431 37628 59 208679 105941 69194 33544 60 213188 108742 71171 33275 61 216234 111680 72545 32009 62 213586 111270 71503 30813 63 209465 110698 69624 29143 64 204045 108517 67407 28121 65 200237 107127 66103 27007 66 203666 107088 67466 29112 67 241476 116321 81088 44067 68 260307 125045 86781 48481 69 243324 116779 79964 46581 70 244460 122887 80407 41166 71 233575 120162 76589 36824 72 237217 123198 78083 35936 73 235243 123610 78000 33633 74 230354 122293 76431 31630 75 227184 121289 75461 30434 76 221678 119393 73739 28546 77 217142 117494 71988 27660 78 219452 116693 72929 29830 79 256446 125062 85785 45599 80 265845 127281 89261 49303 81 248624 120195 84012 44417 82 241114 119804 80924 40386 83 229245 117113 76588 35544 84 231805 119240 77546 35019 85 219277 115823 73054 30400 86 219313 116281 73430 29602 87 212610 113816 71093 27701 88 214771 114632 72202 27937 89 211142 112987 70872 27283 90 211457 111633 70452 29372 91 240048 116721 80506 42821 92 240636 114850 80400 45386 93 230580 112797 77613 40170 94 208795 105368 69056 34371 95 197922 102524 65321 30077 96 194596 101327 64018 29251 97 194581 102612 64767 27202 98 185686 98873 61099 25714 99 178106 95993 58329 23784 100 172608 93244 56396 22968 101 167302 90403 54656 22243 102 168053 88539 55259 24255 103 202300 98106 66912 37282 104 202388 96963 66631 38794 105 182516 90781 59907 31828 106 173476 89253 56274 27949 107 166444 87794 54045 24605 108 171297 89810 55792 25695 109 169701 90864 55499 23338 110 164182 89025 53216 21941 111 161914 87621 52259 22034 112 159612 87718 51257 20637 113 151001 83433 48150 19418 114 158114 84535 51125 22454 115 186530 92223 61046 33261 116 187069 91052 61022 34995 117 174330 88456 56742 29132 118 169362 88706 54485 26171 119 166827 89137 53862 23828 120 178037 94066 58228 25743 121 186413 99258 61951 25204 122 189226 100673 62874 25679 123 191563 102269 64013 25281 124 188906 100833 62937 25136 125 186005 99314 61897 24794 126 195309 101764 65267 28278 127 223532 108242 75228 40062 128 226899 108148 76161 42590 129 214126 104761 71480 37885 130 206903 103772 69070 34061 131 204442 103737 68293 32412 132 220375 111043 74685 34647 133 214320 109906 72664 31750 134 212588 109335 71965 31288 135 205816 107247 69238 29331 136 202196 105690 67738 28768 137 195722 102755 65187 27780 138 198563 102280 66170 30113 139 229139 110590 77309 41240 140 229527 109122 77134 43271 141 211868 102803 70957 38108 142 203555 101424 67749 34382 143 195770 99138 65081 31551 144 199834 101284 66600 31950 145 203089 104260 68384 30445 146 198480 102526 66677 29277 147 192684 100001 64507 28176 148 187827 97562 62526 27739 149 182414 95539 60570 26305 150 182510 93831 60663 28016 151 211524 101031 72923 37570 152 211451 98744 72952 39755 153 200140 95847 68503 35790 154 191568 94278 65289 32001 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) X1 X2 X3 1.689e-11 1.000e+00 1.000e+00 1.000e+00 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -1.115e-10 -1.033e-12 1.150e-13 2.181e-12 8.885e-12 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.689e-11 8.727e-12 1.936e+00 0.0548 . X1 1.000e+00 2.303e-16 4.342e+15 <2e-16 *** X2 1.000e+00 4.381e-16 2.283e+15 <2e-16 *** X3 1.000e+00 3.469e-16 2.883e+15 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 9.582e-12 on 150 degrees of freedom Multiple R-squared: 1, Adjusted R-squared: 1 F-statistic: 3.607e+32 on 3 and 150 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,] 4.680166e-02 9.360331e-02 9.531983e-01 [2,] 9.996737e-01 6.525622e-04 3.262811e-04 [3,] 9.046366e-02 1.809273e-01 9.095363e-01 [4,] 5.306363e-05 1.061273e-04 9.999469e-01 [5,] 3.628464e-02 7.256927e-02 9.637154e-01 [6,] 5.162675e-01 9.674651e-01 4.837325e-01 [7,] 7.112912e-01 5.774177e-01 2.887088e-01 [8,] 1.000000e+00 1.765312e-10 8.826562e-11 [9,] 4.565540e-08 9.131080e-08 1.000000e+00 [10,] 2.871221e-04 5.742441e-04 9.997129e-01 [11,] 1.466388e-01 2.932776e-01 8.533612e-01 [12,] 2.145378e-05 4.290755e-05 9.999785e-01 [13,] 3.769478e-06 7.538957e-06 9.999962e-01 [14,] 2.498512e-07 4.997024e-07 9.999998e-01 [15,] 5.178752e-07 1.035750e-06 9.999995e-01 [16,] 6.502268e-07 1.300454e-06 9.999993e-01 [17,] 1.071026e-05 2.142053e-05 9.999893e-01 [18,] 8.608838e-01 2.782324e-01 1.391162e-01 [19,] 5.823922e-01 8.352156e-01 4.176078e-01 [20,] 2.424421e-08 4.848842e-08 1.000000e+00 [21,] 1.895739e-09 3.791478e-09 1.000000e+00 [22,] 1.365084e-11 2.730168e-11 1.000000e+00 [23,] 4.562804e-05 9.125609e-05 9.999544e-01 [24,] 9.319438e-01 1.361124e-01 6.805620e-02 [25,] 1.000000e+00 1.776973e-12 8.884867e-13 [26,] 1.546926e-01 3.093853e-01 8.453074e-01 [27,] 2.075059e-08 4.150119e-08 1.000000e+00 [28,] 4.946519e-01 9.893038e-01 5.053481e-01 [29,] 9.999994e-01 1.190409e-06 5.952045e-07 [30,] 4.707595e-02 9.415189e-02 9.529241e-01 [31,] 7.414528e-01 5.170944e-01 2.585472e-01 [32,] 2.011061e-03 4.022122e-03 9.979889e-01 [33,] 1.664569e-16 3.329138e-16 1.000000e+00 [34,] 7.461248e-45 1.492250e-44 1.000000e+00 [35,] 2.688924e-14 5.377847e-14 1.000000e+00 [36,] 4.924003e-22 9.848006e-22 1.000000e+00 [37,] 7.054223e-01 5.891554e-01 2.945777e-01 [38,] 1.161333e-03 2.322666e-03 9.988387e-01 [39,] 8.821019e-01 2.357961e-01 1.178981e-01 [40,] 9.682139e-09 1.936428e-08 1.000000e+00 [41,] 4.146185e-23 8.292370e-23 1.000000e+00 [42,] 1.761174e-20 3.522348e-20 1.000000e+00 [43,] 2.059808e-23 4.119616e-23 1.000000e+00 [44,] 9.717332e-01 5.653368e-02 2.826684e-02 [45,] 1.000000e+00 5.703471e-14 2.851735e-14 [46,] 3.220131e-13 6.440262e-13 1.000000e+00 [47,] 9.385556e-01 1.228887e-01 6.144437e-02 [48,] 1.000000e+00 4.990292e-32 2.495146e-32 [49,] 9.999055e-01 1.890190e-04 9.450949e-05 [50,] 9.999925e-01 1.498816e-05 7.494081e-06 [51,] 9.733059e-02 1.946612e-01 9.026694e-01 [52,] 6.129504e-37 1.225901e-36 1.000000e+00 [53,] 3.099328e-12 6.198655e-12 1.000000e+00 [54,] 1.491262e-01 2.982525e-01 8.508738e-01 [55,] 9.667317e-01 6.653650e-02 3.326825e-02 [56,] 9.416626e-06 1.883325e-05 9.999906e-01 [57,] 1.829278e-22 3.658556e-22 1.000000e+00 [58,] 9.848242e-07 1.969648e-06 9.999990e-01 [59,] 6.526976e-08 1.305395e-07 9.999999e-01 [60,] 1.000000e+00 1.558593e-15 7.792967e-16 [61,] 1.000000e+00 4.132209e-11 2.066104e-11 [62,] 1.000000e+00 1.833645e-48 9.168226e-49 [63,] 4.308283e-08 8.616566e-08 1.000000e+00 [64,] 5.130597e-33 1.026119e-32 1.000000e+00 [65,] 7.676499e-01 4.647001e-01 2.323501e-01 [66,] 9.999230e-01 1.539855e-04 7.699275e-05 [67,] 5.100408e-02 1.020082e-01 9.489959e-01 [68,] 2.189985e-27 4.379970e-27 1.000000e+00 [69,] 1.036063e-04 2.072127e-04 9.998964e-01 [70,] 9.339432e-01 1.321135e-01 6.605676e-02 [71,] 7.587720e-03 1.517544e-02 9.924123e-01 [72,] 3.318861e-07 6.637721e-07 9.999997e-01 [73,] 4.202364e-07 8.404729e-07 9.999996e-01 [74,] 9.938224e-01 1.235530e-02 6.177649e-03 [75,] 9.999985e-01 3.098397e-06 1.549198e-06 [76,] 2.491738e-01 4.983477e-01 7.508262e-01 [77,] 1.000000e+00 6.690335e-17 3.345168e-17 [78,] 8.074713e-01 3.850573e-01 1.925287e-01 [79,] 1.000000e+00 2.268580e-28 1.134290e-28 [80,] 9.999785e-01 4.292658e-05 2.146329e-05 [81,] 2.189482e-26 4.378965e-26 1.000000e+00 [82,] 1.000000e+00 1.508611e-10 7.543053e-11 [83,] 4.548627e-05 9.097254e-05 9.999545e-01 [84,] 1.157451e-24 2.314901e-24 1.000000e+00 [85,] 6.655882e-13 1.331176e-12 1.000000e+00 [86,] 2.757461e-04 5.514921e-04 9.997243e-01 [87,] 1.418935e-43 2.837870e-43 1.000000e+00 [88,] 1.904426e-16 3.808852e-16 1.000000e+00 [89,] 6.472508e-04 1.294502e-03 9.993527e-01 [90,] 1.000000e+00 9.729626e-13 4.864813e-13 [91,] 1.000000e+00 8.691562e-33 4.345781e-33 [92,] 1.000000e+00 5.502304e-14 2.751152e-14 [93,] 1.000000e+00 4.597237e-18 2.298618e-18 [94,] 3.375904e-09 6.751808e-09 1.000000e+00 [95,] 1.000000e+00 4.888279e-10 2.444139e-10 [96,] 1.443957e-14 2.887914e-14 1.000000e+00 [97,] 8.399625e-06 1.679925e-05 9.999916e-01 [98,] 4.018650e-68 8.037300e-68 1.000000e+00 [99,] 1.000000e+00 4.268877e-27 2.134439e-27 [100,] 1.000000e+00 4.633206e-08 2.316603e-08 [101,] 8.775315e-01 2.449369e-01 1.224685e-01 [102,] 6.322326e-55 1.264465e-54 1.000000e+00 [103,] 3.733244e-15 7.466487e-15 1.000000e+00 [104,] 1.000000e+00 4.161288e-08 2.080644e-08 [105,] 1.000000e+00 9.599067e-09 4.799534e-09 [106,] 2.381838e-22 4.763676e-22 1.000000e+00 [107,] 1.167356e-43 2.334711e-43 1.000000e+00 [108,] 2.409452e-15 4.818904e-15 1.000000e+00 [109,] 9.999999e-01 2.567847e-07 1.283923e-07 [110,] 4.204069e-01 8.408138e-01 5.795931e-01 [111,] 9.878764e-01 2.424718e-02 1.212359e-02 [112,] 1.109600e-19 2.219201e-19 1.000000e+00 [113,] 9.984215e-01 3.156971e-03 1.578486e-03 [114,] 2.762991e-61 5.525982e-61 1.000000e+00 [115,] 9.907231e-01 1.855382e-02 9.276909e-03 [116,] 2.611114e-67 5.222229e-67 1.000000e+00 [117,] 9.999994e-01 1.163932e-06 5.819660e-07 [118,] 1.682051e-15 3.364103e-15 1.000000e+00 [119,] 5.512859e-20 1.102572e-19 1.000000e+00 [120,] 9.988988e-01 2.202379e-03 1.101189e-03 [121,] 5.889027e-01 8.221946e-01 4.110973e-01 [122,] 8.916794e-07 1.783359e-06 9.999991e-01 [123,] 1.000000e+00 1.551612e-24 7.758058e-25 [124,] 2.783672e-01 5.567345e-01 7.216328e-01 [125,] 9.999999e-01 1.356751e-07 6.783753e-08 [126,] 1.000000e+00 2.862592e-08 1.431296e-08 [127,] 1.083526e-01 2.167051e-01 8.916474e-01 [128,] 4.918149e-02 9.836297e-02 9.508185e-01 [129,] 1.270803e-02 2.541607e-02 9.872920e-01 [130,] 1.425291e-20 2.850581e-20 1.000000e+00 [131,] 9.706392e-43 1.941278e-42 1.000000e+00 [132,] 6.857755e-01 6.284489e-01 3.142245e-01 [133,] 1.207064e-03 2.414127e-03 9.987929e-01 [134,] 4.256088e-05 8.512177e-05 9.999574e-01 [135,] 9.992759e-01 1.448262e-03 7.241309e-04 [136,] 4.972081e-02 9.944162e-02 9.502792e-01 [137,] 2.767085e-21 5.534171e-21 1.000000e+00 [138,] 3.767077e-01 7.534155e-01 6.232923e-01 [139,] 9.983611e-01 3.277891e-03 1.638946e-03 [140,] 5.955022e-01 8.089956e-01 4.044978e-01 [141,] 9.992978e-01 1.404360e-03 7.021799e-04 > postscript(file="/var/www/rcomp/tmp/1fke51321807634.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/2w7c21321807634.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3j7qo1321807634.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/4bt4y1321807634.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/5hs671321807634.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 = 154 Frequency = 1 1 2 3 4 5 -1.114563e-10 8.578828e-12 7.148461e-12 7.133197e-13 6.523008e-12 6 7 8 9 10 7.494735e-12 6.266874e-12 8.884864e-12 2.967950e-12 3.605763e-12 11 12 13 14 15 6.945342e-12 6.190240e-12 6.226970e-12 6.052846e-12 3.776151e-12 16 17 18 19 20 2.619264e-12 1.808018e-13 -1.202467e-13 2.343650e-12 2.329332e-12 21 22 23 24 25 2.049147e-12 1.510903e-12 -2.469125e-12 -7.843616e-13 1.867909e-14 26 27 28 29 30 -5.582273e-13 -2.742854e-13 -2.715165e-13 -2.920327e-13 -1.825872e-14 31 32 33 34 35 3.875784e-12 1.798389e-12 5.377623e-13 2.481486e-13 3.937285e-12 36 37 38 39 40 2.481810e-12 3.054932e-12 2.410425e-12 3.975902e-12 2.648459e-12 41 42 43 44 45 2.203847e-12 4.183074e-12 2.045821e-12 1.967316e-12 -2.093866e-12 46 47 48 49 50 -2.546414e-13 1.680581e-12 1.573384e-13 5.486040e-13 3.197545e-15 51 52 53 54 55 3.272580e-13 1.272370e-12 1.312184e-12 -2.917071e-13 -3.896203e-13 56 57 58 59 60 -1.559063e-13 -7.655288e-13 -1.882954e-13 8.752608e-13 5.232823e-13 61 62 63 64 65 1.567607e-12 3.713847e-13 7.382354e-13 9.922841e-13 9.784806e-13 66 67 68 69 70 3.993509e-13 -3.329261e-13 4.305942e-12 -8.056304e-13 9.005498e-13 71 72 73 74 75 2.341314e-12 4.816493e-12 3.537183e-12 5.266877e-12 7.040296e-12 76 77 78 79 80 4.483447e-12 5.457145e-12 4.186983e-12 -3.639968e-15 7.699384e-13 81 82 83 84 85 -8.209154e-13 -1.415559e-12 4.469327e-12 1.916649e-12 4.697145e-12 86 87 88 89 90 4.405809e-12 1.398174e-12 4.193191e-12 7.186671e-14 4.976723e-14 91 92 93 94 95 -1.088612e-12 1.624096e-12 -3.937772e-12 9.648071e-13 -5.817341e-13 96 97 98 99 100 -5.693372e-13 -6.514325e-13 -1.994303e-13 1.729054e-12 1.783412e-12 101 102 103 104 105 5.487048e-13 1.940554e-14 -1.738315e-12 -4.361407e-12 -1.021642e-12 106 107 108 109 110 3.191361e-14 -1.262377e-12 1.410086e-12 -3.856353e-13 -4.104460e-12 111 112 113 114 115 -3.279596e-12 -3.416984e-12 -3.976797e-12 -5.665331e-12 -1.412070e-12 116 117 118 119 120 -1.133726e-12 -2.353165e-14 4.039752e-14 2.501982e-13 1.120220e-12 121 122 123 124 125 -1.772810e-12 -1.339212e-12 -1.036857e-12 -1.102125e-12 -6.288718e-13 126 127 128 129 130 -1.301884e-12 1.239201e-12 -1.578248e-12 2.111578e-12 -5.462170e-13 131 132 133 134 135 -2.769776e-13 7.566191e-15 1.799939e-13 -9.992997e-13 -1.462507e-12 136 137 138 139 140 -5.880915e-13 -1.217251e-12 -1.312101e-12 2.661359e-12 -1.984175e-12 141 142 143 144 145 -2.184381e-12 -6.178345e-13 -2.894103e-12 -2.186026e-12 -1.440561e-12 146 147 148 149 150 -1.541195e-12 -2.607644e-12 -1.882488e-12 -1.240302e-12 -1.330273e-12 151 152 153 154 -4.169426e-12 -9.154652e-12 -4.787662e-12 -3.817614e-12 > postscript(file="/var/www/rcomp/tmp/6d0m51321807634.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 = 154 Frequency = 1 lag(myerror, k = 1) myerror 0 -1.114563e-10 NA 1 8.578828e-12 -1.114563e-10 2 7.148461e-12 8.578828e-12 3 7.133197e-13 7.148461e-12 4 6.523008e-12 7.133197e-13 5 7.494735e-12 6.523008e-12 6 6.266874e-12 7.494735e-12 7 8.884864e-12 6.266874e-12 8 2.967950e-12 8.884864e-12 9 3.605763e-12 2.967950e-12 10 6.945342e-12 3.605763e-12 11 6.190240e-12 6.945342e-12 12 6.226970e-12 6.190240e-12 13 6.052846e-12 6.226970e-12 14 3.776151e-12 6.052846e-12 15 2.619264e-12 3.776151e-12 16 1.808018e-13 2.619264e-12 17 -1.202467e-13 1.808018e-13 18 2.343650e-12 -1.202467e-13 19 2.329332e-12 2.343650e-12 20 2.049147e-12 2.329332e-12 21 1.510903e-12 2.049147e-12 22 -2.469125e-12 1.510903e-12 23 -7.843616e-13 -2.469125e-12 24 1.867909e-14 -7.843616e-13 25 -5.582273e-13 1.867909e-14 26 -2.742854e-13 -5.582273e-13 27 -2.715165e-13 -2.742854e-13 28 -2.920327e-13 -2.715165e-13 29 -1.825872e-14 -2.920327e-13 30 3.875784e-12 -1.825872e-14 31 1.798389e-12 3.875784e-12 32 5.377623e-13 1.798389e-12 33 2.481486e-13 5.377623e-13 34 3.937285e-12 2.481486e-13 35 2.481810e-12 3.937285e-12 36 3.054932e-12 2.481810e-12 37 2.410425e-12 3.054932e-12 38 3.975902e-12 2.410425e-12 39 2.648459e-12 3.975902e-12 40 2.203847e-12 2.648459e-12 41 4.183074e-12 2.203847e-12 42 2.045821e-12 4.183074e-12 43 1.967316e-12 2.045821e-12 44 -2.093866e-12 1.967316e-12 45 -2.546414e-13 -2.093866e-12 46 1.680581e-12 -2.546414e-13 47 1.573384e-13 1.680581e-12 48 5.486040e-13 1.573384e-13 49 3.197545e-15 5.486040e-13 50 3.272580e-13 3.197545e-15 51 1.272370e-12 3.272580e-13 52 1.312184e-12 1.272370e-12 53 -2.917071e-13 1.312184e-12 54 -3.896203e-13 -2.917071e-13 55 -1.559063e-13 -3.896203e-13 56 -7.655288e-13 -1.559063e-13 57 -1.882954e-13 -7.655288e-13 58 8.752608e-13 -1.882954e-13 59 5.232823e-13 8.752608e-13 60 1.567607e-12 5.232823e-13 61 3.713847e-13 1.567607e-12 62 7.382354e-13 3.713847e-13 63 9.922841e-13 7.382354e-13 64 9.784806e-13 9.922841e-13 65 3.993509e-13 9.784806e-13 66 -3.329261e-13 3.993509e-13 67 4.305942e-12 -3.329261e-13 68 -8.056304e-13 4.305942e-12 69 9.005498e-13 -8.056304e-13 70 2.341314e-12 9.005498e-13 71 4.816493e-12 2.341314e-12 72 3.537183e-12 4.816493e-12 73 5.266877e-12 3.537183e-12 74 7.040296e-12 5.266877e-12 75 4.483447e-12 7.040296e-12 76 5.457145e-12 4.483447e-12 77 4.186983e-12 5.457145e-12 78 -3.639968e-15 4.186983e-12 79 7.699384e-13 -3.639968e-15 80 -8.209154e-13 7.699384e-13 81 -1.415559e-12 -8.209154e-13 82 4.469327e-12 -1.415559e-12 83 1.916649e-12 4.469327e-12 84 4.697145e-12 1.916649e-12 85 4.405809e-12 4.697145e-12 86 1.398174e-12 4.405809e-12 87 4.193191e-12 1.398174e-12 88 7.186671e-14 4.193191e-12 89 4.976723e-14 7.186671e-14 90 -1.088612e-12 4.976723e-14 91 1.624096e-12 -1.088612e-12 92 -3.937772e-12 1.624096e-12 93 9.648071e-13 -3.937772e-12 94 -5.817341e-13 9.648071e-13 95 -5.693372e-13 -5.817341e-13 96 -6.514325e-13 -5.693372e-13 97 -1.994303e-13 -6.514325e-13 98 1.729054e-12 -1.994303e-13 99 1.783412e-12 1.729054e-12 100 5.487048e-13 1.783412e-12 101 1.940554e-14 5.487048e-13 102 -1.738315e-12 1.940554e-14 103 -4.361407e-12 -1.738315e-12 104 -1.021642e-12 -4.361407e-12 105 3.191361e-14 -1.021642e-12 106 -1.262377e-12 3.191361e-14 107 1.410086e-12 -1.262377e-12 108 -3.856353e-13 1.410086e-12 109 -4.104460e-12 -3.856353e-13 110 -3.279596e-12 -4.104460e-12 111 -3.416984e-12 -3.279596e-12 112 -3.976797e-12 -3.416984e-12 113 -5.665331e-12 -3.976797e-12 114 -1.412070e-12 -5.665331e-12 115 -1.133726e-12 -1.412070e-12 116 -2.353165e-14 -1.133726e-12 117 4.039752e-14 -2.353165e-14 118 2.501982e-13 4.039752e-14 119 1.120220e-12 2.501982e-13 120 -1.772810e-12 1.120220e-12 121 -1.339212e-12 -1.772810e-12 122 -1.036857e-12 -1.339212e-12 123 -1.102125e-12 -1.036857e-12 124 -6.288718e-13 -1.102125e-12 125 -1.301884e-12 -6.288718e-13 126 1.239201e-12 -1.301884e-12 127 -1.578248e-12 1.239201e-12 128 2.111578e-12 -1.578248e-12 129 -5.462170e-13 2.111578e-12 130 -2.769776e-13 -5.462170e-13 131 7.566191e-15 -2.769776e-13 132 1.799939e-13 7.566191e-15 133 -9.992997e-13 1.799939e-13 134 -1.462507e-12 -9.992997e-13 135 -5.880915e-13 -1.462507e-12 136 -1.217251e-12 -5.880915e-13 137 -1.312101e-12 -1.217251e-12 138 2.661359e-12 -1.312101e-12 139 -1.984175e-12 2.661359e-12 140 -2.184381e-12 -1.984175e-12 141 -6.178345e-13 -2.184381e-12 142 -2.894103e-12 -6.178345e-13 143 -2.186026e-12 -2.894103e-12 144 -1.440561e-12 -2.186026e-12 145 -1.541195e-12 -1.440561e-12 146 -2.607644e-12 -1.541195e-12 147 -1.882488e-12 -2.607644e-12 148 -1.240302e-12 -1.882488e-12 149 -1.330273e-12 -1.240302e-12 150 -4.169426e-12 -1.330273e-12 151 -9.154652e-12 -4.169426e-12 152 -4.787662e-12 -9.154652e-12 153 -3.817614e-12 -4.787662e-12 154 NA -3.817614e-12 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 8.578828e-12 -1.114563e-10 [2,] 7.148461e-12 8.578828e-12 [3,] 7.133197e-13 7.148461e-12 [4,] 6.523008e-12 7.133197e-13 [5,] 7.494735e-12 6.523008e-12 [6,] 6.266874e-12 7.494735e-12 [7,] 8.884864e-12 6.266874e-12 [8,] 2.967950e-12 8.884864e-12 [9,] 3.605763e-12 2.967950e-12 [10,] 6.945342e-12 3.605763e-12 [11,] 6.190240e-12 6.945342e-12 [12,] 6.226970e-12 6.190240e-12 [13,] 6.052846e-12 6.226970e-12 [14,] 3.776151e-12 6.052846e-12 [15,] 2.619264e-12 3.776151e-12 [16,] 1.808018e-13 2.619264e-12 [17,] -1.202467e-13 1.808018e-13 [18,] 2.343650e-12 -1.202467e-13 [19,] 2.329332e-12 2.343650e-12 [20,] 2.049147e-12 2.329332e-12 [21,] 1.510903e-12 2.049147e-12 [22,] -2.469125e-12 1.510903e-12 [23,] -7.843616e-13 -2.469125e-12 [24,] 1.867909e-14 -7.843616e-13 [25,] -5.582273e-13 1.867909e-14 [26,] -2.742854e-13 -5.582273e-13 [27,] -2.715165e-13 -2.742854e-13 [28,] -2.920327e-13 -2.715165e-13 [29,] -1.825872e-14 -2.920327e-13 [30,] 3.875784e-12 -1.825872e-14 [31,] 1.798389e-12 3.875784e-12 [32,] 5.377623e-13 1.798389e-12 [33,] 2.481486e-13 5.377623e-13 [34,] 3.937285e-12 2.481486e-13 [35,] 2.481810e-12 3.937285e-12 [36,] 3.054932e-12 2.481810e-12 [37,] 2.410425e-12 3.054932e-12 [38,] 3.975902e-12 2.410425e-12 [39,] 2.648459e-12 3.975902e-12 [40,] 2.203847e-12 2.648459e-12 [41,] 4.183074e-12 2.203847e-12 [42,] 2.045821e-12 4.183074e-12 [43,] 1.967316e-12 2.045821e-12 [44,] -2.093866e-12 1.967316e-12 [45,] -2.546414e-13 -2.093866e-12 [46,] 1.680581e-12 -2.546414e-13 [47,] 1.573384e-13 1.680581e-12 [48,] 5.486040e-13 1.573384e-13 [49,] 3.197545e-15 5.486040e-13 [50,] 3.272580e-13 3.197545e-15 [51,] 1.272370e-12 3.272580e-13 [52,] 1.312184e-12 1.272370e-12 [53,] -2.917071e-13 1.312184e-12 [54,] -3.896203e-13 -2.917071e-13 [55,] -1.559063e-13 -3.896203e-13 [56,] -7.655288e-13 -1.559063e-13 [57,] -1.882954e-13 -7.655288e-13 [58,] 8.752608e-13 -1.882954e-13 [59,] 5.232823e-13 8.752608e-13 [60,] 1.567607e-12 5.232823e-13 [61,] 3.713847e-13 1.567607e-12 [62,] 7.382354e-13 3.713847e-13 [63,] 9.922841e-13 7.382354e-13 [64,] 9.784806e-13 9.922841e-13 [65,] 3.993509e-13 9.784806e-13 [66,] -3.329261e-13 3.993509e-13 [67,] 4.305942e-12 -3.329261e-13 [68,] -8.056304e-13 4.305942e-12 [69,] 9.005498e-13 -8.056304e-13 [70,] 2.341314e-12 9.005498e-13 [71,] 4.816493e-12 2.341314e-12 [72,] 3.537183e-12 4.816493e-12 [73,] 5.266877e-12 3.537183e-12 [74,] 7.040296e-12 5.266877e-12 [75,] 4.483447e-12 7.040296e-12 [76,] 5.457145e-12 4.483447e-12 [77,] 4.186983e-12 5.457145e-12 [78,] -3.639968e-15 4.186983e-12 [79,] 7.699384e-13 -3.639968e-15 [80,] -8.209154e-13 7.699384e-13 [81,] -1.415559e-12 -8.209154e-13 [82,] 4.469327e-12 -1.415559e-12 [83,] 1.916649e-12 4.469327e-12 [84,] 4.697145e-12 1.916649e-12 [85,] 4.405809e-12 4.697145e-12 [86,] 1.398174e-12 4.405809e-12 [87,] 4.193191e-12 1.398174e-12 [88,] 7.186671e-14 4.193191e-12 [89,] 4.976723e-14 7.186671e-14 [90,] -1.088612e-12 4.976723e-14 [91,] 1.624096e-12 -1.088612e-12 [92,] -3.937772e-12 1.624096e-12 [93,] 9.648071e-13 -3.937772e-12 [94,] -5.817341e-13 9.648071e-13 [95,] -5.693372e-13 -5.817341e-13 [96,] -6.514325e-13 -5.693372e-13 [97,] -1.994303e-13 -6.514325e-13 [98,] 1.729054e-12 -1.994303e-13 [99,] 1.783412e-12 1.729054e-12 [100,] 5.487048e-13 1.783412e-12 [101,] 1.940554e-14 5.487048e-13 [102,] -1.738315e-12 1.940554e-14 [103,] -4.361407e-12 -1.738315e-12 [104,] -1.021642e-12 -4.361407e-12 [105,] 3.191361e-14 -1.021642e-12 [106,] -1.262377e-12 3.191361e-14 [107,] 1.410086e-12 -1.262377e-12 [108,] -3.856353e-13 1.410086e-12 [109,] -4.104460e-12 -3.856353e-13 [110,] -3.279596e-12 -4.104460e-12 [111,] -3.416984e-12 -3.279596e-12 [112,] -3.976797e-12 -3.416984e-12 [113,] -5.665331e-12 -3.976797e-12 [114,] -1.412070e-12 -5.665331e-12 [115,] -1.133726e-12 -1.412070e-12 [116,] -2.353165e-14 -1.133726e-12 [117,] 4.039752e-14 -2.353165e-14 [118,] 2.501982e-13 4.039752e-14 [119,] 1.120220e-12 2.501982e-13 [120,] -1.772810e-12 1.120220e-12 [121,] -1.339212e-12 -1.772810e-12 [122,] -1.036857e-12 -1.339212e-12 [123,] -1.102125e-12 -1.036857e-12 [124,] -6.288718e-13 -1.102125e-12 [125,] -1.301884e-12 -6.288718e-13 [126,] 1.239201e-12 -1.301884e-12 [127,] -1.578248e-12 1.239201e-12 [128,] 2.111578e-12 -1.578248e-12 [129,] -5.462170e-13 2.111578e-12 [130,] -2.769776e-13 -5.462170e-13 [131,] 7.566191e-15 -2.769776e-13 [132,] 1.799939e-13 7.566191e-15 [133,] -9.992997e-13 1.799939e-13 [134,] -1.462507e-12 -9.992997e-13 [135,] -5.880915e-13 -1.462507e-12 [136,] -1.217251e-12 -5.880915e-13 [137,] -1.312101e-12 -1.217251e-12 [138,] 2.661359e-12 -1.312101e-12 [139,] -1.984175e-12 2.661359e-12 [140,] -2.184381e-12 -1.984175e-12 [141,] -6.178345e-13 -2.184381e-12 [142,] -2.894103e-12 -6.178345e-13 [143,] -2.186026e-12 -2.894103e-12 [144,] -1.440561e-12 -2.186026e-12 [145,] -1.541195e-12 -1.440561e-12 [146,] -2.607644e-12 -1.541195e-12 [147,] -1.882488e-12 -2.607644e-12 [148,] -1.240302e-12 -1.882488e-12 [149,] -1.330273e-12 -1.240302e-12 [150,] -4.169426e-12 -1.330273e-12 [151,] -9.154652e-12 -4.169426e-12 [152,] -4.787662e-12 -9.154652e-12 [153,] -3.817614e-12 -4.787662e-12 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 8.578828e-12 -1.114563e-10 2 7.148461e-12 8.578828e-12 3 7.133197e-13 7.148461e-12 4 6.523008e-12 7.133197e-13 5 7.494735e-12 6.523008e-12 6 6.266874e-12 7.494735e-12 7 8.884864e-12 6.266874e-12 8 2.967950e-12 8.884864e-12 9 3.605763e-12 2.967950e-12 10 6.945342e-12 3.605763e-12 11 6.190240e-12 6.945342e-12 12 6.226970e-12 6.190240e-12 13 6.052846e-12 6.226970e-12 14 3.776151e-12 6.052846e-12 15 2.619264e-12 3.776151e-12 16 1.808018e-13 2.619264e-12 17 -1.202467e-13 1.808018e-13 18 2.343650e-12 -1.202467e-13 19 2.329332e-12 2.343650e-12 20 2.049147e-12 2.329332e-12 21 1.510903e-12 2.049147e-12 22 -2.469125e-12 1.510903e-12 23 -7.843616e-13 -2.469125e-12 24 1.867909e-14 -7.843616e-13 25 -5.582273e-13 1.867909e-14 26 -2.742854e-13 -5.582273e-13 27 -2.715165e-13 -2.742854e-13 28 -2.920327e-13 -2.715165e-13 29 -1.825872e-14 -2.920327e-13 30 3.875784e-12 -1.825872e-14 31 1.798389e-12 3.875784e-12 32 5.377623e-13 1.798389e-12 33 2.481486e-13 5.377623e-13 34 3.937285e-12 2.481486e-13 35 2.481810e-12 3.937285e-12 36 3.054932e-12 2.481810e-12 37 2.410425e-12 3.054932e-12 38 3.975902e-12 2.410425e-12 39 2.648459e-12 3.975902e-12 40 2.203847e-12 2.648459e-12 41 4.183074e-12 2.203847e-12 42 2.045821e-12 4.183074e-12 43 1.967316e-12 2.045821e-12 44 -2.093866e-12 1.967316e-12 45 -2.546414e-13 -2.093866e-12 46 1.680581e-12 -2.546414e-13 47 1.573384e-13 1.680581e-12 48 5.486040e-13 1.573384e-13 49 3.197545e-15 5.486040e-13 50 3.272580e-13 3.197545e-15 51 1.272370e-12 3.272580e-13 52 1.312184e-12 1.272370e-12 53 -2.917071e-13 1.312184e-12 54 -3.896203e-13 -2.917071e-13 55 -1.559063e-13 -3.896203e-13 56 -7.655288e-13 -1.559063e-13 57 -1.882954e-13 -7.655288e-13 58 8.752608e-13 -1.882954e-13 59 5.232823e-13 8.752608e-13 60 1.567607e-12 5.232823e-13 61 3.713847e-13 1.567607e-12 62 7.382354e-13 3.713847e-13 63 9.922841e-13 7.382354e-13 64 9.784806e-13 9.922841e-13 65 3.993509e-13 9.784806e-13 66 -3.329261e-13 3.993509e-13 67 4.305942e-12 -3.329261e-13 68 -8.056304e-13 4.305942e-12 69 9.005498e-13 -8.056304e-13 70 2.341314e-12 9.005498e-13 71 4.816493e-12 2.341314e-12 72 3.537183e-12 4.816493e-12 73 5.266877e-12 3.537183e-12 74 7.040296e-12 5.266877e-12 75 4.483447e-12 7.040296e-12 76 5.457145e-12 4.483447e-12 77 4.186983e-12 5.457145e-12 78 -3.639968e-15 4.186983e-12 79 7.699384e-13 -3.639968e-15 80 -8.209154e-13 7.699384e-13 81 -1.415559e-12 -8.209154e-13 82 4.469327e-12 -1.415559e-12 83 1.916649e-12 4.469327e-12 84 4.697145e-12 1.916649e-12 85 4.405809e-12 4.697145e-12 86 1.398174e-12 4.405809e-12 87 4.193191e-12 1.398174e-12 88 7.186671e-14 4.193191e-12 89 4.976723e-14 7.186671e-14 90 -1.088612e-12 4.976723e-14 91 1.624096e-12 -1.088612e-12 92 -3.937772e-12 1.624096e-12 93 9.648071e-13 -3.937772e-12 94 -5.817341e-13 9.648071e-13 95 -5.693372e-13 -5.817341e-13 96 -6.514325e-13 -5.693372e-13 97 -1.994303e-13 -6.514325e-13 98 1.729054e-12 -1.994303e-13 99 1.783412e-12 1.729054e-12 100 5.487048e-13 1.783412e-12 101 1.940554e-14 5.487048e-13 102 -1.738315e-12 1.940554e-14 103 -4.361407e-12 -1.738315e-12 104 -1.021642e-12 -4.361407e-12 105 3.191361e-14 -1.021642e-12 106 -1.262377e-12 3.191361e-14 107 1.410086e-12 -1.262377e-12 108 -3.856353e-13 1.410086e-12 109 -4.104460e-12 -3.856353e-13 110 -3.279596e-12 -4.104460e-12 111 -3.416984e-12 -3.279596e-12 112 -3.976797e-12 -3.416984e-12 113 -5.665331e-12 -3.976797e-12 114 -1.412070e-12 -5.665331e-12 115 -1.133726e-12 -1.412070e-12 116 -2.353165e-14 -1.133726e-12 117 4.039752e-14 -2.353165e-14 118 2.501982e-13 4.039752e-14 119 1.120220e-12 2.501982e-13 120 -1.772810e-12 1.120220e-12 121 -1.339212e-12 -1.772810e-12 122 -1.036857e-12 -1.339212e-12 123 -1.102125e-12 -1.036857e-12 124 -6.288718e-13 -1.102125e-12 125 -1.301884e-12 -6.288718e-13 126 1.239201e-12 -1.301884e-12 127 -1.578248e-12 1.239201e-12 128 2.111578e-12 -1.578248e-12 129 -5.462170e-13 2.111578e-12 130 -2.769776e-13 -5.462170e-13 131 7.566191e-15 -2.769776e-13 132 1.799939e-13 7.566191e-15 133 -9.992997e-13 1.799939e-13 134 -1.462507e-12 -9.992997e-13 135 -5.880915e-13 -1.462507e-12 136 -1.217251e-12 -5.880915e-13 137 -1.312101e-12 -1.217251e-12 138 2.661359e-12 -1.312101e-12 139 -1.984175e-12 2.661359e-12 140 -2.184381e-12 -1.984175e-12 141 -6.178345e-13 -2.184381e-12 142 -2.894103e-12 -6.178345e-13 143 -2.186026e-12 -2.894103e-12 144 -1.440561e-12 -2.186026e-12 145 -1.541195e-12 -1.440561e-12 146 -2.607644e-12 -1.541195e-12 147 -1.882488e-12 -2.607644e-12 148 -1.240302e-12 -1.882488e-12 149 -1.330273e-12 -1.240302e-12 150 -4.169426e-12 -1.330273e-12 151 -9.154652e-12 -4.169426e-12 152 -4.787662e-12 -9.154652e-12 153 -3.817614e-12 -4.787662e-12 > 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/7flqr1321807634.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/8rv171321807634.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/9fgdb1321807634.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/rcomp/tmp/10npe61321807634.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11x5v81321807634.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/12y06g1321807634.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/13sdn61321807635.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/14eabt1321807635.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/15w2ln1321807635.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/16ymvd1321807635.tab") + } > > try(system("convert tmp/1fke51321807634.ps tmp/1fke51321807634.png",intern=TRUE)) character(0) > try(system("convert tmp/2w7c21321807634.ps tmp/2w7c21321807634.png",intern=TRUE)) character(0) > try(system("convert tmp/3j7qo1321807634.ps tmp/3j7qo1321807634.png",intern=TRUE)) character(0) > try(system("convert tmp/4bt4y1321807634.ps tmp/4bt4y1321807634.png",intern=TRUE)) character(0) > try(system("convert tmp/5hs671321807634.ps tmp/5hs671321807634.png",intern=TRUE)) character(0) > try(system("convert tmp/6d0m51321807634.ps tmp/6d0m51321807634.png",intern=TRUE)) character(0) > try(system("convert tmp/7flqr1321807634.ps tmp/7flqr1321807634.png",intern=TRUE)) character(0) > try(system("convert tmp/8rv171321807634.ps tmp/8rv171321807634.png",intern=TRUE)) character(0) > try(system("convert tmp/9fgdb1321807634.ps tmp/9fgdb1321807634.png",intern=TRUE)) character(0) > try(system("convert tmp/10npe61321807634.ps tmp/10npe61321807634.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.22 0.35 5.55