R version 2.13.0 (2011-04-13) Copyright (C) 2011 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(95556 + ,114468 + ,128 + ,54565 + ,88594 + ,89 + ,63016 + ,74151 + ,68 + ,79774 + ,77921 + ,108 + ,31258 + ,53212 + ,51 + ,52491 + ,34956 + ,33 + ,91256 + ,149703 + ,119 + ,22807 + ,6853 + ,5 + ,77411 + ,58907 + ,63 + ,48821 + ,67067 + ,66 + ,52295 + ,110563 + ,98 + ,63262 + ,58126 + ,71 + ,50466 + ,57113 + ,55 + ,62932 + ,77993 + ,116 + ,38439 + ,68091 + ,71 + ,70817 + ,124676 + ,120 + ,105965 + ,109522 + ,122 + ,73795 + ,75865 + ,74 + ,82043 + ,79746 + ,111 + ,74349 + ,77844 + ,103 + ,82204 + ,98681 + ,98 + ,55709 + ,105531 + ,100 + ,37137 + ,51428 + ,42 + ,70780 + ,65703 + ,100 + ,55027 + ,72562 + ,105 + ,56699 + ,81728 + ,77 + ,65911 + ,95580 + ,83 + ,56316 + ,98278 + ,98 + ,26982 + ,46629 + ,46 + ,54628 + ,115189 + ,95 + ,96750 + ,124865 + ,91 + ,53009 + ,59392 + ,91 + ,64664 + ,127818 + ,94 + ,36990 + ,17821 + ,15 + ,85224 + ,154076 + ,137 + ,37048 + ,64881 + ,56 + ,59635 + ,136506 + ,78 + ,42051 + ,66524 + ,68 + ,26998 + ,45988 + ,34 + ,63717 + ,107445 + ,94 + ,55071 + ,102772 + ,82 + ,40001 + ,46657 + ,63 + ,54506 + ,97563 + ,58 + ,35838 + ,36663 + ,43 + ,50838 + ,55369 + ,36 + ,86997 + ,77921 + ,64 + ,33032 + ,56968 + ,21 + ,61704 + ,77519 + ,104 + ,117986 + ,129805 + ,124 + ,56733 + ,72761 + ,101 + ,55064 + ,81278 + ,85 + ,5950 + ,15049 + ,7 + ,84607 + ,113935 + ,124 + ,32551 + ,25109 + ,21 + ,31701 + ,45824 + ,35 + ,71170 + ,89644 + ,95 + ,101773 + ,109011 + ,102 + ,101653 + ,134245 + ,212 + ,81493 + ,136692 + ,141 + ,55901 + ,50741 + ,54 + ,109104 + ,149510 + ,117 + ,114425 + ,147888 + ,145 + ,36311 + ,54987 + ,50 + ,70027 + ,74467 + ,80 + ,73713 + ,100033 + ,87 + ,40671 + ,85505 + ,78 + ,89041 + ,62426 + ,86 + ,57231 + ,82932 + ,82 + ,68608 + ,72002 + ,119 + ,59155 + ,65469 + ,75 + ,55827 + ,63572 + ,70 + ,22618 + ,23824 + ,25 + ,58425 + ,73831 + ,66 + ,65724 + ,63551 + ,89 + ,56979 + ,56756 + ,99 + ,72369 + ,81399 + ,98 + ,79194 + ,117881 + ,104 + ,202316 + ,70711 + ,48 + ,44970 + ,50495 + ,81 + ,49319 + ,53845 + ,64 + ,36252 + ,51390 + ,44 + ,75741 + ,104953 + ,104 + ,38417 + ,65983 + ,36 + ,64102 + ,76839 + ,120 + ,56622 + ,55792 + ,58 + ,15430 + ,25155 + ,27 + ,72571 + ,55291 + ,84 + ,67271 + ,84279 + ,56 + ,43460 + ,99692 + ,46 + ,99501 + ,59633 + ,119 + ,28340 + ,63249 + ,57 + ,76013 + ,82928 + ,139 + ,37361 + ,50000 + ,51 + ,48204 + ,69455 + ,85 + ,76168 + ,84068 + ,91 + ,85168 + ,76195 + ,79 + ,125410 + ,114634 + ,142 + ,123328 + ,139357 + ,149 + ,83038 + ,110044 + ,96 + ,120087 + ,155118 + ,198 + ,91939 + ,83061 + ,61 + ,103646 + ,127122 + ,145 + ,29467 + ,45653 + ,26 + ,43750 + ,19630 + ,49 + ,34497 + ,67229 + ,68 + ,66477 + ,86060 + ,145 + ,71181 + ,88003 + ,82 + ,74482 + ,95815 + ,102 + ,174949 + ,85499 + ,52 + ,46765 + ,27220 + ,56 + ,90257 + ,109882 + ,80 + ,51370 + ,72579 + ,99 + ,1168 + ,5841 + ,11 + ,51360 + ,68369 + ,87 + ,25162 + ,24610 + ,28 + ,21067 + ,30995 + ,67 + ,58233 + ,150662 + ,150 + ,855 + ,6622 + ,4 + ,85903 + ,93694 + ,71 + ,14116 + ,13155 + ,39 + ,57637 + ,111908 + ,87 + ,94137 + ,57550 + ,66 + ,62147 + ,16356 + ,23 + ,62832 + ,40174 + ,56 + ,8773 + ,13983 + ,16 + ,63785 + ,52316 + ,49 + ,65196 + ,99585 + ,108 + ,73087 + ,86271 + ,112 + ,72631 + ,131012 + ,110 + ,86281 + ,130274 + ,126 + ,162365 + ,159051 + ,155 + ,56530 + ,76506 + ,75 + ,35606 + ,49145 + ,30 + ,70111 + ,66398 + ,78 + ,92046 + ,127546 + ,135 + ,63989 + ,6802 + ,8 + ,104911 + ,99509 + ,114 + ,43448 + ,43106 + ,60 + ,60029 + ,108303 + ,99 + ,38650 + ,64167 + ,98 + ,47261 + ,8579 + ,33 + ,73586 + ,97811 + ,93 + ,83042 + ,84365 + ,157 + ,37238 + ,10901 + ,15 + ,63958 + ,91346 + ,98 + ,78956 + ,33660 + ,49 + ,99518 + ,93634 + ,88 + ,111436 + ,109348 + ,151 + ,0 + ,0 + ,0 + ,6023 + ,7953 + ,5 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,42564 + ,63538 + ,80 + ,38885 + ,108281 + ,122 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1644 + ,4245 + ,6 + ,6179 + ,21509 + ,13 + ,3926 + ,7670 + ,3 + ,23238 + ,10641 + ,18 + ,0 + ,0 + ,0 + ,49288 + ,41243 + ,48) + ,dim=c(3 + ,164) + ,dimnames=list(c('Character' + ,'Seconds' + ,'Blogs') + ,1:164)) > y <- array(NA,dim=c(3,164),dimnames=list(c('Character','Seconds','Blogs'),1:164)) > 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 = '2' > 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 Seconds Character Blogs 1 114468 95556 128 2 88594 54565 89 3 74151 63016 68 4 77921 79774 108 5 53212 31258 51 6 34956 52491 33 7 149703 91256 119 8 6853 22807 5 9 58907 77411 63 10 67067 48821 66 11 110563 52295 98 12 58126 63262 71 13 57113 50466 55 14 77993 62932 116 15 68091 38439 71 16 124676 70817 120 17 109522 105965 122 18 75865 73795 74 19 79746 82043 111 20 77844 74349 103 21 98681 82204 98 22 105531 55709 100 23 51428 37137 42 24 65703 70780 100 25 72562 55027 105 26 81728 56699 77 27 95580 65911 83 28 98278 56316 98 29 46629 26982 46 30 115189 54628 95 31 124865 96750 91 32 59392 53009 91 33 127818 64664 94 34 17821 36990 15 35 154076 85224 137 36 64881 37048 56 37 136506 59635 78 38 66524 42051 68 39 45988 26998 34 40 107445 63717 94 41 102772 55071 82 42 46657 40001 63 43 97563 54506 58 44 36663 35838 43 45 55369 50838 36 46 77921 86997 64 47 56968 33032 21 48 77519 61704 104 49 129805 117986 124 50 72761 56733 101 51 81278 55064 85 52 15049 5950 7 53 113935 84607 124 54 25109 32551 21 55 45824 31701 35 56 89644 71170 95 57 109011 101773 102 58 134245 101653 212 59 136692 81493 141 60 50741 55901 54 61 149510 109104 117 62 147888 114425 145 63 54987 36311 50 64 74467 70027 80 65 100033 73713 87 66 85505 40671 78 67 62426 89041 86 68 82932 57231 82 69 72002 68608 119 70 65469 59155 75 71 63572 55827 70 72 23824 22618 25 73 73831 58425 66 74 63551 65724 89 75 56756 56979 99 76 81399 72369 98 77 117881 79194 104 78 70711 202316 48 79 50495 44970 81 80 53845 49319 64 81 51390 36252 44 82 104953 75741 104 83 65983 38417 36 84 76839 64102 120 85 55792 56622 58 86 25155 15430 27 87 55291 72571 84 88 84279 67271 56 89 99692 43460 46 90 59633 99501 119 91 63249 28340 57 92 82928 76013 139 93 50000 37361 51 94 69455 48204 85 95 84068 76168 91 96 76195 85168 79 97 114634 125410 142 98 139357 123328 149 99 110044 83038 96 100 155118 120087 198 101 83061 91939 61 102 127122 103646 145 103 45653 29467 26 104 19630 43750 49 105 67229 34497 68 106 86060 66477 145 107 88003 71181 82 108 95815 74482 102 109 85499 174949 52 110 27220 46765 56 111 109882 90257 80 112 72579 51370 99 113 5841 1168 11 114 68369 51360 87 115 24610 25162 28 116 30995 21067 67 117 150662 58233 150 118 6622 855 4 119 93694 85903 71 120 13155 14116 39 121 111908 57637 87 122 57550 94137 66 123 16356 62147 23 124 40174 62832 56 125 13983 8773 16 126 52316 63785 49 127 99585 65196 108 128 86271 73087 112 129 131012 72631 110 130 130274 86281 126 131 159051 162365 155 132 76506 56530 75 133 49145 35606 30 134 66398 70111 78 135 127546 92046 135 136 6802 63989 8 137 99509 104911 114 138 43106 43448 60 139 108303 60029 99 140 64167 38650 98 141 8579 47261 33 142 97811 73586 93 143 84365 83042 157 144 10901 37238 15 145 91346 63958 98 146 33660 78956 49 147 93634 99518 88 148 109348 111436 151 149 0 0 0 150 7953 6023 5 151 0 0 0 152 0 0 0 153 0 0 0 154 0 0 0 155 63538 42564 80 156 108281 38885 122 157 0 0 0 158 0 0 0 159 4245 1644 6 160 21509 6179 13 161 7670 3926 3 162 10641 23238 18 163 0 0 0 164 41243 49288 48 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Character Blogs 5911.6923 0.2742 653.6662 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -51352 -11692 -2177 10456 63254 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 5.912e+03 3.213e+03 1.840 0.0676 . Character 2.742e-01 6.285e-02 4.363 2.28e-05 *** Blogs 6.537e+02 4.917e+01 13.293 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 19080 on 161 degrees of freedom Multiple R-squared: 0.7739, Adjusted R-squared: 0.7711 F-statistic: 275.5 on 2 and 161 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,] 0.2475046 4.950093e-01 7.524954e-01 [2,] 0.7569247 4.861506e-01 2.430753e-01 [3,] 0.6382928 7.234145e-01 3.617072e-01 [4,] 0.5259218 9.481565e-01 4.740782e-01 [5,] 0.4069627 8.139254e-01 5.930373e-01 [6,] 0.3499754 6.999507e-01 6.500246e-01 [7,] 0.2986939 5.973878e-01 7.013061e-01 [8,] 0.2166283 4.332567e-01 7.833717e-01 [9,] 0.4154416 8.308832e-01 5.845584e-01 [10,] 0.3302624 6.605248e-01 6.697376e-01 [11,] 0.2915065 5.830130e-01 7.084935e-01 [12,] 0.2317475 4.634951e-01 7.682525e-01 [13,] 0.1751953 3.503906e-01 8.248047e-01 [14,] 0.2217654 4.435308e-01 7.782346e-01 [15,] 0.2174708 4.349415e-01 7.825292e-01 [16,] 0.1744069 3.488137e-01 8.255931e-01 [17,] 0.1530857 3.061715e-01 8.469143e-01 [18,] 0.1200661 2.401322e-01 8.799339e-01 [19,] 0.1721241 3.442481e-01 8.278759e-01 [20,] 0.1942504 3.885007e-01 8.057496e-01 [21,] 0.1624097 3.248193e-01 8.375903e-01 [22,] 0.1600662 3.201324e-01 8.399338e-01 [23,] 0.1327129 2.654258e-01 8.672871e-01 [24,] 0.1012223 2.024445e-01 8.987777e-01 [25,] 0.1451238 2.902477e-01 8.548762e-01 [26,] 0.2614804 5.229607e-01 7.385196e-01 [27,] 0.2926066 5.852132e-01 7.073934e-01 [28,] 0.4836592 9.673183e-01 5.163408e-01 [29,] 0.4345076 8.690152e-01 5.654924e-01 [30,] 0.5070870 9.858259e-01 4.929130e-01 [31,] 0.4683135 9.366270e-01 5.316865e-01 [32,] 0.8645611 2.708779e-01 1.354389e-01 [33,] 0.8342885 3.314229e-01 1.657115e-01 [34,] 0.8067763 3.864475e-01 1.932237e-01 [35,] 0.8025722 3.948556e-01 1.974278e-01 [36,] 0.8213472 3.573056e-01 1.786528e-01 [37,] 0.8096884 3.806233e-01 1.903116e-01 [38,] 0.8853548 2.292905e-01 1.146452e-01 [39,] 0.8664162 2.671677e-01 1.335838e-01 [40,] 0.8466443 3.067114e-01 1.533557e-01 [41,] 0.8171294 3.657412e-01 1.828706e-01 [42,] 0.8456343 3.087313e-01 1.543657e-01 [43,] 0.8441699 3.116603e-01 1.558301e-01 [44,] 0.8188375 3.623250e-01 1.811625e-01 [45,] 0.8185188 3.629625e-01 1.814812e-01 [46,] 0.7869208 4.261583e-01 2.130792e-01 [47,] 0.7510593 4.978815e-01 2.489407e-01 [48,] 0.7146741 5.706518e-01 2.853259e-01 [49,] 0.6782789 6.434422e-01 3.217211e-01 [50,] 0.6404197 7.191607e-01 3.595803e-01 [51,] 0.5983298 8.033405e-01 4.016702e-01 [52,] 0.5584265 8.831470e-01 4.415735e-01 [53,] 0.6948829 6.102343e-01 3.051171e-01 [54,] 0.6843182 6.313636e-01 3.156818e-01 [55,] 0.6558121 6.883759e-01 3.441879e-01 [56,] 0.7446491 5.107017e-01 2.553509e-01 [57,] 0.7285237 5.429526e-01 2.714763e-01 [58,] 0.6934049 6.131903e-01 3.065951e-01 [59,] 0.6604207 6.791587e-01 3.395793e-01 [60,] 0.6471881 7.056238e-01 3.528119e-01 [61,] 0.6433125 7.133751e-01 3.566875e-01 [62,] 0.6998463 6.003073e-01 3.001537e-01 [63,] 0.6667691 6.664618e-01 3.332309e-01 [64,] 0.7334556 5.330888e-01 2.665444e-01 [65,] 0.7030041 5.939917e-01 2.969959e-01 [66,] 0.6677750 6.644499e-01 3.322250e-01 [67,] 0.6320774 7.358452e-01 3.679226e-01 [68,] 0.5983916 8.032168e-01 4.016084e-01 [69,] 0.6034252 7.931496e-01 3.965748e-01 [70,] 0.6618457 6.763086e-01 3.381543e-01 [71,] 0.6308003 7.383993e-01 3.691997e-01 [72,] 0.6478586 7.042828e-01 3.521414e-01 [73,] 0.6929255 6.141489e-01 3.070745e-01 [74,] 0.7036665 5.926670e-01 2.963335e-01 [75,] 0.6719050 6.561901e-01 3.280950e-01 [76,] 0.6366205 7.267589e-01 3.633795e-01 [77,] 0.6092061 7.815879e-01 3.907939e-01 [78,] 0.6468108 7.063784e-01 3.531892e-01 [79,] 0.6749379 6.501242e-01 3.250621e-01 [80,] 0.6367240 7.265519e-01 3.632760e-01 [81,] 0.5974717 8.050566e-01 4.025283e-01 [82,] 0.6299906 7.400188e-01 3.700094e-01 [83,] 0.6557063 6.885874e-01 3.442937e-01 [84,] 0.8813382 2.373237e-01 1.186618e-01 [85,] 0.9705722 5.885567e-02 2.942784e-02 [86,] 0.9674252 6.514965e-02 3.257482e-02 [87,] 0.9823691 3.526181e-02 1.763090e-02 [88,] 0.9771958 4.560839e-02 2.280420e-02 [89,] 0.9707131 5.857375e-02 2.928688e-02 [90,] 0.9622193 7.556144e-02 3.778072e-02 [91,] 0.9521845 9.563104e-02 4.781552e-02 [92,] 0.9504125 9.917501e-02 4.958751e-02 [93,] 0.9380161 1.239678e-01 6.198392e-02 [94,] 0.9411358 1.177284e-01 5.886420e-02 [95,] 0.9340159 1.319681e-01 6.598407e-02 [96,] 0.9284287 1.431425e-01 7.157125e-02 [97,] 0.9109890 1.780221e-01 8.901104e-02 [98,] 0.9108339 1.783323e-01 8.916613e-02 [99,] 0.9366298 1.267403e-01 6.337017e-02 [100,] 0.9251846 1.496309e-01 7.481544e-02 [101,] 0.9594135 8.117297e-02 4.058649e-02 [102,] 0.9520274 9.594517e-02 4.797258e-02 [103,] 0.9390808 1.218384e-01 6.091921e-02 [104,] 0.9269287 1.461425e-01 7.307126e-02 [105,] 0.9445888 1.108224e-01 5.541119e-02 [106,] 0.9676060 6.478803e-02 3.239401e-02 [107,] 0.9630433 7.391332e-02 3.695666e-02 [108,] 0.9539767 9.204667e-02 4.602334e-02 [109,] 0.9435812 1.128376e-01 5.641882e-02 [110,] 0.9295280 1.409439e-01 7.047197e-02 [111,] 0.9468942 1.062115e-01 5.310576e-02 [112,] 0.9602405 7.951893e-02 3.975946e-02 [113,] 0.9488046 1.023908e-01 5.119538e-02 [114,] 0.9595503 8.089949e-02 4.044975e-02 [115,] 0.9643572 7.128563e-02 3.564281e-02 [116,] 0.9870115 2.597698e-02 1.298849e-02 [117,] 0.9836283 3.274345e-02 1.637173e-02 [118,] 0.9816636 3.667271e-02 1.833635e-02 [119,] 0.9798296 4.034071e-02 2.017035e-02 [120,] 0.9721662 5.566762e-02 2.783381e-02 [121,] 0.9624387 7.512257e-02 3.756129e-02 [122,] 0.9510291 9.794181e-02 4.897091e-02 [123,] 0.9418598 1.162804e-01 5.814019e-02 [124,] 0.9797826 4.043484e-02 2.021742e-02 [125,] 0.9849144 3.017112e-02 1.508556e-02 [126,] 0.9875376 2.492478e-02 1.246239e-02 [127,] 0.9854629 2.907416e-02 1.453708e-02 [128,] 0.9892199 2.156028e-02 1.078014e-02 [129,] 0.9839541 3.209182e-02 1.604591e-02 [130,] 0.9852707 2.945855e-02 1.472928e-02 [131,] 0.9805697 3.886058e-02 1.943029e-02 [132,] 0.9726218 5.475644e-02 2.737822e-02 [133,] 0.9623532 7.529367e-02 3.764683e-02 [134,] 0.9871478 2.570440e-02 1.285220e-02 [135,] 0.9830104 3.397915e-02 1.698958e-02 [136,] 0.9894482 2.110366e-02 1.055183e-02 [137,] 0.9951460 9.707967e-03 4.853984e-03 [138,] 0.9999877 2.456115e-05 1.228058e-05 [139,] 0.9999695 6.098979e-05 3.049489e-05 [140,] 0.9999570 8.602775e-05 4.301388e-05 [141,] 0.9999440 1.119191e-04 5.595953e-05 [142,] 1.0000000 7.504355e-08 3.752177e-08 [143,] 1.0000000 6.565350e-08 3.282675e-08 [144,] 0.9999998 3.396026e-07 1.698013e-07 [145,] 0.9999993 1.368533e-06 6.842664e-07 [146,] 0.9999966 6.822834e-06 3.411417e-06 [147,] 0.9999838 3.241019e-05 1.620510e-05 [148,] 0.9999270 1.459173e-04 7.295866e-05 [149,] 0.9996908 6.184171e-04 3.092086e-04 [150,] 0.9992554 1.489174e-03 7.445870e-04 [151,] 0.9992043 1.591478e-03 7.957390e-04 [152,] 0.9957441 8.511795e-03 4.255898e-03 [153,] 0.9794007 4.119869e-02 2.059935e-02 > postscript(file="/var/wessaorg/rcomp/tmp/1fr031321971799.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/wessaorg/rcomp/tmp/28c7t1321971799.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/30ecx1321971799.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4c50r1321971799.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5wt8x1321971799.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 = 164 Frequency = 1 1 2 3 4 5 6 -1318.0848 9542.2005 6508.6032 -20463.7313 5391.1892 -6921.7207 7 8 9 10 11 12 40979.1360 -8581.5765 -9414.7261 4624.7485 26250.7254 -11544.8581 13 14 15 16 17 18 1409.9549 -21002.3395 5227.5594 20903.6265 -5196.6340 1344.5912 19 20 21 22 23 24 -21221.9767 -15784.6574 6166.5319 18975.1434 7877.9383 -24985.9022 25 26 27 28 29 30 -17075.1572 9934.9706 17338.6900 12863.0132 3249.1634 32197.9261 31 32 33 34 35 36 32937.1250 -20540.4172 42728.3367 -8039.7606 35240.3494 12204.0183 37 38 39 40 41 42 63254.1407 4631.0094 10447.7702 22615.0403 28157.0994 -11405.4710 43 44 45 46 47 48 38791.0335 -7184.4924 11983.5966 6316.7593 28270.6772 -13295.5802 49 50 51 52 53 54 10482.4149 -14729.3429 4704.0204 2929.9262 3766.2154 -3456.4141 55 56 57 58 59 60 8340.3613 2116.4760 8515.2975 -38121.0786 16264.8676 -5798.8640 61 62 63 64 65 66 37198.8631 15814.9868 6434.1293 -2942.0762 17037.4178 17453.7960 67 68 69 70 71 72 -24119.4407 7724.7447 -30510.9148 -5690.2262 -3406.2300 -4632.0699 73 74 75 76 77 78 8754.9638 -18561.0248 -29494.4731 -8418.3344 22269.9918 -22059.4684 79 80 81 82 83 84 -20696.1532 -7426.4898 6775.3067 10288.9367 26003.9106 -25091.8633 85 86 87 88 89 90 -3560.2548 -2637.1774 -25430.4034 23313.7140 51793.2645 -51351.9589 91 92 93 94 95 96 12306.4193 -34688.9740 505.5128 -5237.7048 -2215.5021 -4712.6522 97 98 99 100 101 102 -18490.5223 2227.7782 18608.1496 -13152.0604 12062.4723 -1994.9985 103 104 105 106 107 108 14665.0057 -30309.2632 7407.6055 -32863.8349 8970.1203 2803.5352 109 110 111 112 113 114 -2381.0536 -28121.7553 26925.0828 -12133.2704 -7581.3311 -8496.5333 115 116 117 118 119 120 -6504.7309 -24489.7078 30730.6546 -2138.8309 17814.1124 -22120.8230 121 122 123 124 125 126 33321.0728 -17319.6347 -21633.1035 -19573.9422 -4793.2446 -3117.6277 127 128 129 130 131 132 5198.1147 -12894.5646 33278.8205 18338.8080 7294.3401 6066.6493 133 134 135 136 137 138 13858.7918 -9726.7798 8146.8281 -21887.2571 -9691.2570 -13940.7718 139 140 141 142 143 144 21216.1001 -16403.2929 -31864.4543 10928.2487 -46945.5871 -15027.7717 145 146 147 148 149 150 3835.2840 -25934.0971 2908.0321 -25827.3123 -5911.6923 -2878.7607 151 152 153 154 155 156 -5911.6923 -5911.6923 -5911.6923 -5911.6923 -6339.6696 11958.2717 157 158 159 160 161 162 -5911.6923 -5911.6923 -6039.5374 5405.1283 -1279.3506 -13409.4341 163 164 -5911.6923 -9561.3288 > postscript(file="/var/wessaorg/rcomp/tmp/681vh1321971799.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 = 164 Frequency = 1 lag(myerror, k = 1) myerror 0 -1318.0848 NA 1 9542.2005 -1318.0848 2 6508.6032 9542.2005 3 -20463.7313 6508.6032 4 5391.1892 -20463.7313 5 -6921.7207 5391.1892 6 40979.1360 -6921.7207 7 -8581.5765 40979.1360 8 -9414.7261 -8581.5765 9 4624.7485 -9414.7261 10 26250.7254 4624.7485 11 -11544.8581 26250.7254 12 1409.9549 -11544.8581 13 -21002.3395 1409.9549 14 5227.5594 -21002.3395 15 20903.6265 5227.5594 16 -5196.6340 20903.6265 17 1344.5912 -5196.6340 18 -21221.9767 1344.5912 19 -15784.6574 -21221.9767 20 6166.5319 -15784.6574 21 18975.1434 6166.5319 22 7877.9383 18975.1434 23 -24985.9022 7877.9383 24 -17075.1572 -24985.9022 25 9934.9706 -17075.1572 26 17338.6900 9934.9706 27 12863.0132 17338.6900 28 3249.1634 12863.0132 29 32197.9261 3249.1634 30 32937.1250 32197.9261 31 -20540.4172 32937.1250 32 42728.3367 -20540.4172 33 -8039.7606 42728.3367 34 35240.3494 -8039.7606 35 12204.0183 35240.3494 36 63254.1407 12204.0183 37 4631.0094 63254.1407 38 10447.7702 4631.0094 39 22615.0403 10447.7702 40 28157.0994 22615.0403 41 -11405.4710 28157.0994 42 38791.0335 -11405.4710 43 -7184.4924 38791.0335 44 11983.5966 -7184.4924 45 6316.7593 11983.5966 46 28270.6772 6316.7593 47 -13295.5802 28270.6772 48 10482.4149 -13295.5802 49 -14729.3429 10482.4149 50 4704.0204 -14729.3429 51 2929.9262 4704.0204 52 3766.2154 2929.9262 53 -3456.4141 3766.2154 54 8340.3613 -3456.4141 55 2116.4760 8340.3613 56 8515.2975 2116.4760 57 -38121.0786 8515.2975 58 16264.8676 -38121.0786 59 -5798.8640 16264.8676 60 37198.8631 -5798.8640 61 15814.9868 37198.8631 62 6434.1293 15814.9868 63 -2942.0762 6434.1293 64 17037.4178 -2942.0762 65 17453.7960 17037.4178 66 -24119.4407 17453.7960 67 7724.7447 -24119.4407 68 -30510.9148 7724.7447 69 -5690.2262 -30510.9148 70 -3406.2300 -5690.2262 71 -4632.0699 -3406.2300 72 8754.9638 -4632.0699 73 -18561.0248 8754.9638 74 -29494.4731 -18561.0248 75 -8418.3344 -29494.4731 76 22269.9918 -8418.3344 77 -22059.4684 22269.9918 78 -20696.1532 -22059.4684 79 -7426.4898 -20696.1532 80 6775.3067 -7426.4898 81 10288.9367 6775.3067 82 26003.9106 10288.9367 83 -25091.8633 26003.9106 84 -3560.2548 -25091.8633 85 -2637.1774 -3560.2548 86 -25430.4034 -2637.1774 87 23313.7140 -25430.4034 88 51793.2645 23313.7140 89 -51351.9589 51793.2645 90 12306.4193 -51351.9589 91 -34688.9740 12306.4193 92 505.5128 -34688.9740 93 -5237.7048 505.5128 94 -2215.5021 -5237.7048 95 -4712.6522 -2215.5021 96 -18490.5223 -4712.6522 97 2227.7782 -18490.5223 98 18608.1496 2227.7782 99 -13152.0604 18608.1496 100 12062.4723 -13152.0604 101 -1994.9985 12062.4723 102 14665.0057 -1994.9985 103 -30309.2632 14665.0057 104 7407.6055 -30309.2632 105 -32863.8349 7407.6055 106 8970.1203 -32863.8349 107 2803.5352 8970.1203 108 -2381.0536 2803.5352 109 -28121.7553 -2381.0536 110 26925.0828 -28121.7553 111 -12133.2704 26925.0828 112 -7581.3311 -12133.2704 113 -8496.5333 -7581.3311 114 -6504.7309 -8496.5333 115 -24489.7078 -6504.7309 116 30730.6546 -24489.7078 117 -2138.8309 30730.6546 118 17814.1124 -2138.8309 119 -22120.8230 17814.1124 120 33321.0728 -22120.8230 121 -17319.6347 33321.0728 122 -21633.1035 -17319.6347 123 -19573.9422 -21633.1035 124 -4793.2446 -19573.9422 125 -3117.6277 -4793.2446 126 5198.1147 -3117.6277 127 -12894.5646 5198.1147 128 33278.8205 -12894.5646 129 18338.8080 33278.8205 130 7294.3401 18338.8080 131 6066.6493 7294.3401 132 13858.7918 6066.6493 133 -9726.7798 13858.7918 134 8146.8281 -9726.7798 135 -21887.2571 8146.8281 136 -9691.2570 -21887.2571 137 -13940.7718 -9691.2570 138 21216.1001 -13940.7718 139 -16403.2929 21216.1001 140 -31864.4543 -16403.2929 141 10928.2487 -31864.4543 142 -46945.5871 10928.2487 143 -15027.7717 -46945.5871 144 3835.2840 -15027.7717 145 -25934.0971 3835.2840 146 2908.0321 -25934.0971 147 -25827.3123 2908.0321 148 -5911.6923 -25827.3123 149 -2878.7607 -5911.6923 150 -5911.6923 -2878.7607 151 -5911.6923 -5911.6923 152 -5911.6923 -5911.6923 153 -5911.6923 -5911.6923 154 -6339.6696 -5911.6923 155 11958.2717 -6339.6696 156 -5911.6923 11958.2717 157 -5911.6923 -5911.6923 158 -6039.5374 -5911.6923 159 5405.1283 -6039.5374 160 -1279.3506 5405.1283 161 -13409.4341 -1279.3506 162 -5911.6923 -13409.4341 163 -9561.3288 -5911.6923 164 NA -9561.3288 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 9542.2005 -1318.0848 [2,] 6508.6032 9542.2005 [3,] -20463.7313 6508.6032 [4,] 5391.1892 -20463.7313 [5,] -6921.7207 5391.1892 [6,] 40979.1360 -6921.7207 [7,] -8581.5765 40979.1360 [8,] -9414.7261 -8581.5765 [9,] 4624.7485 -9414.7261 [10,] 26250.7254 4624.7485 [11,] -11544.8581 26250.7254 [12,] 1409.9549 -11544.8581 [13,] -21002.3395 1409.9549 [14,] 5227.5594 -21002.3395 [15,] 20903.6265 5227.5594 [16,] -5196.6340 20903.6265 [17,] 1344.5912 -5196.6340 [18,] -21221.9767 1344.5912 [19,] -15784.6574 -21221.9767 [20,] 6166.5319 -15784.6574 [21,] 18975.1434 6166.5319 [22,] 7877.9383 18975.1434 [23,] -24985.9022 7877.9383 [24,] -17075.1572 -24985.9022 [25,] 9934.9706 -17075.1572 [26,] 17338.6900 9934.9706 [27,] 12863.0132 17338.6900 [28,] 3249.1634 12863.0132 [29,] 32197.9261 3249.1634 [30,] 32937.1250 32197.9261 [31,] -20540.4172 32937.1250 [32,] 42728.3367 -20540.4172 [33,] -8039.7606 42728.3367 [34,] 35240.3494 -8039.7606 [35,] 12204.0183 35240.3494 [36,] 63254.1407 12204.0183 [37,] 4631.0094 63254.1407 [38,] 10447.7702 4631.0094 [39,] 22615.0403 10447.7702 [40,] 28157.0994 22615.0403 [41,] -11405.4710 28157.0994 [42,] 38791.0335 -11405.4710 [43,] -7184.4924 38791.0335 [44,] 11983.5966 -7184.4924 [45,] 6316.7593 11983.5966 [46,] 28270.6772 6316.7593 [47,] -13295.5802 28270.6772 [48,] 10482.4149 -13295.5802 [49,] -14729.3429 10482.4149 [50,] 4704.0204 -14729.3429 [51,] 2929.9262 4704.0204 [52,] 3766.2154 2929.9262 [53,] -3456.4141 3766.2154 [54,] 8340.3613 -3456.4141 [55,] 2116.4760 8340.3613 [56,] 8515.2975 2116.4760 [57,] -38121.0786 8515.2975 [58,] 16264.8676 -38121.0786 [59,] -5798.8640 16264.8676 [60,] 37198.8631 -5798.8640 [61,] 15814.9868 37198.8631 [62,] 6434.1293 15814.9868 [63,] -2942.0762 6434.1293 [64,] 17037.4178 -2942.0762 [65,] 17453.7960 17037.4178 [66,] -24119.4407 17453.7960 [67,] 7724.7447 -24119.4407 [68,] -30510.9148 7724.7447 [69,] -5690.2262 -30510.9148 [70,] -3406.2300 -5690.2262 [71,] -4632.0699 -3406.2300 [72,] 8754.9638 -4632.0699 [73,] -18561.0248 8754.9638 [74,] -29494.4731 -18561.0248 [75,] -8418.3344 -29494.4731 [76,] 22269.9918 -8418.3344 [77,] -22059.4684 22269.9918 [78,] -20696.1532 -22059.4684 [79,] -7426.4898 -20696.1532 [80,] 6775.3067 -7426.4898 [81,] 10288.9367 6775.3067 [82,] 26003.9106 10288.9367 [83,] -25091.8633 26003.9106 [84,] -3560.2548 -25091.8633 [85,] -2637.1774 -3560.2548 [86,] -25430.4034 -2637.1774 [87,] 23313.7140 -25430.4034 [88,] 51793.2645 23313.7140 [89,] -51351.9589 51793.2645 [90,] 12306.4193 -51351.9589 [91,] -34688.9740 12306.4193 [92,] 505.5128 -34688.9740 [93,] -5237.7048 505.5128 [94,] -2215.5021 -5237.7048 [95,] -4712.6522 -2215.5021 [96,] -18490.5223 -4712.6522 [97,] 2227.7782 -18490.5223 [98,] 18608.1496 2227.7782 [99,] -13152.0604 18608.1496 [100,] 12062.4723 -13152.0604 [101,] -1994.9985 12062.4723 [102,] 14665.0057 -1994.9985 [103,] -30309.2632 14665.0057 [104,] 7407.6055 -30309.2632 [105,] -32863.8349 7407.6055 [106,] 8970.1203 -32863.8349 [107,] 2803.5352 8970.1203 [108,] -2381.0536 2803.5352 [109,] -28121.7553 -2381.0536 [110,] 26925.0828 -28121.7553 [111,] -12133.2704 26925.0828 [112,] -7581.3311 -12133.2704 [113,] -8496.5333 -7581.3311 [114,] -6504.7309 -8496.5333 [115,] -24489.7078 -6504.7309 [116,] 30730.6546 -24489.7078 [117,] -2138.8309 30730.6546 [118,] 17814.1124 -2138.8309 [119,] -22120.8230 17814.1124 [120,] 33321.0728 -22120.8230 [121,] -17319.6347 33321.0728 [122,] -21633.1035 -17319.6347 [123,] -19573.9422 -21633.1035 [124,] -4793.2446 -19573.9422 [125,] -3117.6277 -4793.2446 [126,] 5198.1147 -3117.6277 [127,] -12894.5646 5198.1147 [128,] 33278.8205 -12894.5646 [129,] 18338.8080 33278.8205 [130,] 7294.3401 18338.8080 [131,] 6066.6493 7294.3401 [132,] 13858.7918 6066.6493 [133,] -9726.7798 13858.7918 [134,] 8146.8281 -9726.7798 [135,] -21887.2571 8146.8281 [136,] -9691.2570 -21887.2571 [137,] -13940.7718 -9691.2570 [138,] 21216.1001 -13940.7718 [139,] -16403.2929 21216.1001 [140,] -31864.4543 -16403.2929 [141,] 10928.2487 -31864.4543 [142,] -46945.5871 10928.2487 [143,] -15027.7717 -46945.5871 [144,] 3835.2840 -15027.7717 [145,] -25934.0971 3835.2840 [146,] 2908.0321 -25934.0971 [147,] -25827.3123 2908.0321 [148,] -5911.6923 -25827.3123 [149,] -2878.7607 -5911.6923 [150,] -5911.6923 -2878.7607 [151,] -5911.6923 -5911.6923 [152,] -5911.6923 -5911.6923 [153,] -5911.6923 -5911.6923 [154,] -6339.6696 -5911.6923 [155,] 11958.2717 -6339.6696 [156,] -5911.6923 11958.2717 [157,] -5911.6923 -5911.6923 [158,] -6039.5374 -5911.6923 [159,] 5405.1283 -6039.5374 [160,] -1279.3506 5405.1283 [161,] -13409.4341 -1279.3506 [162,] -5911.6923 -13409.4341 [163,] -9561.3288 -5911.6923 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 9542.2005 -1318.0848 2 6508.6032 9542.2005 3 -20463.7313 6508.6032 4 5391.1892 -20463.7313 5 -6921.7207 5391.1892 6 40979.1360 -6921.7207 7 -8581.5765 40979.1360 8 -9414.7261 -8581.5765 9 4624.7485 -9414.7261 10 26250.7254 4624.7485 11 -11544.8581 26250.7254 12 1409.9549 -11544.8581 13 -21002.3395 1409.9549 14 5227.5594 -21002.3395 15 20903.6265 5227.5594 16 -5196.6340 20903.6265 17 1344.5912 -5196.6340 18 -21221.9767 1344.5912 19 -15784.6574 -21221.9767 20 6166.5319 -15784.6574 21 18975.1434 6166.5319 22 7877.9383 18975.1434 23 -24985.9022 7877.9383 24 -17075.1572 -24985.9022 25 9934.9706 -17075.1572 26 17338.6900 9934.9706 27 12863.0132 17338.6900 28 3249.1634 12863.0132 29 32197.9261 3249.1634 30 32937.1250 32197.9261 31 -20540.4172 32937.1250 32 42728.3367 -20540.4172 33 -8039.7606 42728.3367 34 35240.3494 -8039.7606 35 12204.0183 35240.3494 36 63254.1407 12204.0183 37 4631.0094 63254.1407 38 10447.7702 4631.0094 39 22615.0403 10447.7702 40 28157.0994 22615.0403 41 -11405.4710 28157.0994 42 38791.0335 -11405.4710 43 -7184.4924 38791.0335 44 11983.5966 -7184.4924 45 6316.7593 11983.5966 46 28270.6772 6316.7593 47 -13295.5802 28270.6772 48 10482.4149 -13295.5802 49 -14729.3429 10482.4149 50 4704.0204 -14729.3429 51 2929.9262 4704.0204 52 3766.2154 2929.9262 53 -3456.4141 3766.2154 54 8340.3613 -3456.4141 55 2116.4760 8340.3613 56 8515.2975 2116.4760 57 -38121.0786 8515.2975 58 16264.8676 -38121.0786 59 -5798.8640 16264.8676 60 37198.8631 -5798.8640 61 15814.9868 37198.8631 62 6434.1293 15814.9868 63 -2942.0762 6434.1293 64 17037.4178 -2942.0762 65 17453.7960 17037.4178 66 -24119.4407 17453.7960 67 7724.7447 -24119.4407 68 -30510.9148 7724.7447 69 -5690.2262 -30510.9148 70 -3406.2300 -5690.2262 71 -4632.0699 -3406.2300 72 8754.9638 -4632.0699 73 -18561.0248 8754.9638 74 -29494.4731 -18561.0248 75 -8418.3344 -29494.4731 76 22269.9918 -8418.3344 77 -22059.4684 22269.9918 78 -20696.1532 -22059.4684 79 -7426.4898 -20696.1532 80 6775.3067 -7426.4898 81 10288.9367 6775.3067 82 26003.9106 10288.9367 83 -25091.8633 26003.9106 84 -3560.2548 -25091.8633 85 -2637.1774 -3560.2548 86 -25430.4034 -2637.1774 87 23313.7140 -25430.4034 88 51793.2645 23313.7140 89 -51351.9589 51793.2645 90 12306.4193 -51351.9589 91 -34688.9740 12306.4193 92 505.5128 -34688.9740 93 -5237.7048 505.5128 94 -2215.5021 -5237.7048 95 -4712.6522 -2215.5021 96 -18490.5223 -4712.6522 97 2227.7782 -18490.5223 98 18608.1496 2227.7782 99 -13152.0604 18608.1496 100 12062.4723 -13152.0604 101 -1994.9985 12062.4723 102 14665.0057 -1994.9985 103 -30309.2632 14665.0057 104 7407.6055 -30309.2632 105 -32863.8349 7407.6055 106 8970.1203 -32863.8349 107 2803.5352 8970.1203 108 -2381.0536 2803.5352 109 -28121.7553 -2381.0536 110 26925.0828 -28121.7553 111 -12133.2704 26925.0828 112 -7581.3311 -12133.2704 113 -8496.5333 -7581.3311 114 -6504.7309 -8496.5333 115 -24489.7078 -6504.7309 116 30730.6546 -24489.7078 117 -2138.8309 30730.6546 118 17814.1124 -2138.8309 119 -22120.8230 17814.1124 120 33321.0728 -22120.8230 121 -17319.6347 33321.0728 122 -21633.1035 -17319.6347 123 -19573.9422 -21633.1035 124 -4793.2446 -19573.9422 125 -3117.6277 -4793.2446 126 5198.1147 -3117.6277 127 -12894.5646 5198.1147 128 33278.8205 -12894.5646 129 18338.8080 33278.8205 130 7294.3401 18338.8080 131 6066.6493 7294.3401 132 13858.7918 6066.6493 133 -9726.7798 13858.7918 134 8146.8281 -9726.7798 135 -21887.2571 8146.8281 136 -9691.2570 -21887.2571 137 -13940.7718 -9691.2570 138 21216.1001 -13940.7718 139 -16403.2929 21216.1001 140 -31864.4543 -16403.2929 141 10928.2487 -31864.4543 142 -46945.5871 10928.2487 143 -15027.7717 -46945.5871 144 3835.2840 -15027.7717 145 -25934.0971 3835.2840 146 2908.0321 -25934.0971 147 -25827.3123 2908.0321 148 -5911.6923 -25827.3123 149 -2878.7607 -5911.6923 150 -5911.6923 -2878.7607 151 -5911.6923 -5911.6923 152 -5911.6923 -5911.6923 153 -5911.6923 -5911.6923 154 -6339.6696 -5911.6923 155 11958.2717 -6339.6696 156 -5911.6923 11958.2717 157 -5911.6923 -5911.6923 158 -6039.5374 -5911.6923 159 5405.1283 -6039.5374 160 -1279.3506 5405.1283 161 -13409.4341 -1279.3506 162 -5911.6923 -13409.4341 163 -9561.3288 -5911.6923 > 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/wessaorg/rcomp/tmp/7ppyd1321971799.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/wessaorg/rcomp/tmp/8cnzs1321971799.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/wessaorg/rcomp/tmp/9e0wm1321971799.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/wessaorg/rcomp/tmp/10c3gc1321971799.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/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/11itav1321971799.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12ybmu1321971799.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/135usd1321971799.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/14wnvl1321971799.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/wessaorg/rcomp/tmp/15duyp1321971799.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/wessaorg/rcomp/tmp/16hyhv1321971799.tab") + } > > try(system("convert tmp/1fr031321971799.ps tmp/1fr031321971799.png",intern=TRUE)) character(0) > try(system("convert tmp/28c7t1321971799.ps tmp/28c7t1321971799.png",intern=TRUE)) character(0) > try(system("convert tmp/30ecx1321971799.ps tmp/30ecx1321971799.png",intern=TRUE)) character(0) > try(system("convert tmp/4c50r1321971799.ps tmp/4c50r1321971799.png",intern=TRUE)) character(0) > try(system("convert tmp/5wt8x1321971799.ps tmp/5wt8x1321971799.png",intern=TRUE)) character(0) > try(system("convert tmp/681vh1321971799.ps tmp/681vh1321971799.png",intern=TRUE)) character(0) > try(system("convert tmp/7ppyd1321971799.ps tmp/7ppyd1321971799.png",intern=TRUE)) character(0) > try(system("convert tmp/8cnzs1321971799.ps tmp/8cnzs1321971799.png",intern=TRUE)) character(0) > try(system("convert tmp/9e0wm1321971799.ps tmp/9e0wm1321971799.png",intern=TRUE)) character(0) > try(system("convert tmp/10c3gc1321971799.ps tmp/10c3gc1321971799.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.755 0.512 5.388