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(65 + ,146455 + ,1 + ,95556 + ,114468 + ,127 + ,128 + ,54 + ,84944 + ,4 + ,54565 + ,88594 + ,90 + ,89 + ,58 + ,113337 + ,9 + ,63016 + ,74151 + ,68 + ,68 + ,75 + ,128655 + ,2 + ,79774 + ,77921 + ,111 + ,108 + ,41 + ,74398 + ,1 + ,31258 + ,53212 + ,51 + ,51 + ,0 + ,35523 + ,2 + ,52491 + ,34956 + ,33 + ,33 + ,111 + ,293403 + ,0 + ,91256 + ,149703 + ,123 + ,119 + ,1 + ,32750 + ,0 + ,22807 + ,6853 + ,5 + ,5 + ,36 + ,106539 + ,5 + ,77411 + ,58907 + ,63 + ,63 + ,60 + ,130539 + ,0 + ,48821 + ,67067 + ,66 + ,66 + ,63 + ,154991 + ,0 + ,52295 + ,110563 + ,99 + ,98 + ,71 + ,126683 + ,7 + ,63262 + ,58126 + ,72 + ,71 + ,38 + ,100672 + ,6 + ,50466 + ,57113 + ,55 + ,55 + ,76 + ,179562 + ,3 + ,62932 + ,77993 + ,116 + ,116 + ,61 + ,125971 + ,4 + ,38439 + ,68091 + ,71 + ,71 + ,125 + ,234509 + ,0 + ,70817 + ,124676 + ,125 + ,120 + ,84 + ,158980 + ,4 + ,105965 + ,109522 + ,123 + ,122 + ,69 + ,184217 + ,3 + ,73795 + ,75865 + ,74 + ,74 + ,77 + ,107342 + ,0 + ,82043 + 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,108281 + ,122 + ,122 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,203 + ,0 + ,0 + ,0 + ,0 + ,0 + ,7 + ,7199 + ,0 + ,1644 + ,4245 + ,6 + ,6 + ,12 + ,46660 + ,0 + ,6179 + ,21509 + ,13 + ,13 + ,0 + ,17547 + ,0 + ,3926 + ,7670 + ,3 + ,3 + ,37 + ,73567 + ,0 + ,23238 + ,10641 + ,18 + ,18 + ,0 + ,969 + ,0 + ,0 + ,0 + ,0 + ,0 + ,39 + ,101060 + ,2 + ,49288 + ,41243 + ,49 + ,48) + ,dim=c(7 + ,164) + ,dimnames=list(c('BlogdComputations' + ,'TotalTime' + ,'Shared' + ,'Caracters' + ,'Writing' + ,'Hyperlink' + ,'Blogs') + ,1:164)) > y <- array(NA,dim=c(7,164),dimnames=list(c('BlogdComputations','TotalTime','Shared','Caracters','Writing','Hyperlink','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 = '1' > #'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 BlogdComputations TotalTime Shared Caracters Writing Hyperlink Blogs 1 65 146455 1 95556 114468 127 128 2 54 84944 4 54565 88594 90 89 3 58 113337 9 63016 74151 68 68 4 75 128655 2 79774 77921 111 108 5 41 74398 1 31258 53212 51 51 6 0 35523 2 52491 34956 33 33 7 111 293403 0 91256 149703 123 119 8 1 32750 0 22807 6853 5 5 9 36 106539 5 77411 58907 63 63 10 60 130539 0 48821 67067 66 66 11 63 154991 0 52295 110563 99 98 12 71 126683 7 63262 58126 72 71 13 38 100672 6 50466 57113 55 55 14 76 179562 3 62932 77993 116 116 15 61 125971 4 38439 68091 71 71 16 125 234509 0 70817 124676 125 120 17 84 158980 4 105965 109522 123 122 18 69 184217 3 73795 75865 74 74 19 77 107342 0 82043 79746 116 111 20 95 141371 5 74349 77844 117 103 21 78 154730 0 82204 98681 98 98 22 76 264020 1 55709 105531 101 100 23 40 90938 3 37137 51428 43 42 24 81 101324 5 70780 65703 103 100 25 102 130232 0 55027 72562 107 105 26 70 137793 0 56699 81728 77 77 27 75 161678 4 65911 95580 87 83 28 93 151503 0 56316 98278 99 98 29 42 105324 0 26982 46629 46 46 30 95 175914 0 54628 115189 96 95 31 87 181853 3 96750 124865 92 91 32 44 114928 4 53009 59392 96 91 33 84 190410 1 64664 127818 96 94 34 28 61499 4 36990 17821 15 15 35 87 223004 1 85224 154076 147 137 36 71 167131 0 37048 64881 56 56 37 68 233482 0 59635 136506 81 78 38 50 121185 2 42051 66524 69 68 39 30 78776 1 26998 45988 34 34 40 86 188967 2 63717 107445 98 94 41 75 199512 8 55071 102772 82 82 42 46 102531 5 40001 46657 64 63 43 52 118958 3 54506 97563 61 58 44 31 68948 4 35838 36663 45 43 45 30 93125 1 50838 55369 37 36 46 70 277108 2 86997 77921 64 64 47 20 78800 2 33032 56968 21 21 48 84 157250 0 61704 77519 104 104 49 81 210554 6 117986 129805 126 124 50 79 127324 3 56733 72761 104 101 51 70 114397 0 55064 81278 87 85 52 8 24188 0 5950 15049 7 7 53 67 246209 6 84607 113935 130 124 54 21 65029 5 32551 25109 21 21 55 30 98030 3 31701 45824 35 35 56 70 173587 1 71170 89644 97 95 57 87 172684 5 101773 109011 103 102 58 87 191381 5 101653 134245 210 212 59 112 191276 0 81493 136692 151 141 60 54 134043 9 55901 50741 57 54 61 96 233406 6 109104 149510 117 117 62 93 195304 6 114425 147888 152 145 63 49 127619 5 36311 54987 52 50 64 49 162810 6 70027 74467 83 80 65 38 129100 2 73713 100033 87 87 66 64 108715 0 40671 85505 80 78 67 62 106469 3 89041 62426 88 86 68 66 142069 8 57231 82932 83 82 69 98 143937 2 78792 79169 140 139 70 97 84256 5 59155 65469 76 75 71 56 118807 11 55827 63572 70 70 72 22 69471 6 22618 23824 26 25 73 51 122433 5 58425 73831 66 66 74 56 131122 1 65724 63551 89 89 75 94 94763 0 56979 56756 100 99 76 98 188780 3 72369 81399 98 98 77 76 191467 3 79194 117881 109 104 78 57 105615 6 202316 70711 51 48 79 75 89318 1 44970 50495 82 81 80 48 107335 0 49319 53845 65 64 81 48 98599 1 36252 51390 46 44 82 109 260646 0 75741 104953 104 104 83 27 131876 5 38417 65983 36 36 84 83 119291 2 64102 76839 123 120 85 49 80953 0 56622 55792 59 58 86 24 99768 0 15430 25155 27 27 87 43 84572 5 72571 55291 84 84 88 44 202373 1 67271 84279 61 56 89 49 166790 0 43460 99692 46 46 90 106 99946 1 99501 59633 125 119 91 42 116900 1 28340 63249 58 57 92 108 142146 2 76013 82928 152 139 93 27 99246 4 37361 50000 52 51 94 79 156833 1 48204 69455 85 85 95 49 175078 4 76168 84068 95 91 96 64 130533 0 85168 76195 78 79 97 75 142339 2 125410 114634 144 142 98 115 176789 0 123328 139357 149 149 99 92 181379 7 83038 110044 101 96 100 106 228548 7 120087 155118 205 198 101 73 142141 6 91939 83061 61 61 102 105 167845 0 103646 127122 145 145 103 30 103012 0 29467 45653 28 26 104 13 43287 4 43750 19630 49 49 105 69 125366 4 34497 67229 68 68 106 72 118372 0 66477 86060 142 145 107 80 135171 0 71181 88003 82 82 108 106 175568 0 74482 95815 105 102 109 28 74112 0 174949 85499 52 52 110 70 88817 0 46765 27220 56 56 111 51 164767 4 90257 109882 81 80 112 90 141933 0 51370 72579 100 99 113 12 22938 0 1168 5841 11 11 114 84 115199 0 51360 68369 87 87 115 23 61857 4 25162 24610 31 28 116 57 91185 0 21067 30995 67 67 117 84 213765 1 58233 150662 150 150 118 4 21054 0 855 6622 4 4 119 56 167105 5 85903 93694 75 71 120 18 31414 0 14116 13155 39 39 121 86 178863 1 57637 111908 88 87 122 39 126681 7 94137 57550 67 66 123 16 64320 5 62147 16356 24 23 124 18 67746 2 62832 40174 58 56 125 16 38214 0 8773 13983 16 16 126 42 90961 1 63785 52316 49 49 127 75 181510 0 65196 99585 109 108 128 30 116775 0 73087 86271 124 112 129 104 223914 2 72631 131012 115 110 130 121 185139 0 86281 130274 128 126 131 106 242879 2 162365 159051 159 155 132 57 139144 0 56530 76506 75 75 133 28 75812 0 35606 49145 30 30 134 56 178218 4 70111 66398 83 78 135 81 246834 4 92046 127546 135 135 136 2 50999 8 63989 6802 8 8 137 88 223842 0 104911 99509 115 114 138 41 93577 4 43448 43106 60 60 139 83 155383 0 60029 108303 99 99 140 55 111664 1 38650 64167 98 98 141 3 75426 0 47261 8579 36 33 142 54 243551 9 73586 97811 93 93 143 89 136548 0 83042 84365 158 157 144 41 173260 3 37238 10901 16 15 145 94 185039 7 63958 91346 100 98 146 101 67507 5 78956 33660 49 49 147 70 139350 2 99518 93634 89 88 148 111 172964 1 111436 109348 153 151 149 0 0 9 0 0 0 0 150 4 14688 0 6023 7953 5 5 151 0 98 0 0 0 0 0 152 0 455 0 0 0 0 0 153 0 0 1 0 0 0 0 154 0 0 0 0 0 0 0 155 42 128066 2 42564 63538 80 80 156 97 176460 1 38885 108281 122 122 157 0 0 0 0 0 0 0 158 0 203 0 0 0 0 0 159 7 7199 0 1644 4245 6 6 160 12 46660 0 6179 21509 13 13 161 0 17547 0 3926 7670 3 3 162 37 73567 0 23238 10641 18 18 163 0 969 0 0 0 0 0 164 39 101060 2 49288 41243 49 48 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) TotalTime Shared Caracters Writing Hyperlink 4.619e+00 1.573e-04 -8.440e-01 3.196e-05 -3.734e-06 1.969e-01 Blogs 2.538e-01 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -47.846 -8.842 -0.905 8.634 65.498 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4.619e+00 2.918e+00 1.583 0.115389 TotalTime 1.573e-04 3.977e-05 3.956 0.000115 *** Shared -8.440e-01 4.909e-01 -1.719 0.087570 . Caracters 3.196e-05 5.614e-05 0.569 0.569973 Writing -3.734e-06 8.566e-05 -0.044 0.965280 Hyperlink 1.969e-01 5.182e-01 0.380 0.704459 Blogs 2.538e-01 5.290e-01 0.480 0.632097 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 15.46 on 157 degrees of freedom Multiple R-squared: 0.7787, Adjusted R-squared: 0.7702 F-statistic: 92.07 on 6 and 157 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,] 0.003825078 7.650156e-03 9.961749e-01 [2,] 0.027485424 5.497085e-02 9.725146e-01 [3,] 0.008962119 1.792424e-02 9.910379e-01 [4,] 0.014907944 2.981589e-02 9.850921e-01 [5,] 0.083005569 1.660111e-01 9.169944e-01 [6,] 0.044568185 8.913637e-02 9.554318e-01 [7,] 0.039832434 7.966487e-02 9.601676e-01 [8,] 0.033398630 6.679726e-02 9.666014e-01 [9,] 0.021094564 4.218913e-02 9.789054e-01 [10,] 0.010999655 2.199931e-02 9.890003e-01 [11,] 0.026222542 5.244508e-02 9.737775e-01 [12,] 0.027376337 5.475267e-02 9.726237e-01 [13,] 0.082102439 1.642049e-01 9.178976e-01 [14,] 0.056434395 1.128688e-01 9.435656e-01 [15,] 0.054594881 1.091898e-01 9.454051e-01 [16,] 0.134991731 2.699835e-01 8.650083e-01 [17,] 0.125479612 2.509592e-01 8.745204e-01 [18,] 0.092994102 1.859882e-01 9.070059e-01 [19,] 0.107687538 2.153751e-01 8.923125e-01 [20,] 0.078442751 1.568855e-01 9.215572e-01 [21,] 0.080861135 1.617223e-01 9.191389e-01 [22,] 0.085573521 1.711470e-01 9.144265e-01 [23,] 0.178198735 3.563975e-01 8.218013e-01 [24,] 0.143079632 2.861593e-01 8.569204e-01 [25,] 0.136523003 2.730460e-01 8.634770e-01 [26,] 0.270967221 5.419344e-01 7.290328e-01 [27,] 0.249246269 4.984925e-01 7.507537e-01 [28,] 0.238112771 4.762255e-01 7.618872e-01 [29,] 0.202143646 4.042873e-01 7.978564e-01 [30,] 0.163824677 3.276494e-01 8.361753e-01 [31,] 0.135084983 2.701700e-01 8.649150e-01 [32,] 0.107941470 2.158829e-01 8.920585e-01 [33,] 0.086148074 1.722961e-01 9.138519e-01 [34,] 0.066810223 1.336204e-01 9.331898e-01 [35,] 0.051487052 1.029741e-01 9.485129e-01 [36,] 0.039199524 7.839905e-02 9.608005e-01 [37,] 0.031401979 6.280396e-02 9.685980e-01 [38,] 0.023275919 4.655184e-02 9.767241e-01 [39,] 0.017274622 3.454924e-02 9.827254e-01 [40,] 0.014796344 2.959269e-02 9.852037e-01 [41,] 0.010964710 2.192942e-02 9.890353e-01 [42,] 0.008225090 1.645018e-02 9.917749e-01 [43,] 0.005791559 1.158312e-02 9.942084e-01 [44,] 0.029488501 5.897700e-02 9.705115e-01 [45,] 0.021761762 4.352352e-02 9.782382e-01 [46,] 0.016356173 3.271235e-02 9.836438e-01 [47,] 0.012520972 2.504194e-02 9.874790e-01 [48,] 0.011579179 2.315836e-02 9.884208e-01 [49,] 0.063705496 1.274110e-01 9.362945e-01 [50,] 0.054282724 1.085654e-01 9.457173e-01 [51,] 0.045165333 9.033067e-02 9.548347e-01 [52,] 0.036505501 7.301100e-02 9.634945e-01 [53,] 0.029587727 5.917545e-02 9.704123e-01 [54,] 0.022576254 4.515251e-02 9.774237e-01 [55,] 0.022733204 4.546641e-02 9.772668e-01 [56,] 0.037428402 7.485680e-02 9.625716e-01 [57,] 0.029511414 5.902283e-02 9.704886e-01 [58,] 0.022774050 4.554810e-02 9.772260e-01 [59,] 0.018245780 3.649156e-02 9.817542e-01 [60,] 0.015934506 3.186901e-02 9.840655e-01 [61,] 0.139247020 2.784940e-01 8.607530e-01 [62,] 0.121256229 2.425125e-01 8.787438e-01 [63,] 0.101568140 2.031363e-01 8.984319e-01 [64,] 0.082229325 1.644587e-01 9.177707e-01 [65,] 0.072332322 1.446646e-01 9.276677e-01 [66,] 0.117358006 2.347160e-01 8.826420e-01 [67,] 0.135851265 2.717025e-01 8.641487e-01 [68,] 0.115718880 2.314378e-01 8.842811e-01 [69,] 0.109625939 2.192519e-01 8.903741e-01 [70,] 0.117806569 2.356131e-01 8.821934e-01 [71,] 0.099014711 1.980294e-01 9.009853e-01 [72,] 0.083893871 1.677877e-01 9.161061e-01 [73,] 0.082030237 1.640605e-01 9.179698e-01 [74,] 0.074174160 1.483483e-01 9.258258e-01 [75,] 0.060514341 1.210287e-01 9.394857e-01 [76,] 0.048387009 9.677402e-02 9.516130e-01 [77,] 0.042890032 8.578006e-02 9.571100e-01 [78,] 0.037959967 7.591993e-02 9.620400e-01 [79,] 0.045551232 9.110246e-02 9.544488e-01 [80,] 0.035873032 7.174606e-02 9.641270e-01 [81,] 0.065473053 1.309461e-01 9.345269e-01 [82,] 0.054853364 1.097067e-01 9.451466e-01 [83,] 0.090924322 1.818486e-01 9.090757e-01 [84,] 0.088896756 1.777935e-01 9.111032e-01 [85,] 0.078596481 1.571930e-01 9.214035e-01 [86,] 0.099543668 1.990873e-01 9.004563e-01 [87,] 0.080739822 1.614796e-01 9.192602e-01 [88,] 0.082925436 1.658509e-01 9.170746e-01 [89,] 0.076310483 1.526210e-01 9.236895e-01 [90,] 0.094389089 1.887782e-01 9.056109e-01 [91,] 0.096535712 1.930714e-01 9.034643e-01 [92,] 0.110346824 2.206936e-01 8.896532e-01 [93,] 0.092735968 1.854719e-01 9.072640e-01 [94,] 0.076291194 1.525824e-01 9.237088e-01 [95,] 0.081818644 1.636373e-01 9.181814e-01 [96,] 0.083368034 1.667361e-01 9.166320e-01 [97,] 0.099928252 1.998565e-01 9.000717e-01 [98,] 0.095869126 1.917383e-01 9.041309e-01 [99,] 0.151593056 3.031861e-01 8.484069e-01 [100,] 0.176574060 3.531481e-01 8.234259e-01 [101,] 0.223330273 4.466605e-01 7.766697e-01 [102,] 0.231900928 4.638019e-01 7.680991e-01 [103,] 0.257820060 5.156401e-01 7.421799e-01 [104,] 0.221632737 4.432655e-01 7.783673e-01 [105,] 0.255732207 5.114644e-01 7.442678e-01 [106,] 0.226855060 4.537101e-01 7.731449e-01 [107,] 0.221438114 4.428762e-01 7.785619e-01 [108,] 0.280215078 5.604302e-01 7.197849e-01 [109,] 0.244475866 4.889517e-01 7.555241e-01 [110,] 0.208459815 4.169196e-01 7.915402e-01 [111,] 0.181673554 3.633471e-01 8.183264e-01 [112,] 0.163935019 3.278700e-01 8.360650e-01 [113,] 0.158353768 3.167075e-01 8.416462e-01 [114,] 0.134583295 2.691666e-01 8.654167e-01 [115,] 0.163824512 3.276490e-01 8.361755e-01 [116,] 0.133162014 2.663240e-01 8.668380e-01 [117,] 0.107898771 2.157975e-01 8.921012e-01 [118,] 0.088085180 1.761704e-01 9.119148e-01 [119,] 0.190889034 3.817781e-01 8.091110e-01 [120,] 0.196332795 3.926656e-01 8.036672e-01 [121,] 0.323274257 6.465485e-01 6.767257e-01 [122,] 0.305495718 6.109914e-01 6.945043e-01 [123,] 0.256447504 5.128950e-01 7.435525e-01 [124,] 0.211807256 4.236145e-01 7.881927e-01 [125,] 0.175257142 3.505143e-01 8.247429e-01 [126,] 0.220801004 4.416020e-01 7.791990e-01 [127,] 0.343405600 6.868112e-01 6.565944e-01 [128,] 0.309390978 6.187820e-01 6.906090e-01 [129,] 0.270568713 5.411374e-01 7.294313e-01 [130,] 0.228155875 4.563117e-01 7.718441e-01 [131,] 0.189623065 3.792461e-01 8.103769e-01 [132,] 0.277929165 5.558583e-01 7.220708e-01 [133,] 0.703963803 5.920724e-01 2.960362e-01 [134,] 0.720685755 5.586285e-01 2.793142e-01 [135,] 0.671457990 6.570840e-01 3.285420e-01 [136,] 0.890585390 2.188292e-01 1.094146e-01 [137,] 0.994339390 1.132122e-02 5.660610e-03 [138,] 0.998627075 2.745851e-03 1.372925e-03 [139,] 0.999910212 1.795762e-04 8.978808e-05 [140,] 0.999886937 2.261258e-04 1.130629e-04 [141,] 0.999998939 2.121043e-06 1.060522e-06 [142,] 0.999989722 2.055520e-05 1.027760e-05 [143,] 0.999913880 1.722398e-04 8.611991e-05 [144,] 0.999999541 9.185387e-07 4.592693e-07 [145,] 0.999978260 4.348031e-05 2.174015e-05 > postscript(file="/var/wessaorg/rcomp/tmp/1i1ph1321542431.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2ovja1321542431.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3gd3y1321542431.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4wo561321542431.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/546xq1321542431.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 -21.934823483 -2.329126551 10.761664826 0.303669151 1.734581785 6 7 8 9 10 -24.939949476 3.444650293 -11.728073999 -11.808230869 3.788562952 11 12 13 14 15 -11.626213583 18.357457576 -3.581137918 -8.337122467 6.965143313 16 17 18 19 20 26.621797658 -0.414138824 2.505015743 2.156945752 21.095486051 21 22 23 24 25 2.611443748 -15.963584489 3.485330010 16.982622600 27.687673290 26 27 28 29 30 7.492592674 8.376262806 18.748106083 -0.608919478 18.377085289 31 32 33 34 35 12.467292367 -18.794549007 5.920750463 9.205666305 -17.721654028 36 37 38 39 40 13.907231693 -10.491594629 -3.935973952 -2.182985877 8.552035626 41 42 43 44 45 7.411960143 -0.224677082 3.089331804 -1.872591576 -6.265603838 46 47 48 49 50 -7.859423880 -5.635499496 6.086977943 -11.246455445 9.229120788 51 52 53 54 55 7.224233647 -3.713146981 -30.635682248 -0.040832996 -4.125670619 56 57 58 59 60 -6.233933854 10.420131508 -41.409856420 9.677223906 9.363336673 61 62 63 64 65 4.064833947 -7.115498272 4.639857112 -14.775261759 -26.434440251 66 67 68 69 70 5.748855671 1.396214304 7.108600691 7.357631945 47.699862853 71 72 73 74 75 8.877920706 -0.582741946 0.001844499 -10.378793571 28.047257737 76 77 78 79 80 20.036514529 -6.156984975 12.402686613 19.220939189 -3.921893716 81 82 83 84 85 7.522021441 14.474928336 -11.352354974 4.865192636 3.706495611 86 87 88 89 90 -8.882447445 -10.676075300 -19.671159739 -3.607018040 28.728731916 91 92 93 94 95 -6.722074726 15.379058920 -14.046529362 10.961301797 -23.708238335 96 97 98 99 100 0.999631261 -18.297600273 11.992754114 18.259353718 -22.542675115 101 102 103 104 105 20.962224661 5.785998645 -3.708046359 -18.462564536 16.535210033 106 107 108 109 110 -17.805646830 15.212107026 25.175787230 -16.987211755 24.776055480 111 112 113 114 115 -14.891519849 16.865028965 -1.200864011 20.660701443 -1.897207101 116 117 118 119 120 7.281244958 -22.308104251 -5.736626817 -5.871111907 -9.540540808 121 122 123 124 125 13.254327469 -12.377692264 -7.006282672 -23.080252141 -2.070214351 126 127 128 129 130 -0.012618675 -8.758467057 -47.845624643 13.449203564 27.801697592 131 132 133 134 135 -10.382471293 -4.832631623 -3.021181226 -11.412373927 -22.385014529 136 137 138 139 140 -9.515856409 -6.391864411 -3.234415261 7.802709508 -11.506432514 141 142 143 144 145 -30.427381909 -25.240183849 -10.396994191 3.549213588 19.912978560 146 147 148 149 150 65.498327445 2.456748810 8.411198890 2.976532971 -5.346068566 151 152 153 154 155 -4.634495600 -4.690657415 -3.775121787 -4.619078632 -18.257537527 156 157 158 159 160 9.640437802 -4.619078632 -4.651013781 -1.492518083 -5.935775260 161 162 163 164 -8.828457452 11.991967645 -4.771517843 -3.081617679 > postscript(file="/var/wessaorg/rcomp/tmp/6hf8w1321542431.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 -21.934823483 NA 1 -2.329126551 -21.934823483 2 10.761664826 -2.329126551 3 0.303669151 10.761664826 4 1.734581785 0.303669151 5 -24.939949476 1.734581785 6 3.444650293 -24.939949476 7 -11.728073999 3.444650293 8 -11.808230869 -11.728073999 9 3.788562952 -11.808230869 10 -11.626213583 3.788562952 11 18.357457576 -11.626213583 12 -3.581137918 18.357457576 13 -8.337122467 -3.581137918 14 6.965143313 -8.337122467 15 26.621797658 6.965143313 16 -0.414138824 26.621797658 17 2.505015743 -0.414138824 18 2.156945752 2.505015743 19 21.095486051 2.156945752 20 2.611443748 21.095486051 21 -15.963584489 2.611443748 22 3.485330010 -15.963584489 23 16.982622600 3.485330010 24 27.687673290 16.982622600 25 7.492592674 27.687673290 26 8.376262806 7.492592674 27 18.748106083 8.376262806 28 -0.608919478 18.748106083 29 18.377085289 -0.608919478 30 12.467292367 18.377085289 31 -18.794549007 12.467292367 32 5.920750463 -18.794549007 33 9.205666305 5.920750463 34 -17.721654028 9.205666305 35 13.907231693 -17.721654028 36 -10.491594629 13.907231693 37 -3.935973952 -10.491594629 38 -2.182985877 -3.935973952 39 8.552035626 -2.182985877 40 7.411960143 8.552035626 41 -0.224677082 7.411960143 42 3.089331804 -0.224677082 43 -1.872591576 3.089331804 44 -6.265603838 -1.872591576 45 -7.859423880 -6.265603838 46 -5.635499496 -7.859423880 47 6.086977943 -5.635499496 48 -11.246455445 6.086977943 49 9.229120788 -11.246455445 50 7.224233647 9.229120788 51 -3.713146981 7.224233647 52 -30.635682248 -3.713146981 53 -0.040832996 -30.635682248 54 -4.125670619 -0.040832996 55 -6.233933854 -4.125670619 56 10.420131508 -6.233933854 57 -41.409856420 10.420131508 58 9.677223906 -41.409856420 59 9.363336673 9.677223906 60 4.064833947 9.363336673 61 -7.115498272 4.064833947 62 4.639857112 -7.115498272 63 -14.775261759 4.639857112 64 -26.434440251 -14.775261759 65 5.748855671 -26.434440251 66 1.396214304 5.748855671 67 7.108600691 1.396214304 68 7.357631945 7.108600691 69 47.699862853 7.357631945 70 8.877920706 47.699862853 71 -0.582741946 8.877920706 72 0.001844499 -0.582741946 73 -10.378793571 0.001844499 74 28.047257737 -10.378793571 75 20.036514529 28.047257737 76 -6.156984975 20.036514529 77 12.402686613 -6.156984975 78 19.220939189 12.402686613 79 -3.921893716 19.220939189 80 7.522021441 -3.921893716 81 14.474928336 7.522021441 82 -11.352354974 14.474928336 83 4.865192636 -11.352354974 84 3.706495611 4.865192636 85 -8.882447445 3.706495611 86 -10.676075300 -8.882447445 87 -19.671159739 -10.676075300 88 -3.607018040 -19.671159739 89 28.728731916 -3.607018040 90 -6.722074726 28.728731916 91 15.379058920 -6.722074726 92 -14.046529362 15.379058920 93 10.961301797 -14.046529362 94 -23.708238335 10.961301797 95 0.999631261 -23.708238335 96 -18.297600273 0.999631261 97 11.992754114 -18.297600273 98 18.259353718 11.992754114 99 -22.542675115 18.259353718 100 20.962224661 -22.542675115 101 5.785998645 20.962224661 102 -3.708046359 5.785998645 103 -18.462564536 -3.708046359 104 16.535210033 -18.462564536 105 -17.805646830 16.535210033 106 15.212107026 -17.805646830 107 25.175787230 15.212107026 108 -16.987211755 25.175787230 109 24.776055480 -16.987211755 110 -14.891519849 24.776055480 111 16.865028965 -14.891519849 112 -1.200864011 16.865028965 113 20.660701443 -1.200864011 114 -1.897207101 20.660701443 115 7.281244958 -1.897207101 116 -22.308104251 7.281244958 117 -5.736626817 -22.308104251 118 -5.871111907 -5.736626817 119 -9.540540808 -5.871111907 120 13.254327469 -9.540540808 121 -12.377692264 13.254327469 122 -7.006282672 -12.377692264 123 -23.080252141 -7.006282672 124 -2.070214351 -23.080252141 125 -0.012618675 -2.070214351 126 -8.758467057 -0.012618675 127 -47.845624643 -8.758467057 128 13.449203564 -47.845624643 129 27.801697592 13.449203564 130 -10.382471293 27.801697592 131 -4.832631623 -10.382471293 132 -3.021181226 -4.832631623 133 -11.412373927 -3.021181226 134 -22.385014529 -11.412373927 135 -9.515856409 -22.385014529 136 -6.391864411 -9.515856409 137 -3.234415261 -6.391864411 138 7.802709508 -3.234415261 139 -11.506432514 7.802709508 140 -30.427381909 -11.506432514 141 -25.240183849 -30.427381909 142 -10.396994191 -25.240183849 143 3.549213588 -10.396994191 144 19.912978560 3.549213588 145 65.498327445 19.912978560 146 2.456748810 65.498327445 147 8.411198890 2.456748810 148 2.976532971 8.411198890 149 -5.346068566 2.976532971 150 -4.634495600 -5.346068566 151 -4.690657415 -4.634495600 152 -3.775121787 -4.690657415 153 -4.619078632 -3.775121787 154 -18.257537527 -4.619078632 155 9.640437802 -18.257537527 156 -4.619078632 9.640437802 157 -4.651013781 -4.619078632 158 -1.492518083 -4.651013781 159 -5.935775260 -1.492518083 160 -8.828457452 -5.935775260 161 11.991967645 -8.828457452 162 -4.771517843 11.991967645 163 -3.081617679 -4.771517843 164 NA -3.081617679 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -2.329126551 -21.934823483 [2,] 10.761664826 -2.329126551 [3,] 0.303669151 10.761664826 [4,] 1.734581785 0.303669151 [5,] -24.939949476 1.734581785 [6,] 3.444650293 -24.939949476 [7,] -11.728073999 3.444650293 [8,] -11.808230869 -11.728073999 [9,] 3.788562952 -11.808230869 [10,] -11.626213583 3.788562952 [11,] 18.357457576 -11.626213583 [12,] -3.581137918 18.357457576 [13,] -8.337122467 -3.581137918 [14,] 6.965143313 -8.337122467 [15,] 26.621797658 6.965143313 [16,] -0.414138824 26.621797658 [17,] 2.505015743 -0.414138824 [18,] 2.156945752 2.505015743 [19,] 21.095486051 2.156945752 [20,] 2.611443748 21.095486051 [21,] -15.963584489 2.611443748 [22,] 3.485330010 -15.963584489 [23,] 16.982622600 3.485330010 [24,] 27.687673290 16.982622600 [25,] 7.492592674 27.687673290 [26,] 8.376262806 7.492592674 [27,] 18.748106083 8.376262806 [28,] -0.608919478 18.748106083 [29,] 18.377085289 -0.608919478 [30,] 12.467292367 18.377085289 [31,] -18.794549007 12.467292367 [32,] 5.920750463 -18.794549007 [33,] 9.205666305 5.920750463 [34,] -17.721654028 9.205666305 [35,] 13.907231693 -17.721654028 [36,] -10.491594629 13.907231693 [37,] -3.935973952 -10.491594629 [38,] -2.182985877 -3.935973952 [39,] 8.552035626 -2.182985877 [40,] 7.411960143 8.552035626 [41,] -0.224677082 7.411960143 [42,] 3.089331804 -0.224677082 [43,] -1.872591576 3.089331804 [44,] -6.265603838 -1.872591576 [45,] -7.859423880 -6.265603838 [46,] -5.635499496 -7.859423880 [47,] 6.086977943 -5.635499496 [48,] -11.246455445 6.086977943 [49,] 9.229120788 -11.246455445 [50,] 7.224233647 9.229120788 [51,] -3.713146981 7.224233647 [52,] -30.635682248 -3.713146981 [53,] -0.040832996 -30.635682248 [54,] -4.125670619 -0.040832996 [55,] -6.233933854 -4.125670619 [56,] 10.420131508 -6.233933854 [57,] -41.409856420 10.420131508 [58,] 9.677223906 -41.409856420 [59,] 9.363336673 9.677223906 [60,] 4.064833947 9.363336673 [61,] -7.115498272 4.064833947 [62,] 4.639857112 -7.115498272 [63,] -14.775261759 4.639857112 [64,] -26.434440251 -14.775261759 [65,] 5.748855671 -26.434440251 [66,] 1.396214304 5.748855671 [67,] 7.108600691 1.396214304 [68,] 7.357631945 7.108600691 [69,] 47.699862853 7.357631945 [70,] 8.877920706 47.699862853 [71,] -0.582741946 8.877920706 [72,] 0.001844499 -0.582741946 [73,] -10.378793571 0.001844499 [74,] 28.047257737 -10.378793571 [75,] 20.036514529 28.047257737 [76,] -6.156984975 20.036514529 [77,] 12.402686613 -6.156984975 [78,] 19.220939189 12.402686613 [79,] -3.921893716 19.220939189 [80,] 7.522021441 -3.921893716 [81,] 14.474928336 7.522021441 [82,] -11.352354974 14.474928336 [83,] 4.865192636 -11.352354974 [84,] 3.706495611 4.865192636 [85,] -8.882447445 3.706495611 [86,] -10.676075300 -8.882447445 [87,] -19.671159739 -10.676075300 [88,] -3.607018040 -19.671159739 [89,] 28.728731916 -3.607018040 [90,] -6.722074726 28.728731916 [91,] 15.379058920 -6.722074726 [92,] -14.046529362 15.379058920 [93,] 10.961301797 -14.046529362 [94,] -23.708238335 10.961301797 [95,] 0.999631261 -23.708238335 [96,] -18.297600273 0.999631261 [97,] 11.992754114 -18.297600273 [98,] 18.259353718 11.992754114 [99,] -22.542675115 18.259353718 [100,] 20.962224661 -22.542675115 [101,] 5.785998645 20.962224661 [102,] -3.708046359 5.785998645 [103,] -18.462564536 -3.708046359 [104,] 16.535210033 -18.462564536 [105,] -17.805646830 16.535210033 [106,] 15.212107026 -17.805646830 [107,] 25.175787230 15.212107026 [108,] -16.987211755 25.175787230 [109,] 24.776055480 -16.987211755 [110,] -14.891519849 24.776055480 [111,] 16.865028965 -14.891519849 [112,] -1.200864011 16.865028965 [113,] 20.660701443 -1.200864011 [114,] -1.897207101 20.660701443 [115,] 7.281244958 -1.897207101 [116,] -22.308104251 7.281244958 [117,] -5.736626817 -22.308104251 [118,] -5.871111907 -5.736626817 [119,] -9.540540808 -5.871111907 [120,] 13.254327469 -9.540540808 [121,] -12.377692264 13.254327469 [122,] -7.006282672 -12.377692264 [123,] -23.080252141 -7.006282672 [124,] -2.070214351 -23.080252141 [125,] -0.012618675 -2.070214351 [126,] -8.758467057 -0.012618675 [127,] -47.845624643 -8.758467057 [128,] 13.449203564 -47.845624643 [129,] 27.801697592 13.449203564 [130,] -10.382471293 27.801697592 [131,] -4.832631623 -10.382471293 [132,] -3.021181226 -4.832631623 [133,] -11.412373927 -3.021181226 [134,] -22.385014529 -11.412373927 [135,] -9.515856409 -22.385014529 [136,] -6.391864411 -9.515856409 [137,] -3.234415261 -6.391864411 [138,] 7.802709508 -3.234415261 [139,] -11.506432514 7.802709508 [140,] -30.427381909 -11.506432514 [141,] -25.240183849 -30.427381909 [142,] -10.396994191 -25.240183849 [143,] 3.549213588 -10.396994191 [144,] 19.912978560 3.549213588 [145,] 65.498327445 19.912978560 [146,] 2.456748810 65.498327445 [147,] 8.411198890 2.456748810 [148,] 2.976532971 8.411198890 [149,] -5.346068566 2.976532971 [150,] -4.634495600 -5.346068566 [151,] -4.690657415 -4.634495600 [152,] -3.775121787 -4.690657415 [153,] -4.619078632 -3.775121787 [154,] -18.257537527 -4.619078632 [155,] 9.640437802 -18.257537527 [156,] -4.619078632 9.640437802 [157,] -4.651013781 -4.619078632 [158,] -1.492518083 -4.651013781 [159,] -5.935775260 -1.492518083 [160,] -8.828457452 -5.935775260 [161,] 11.991967645 -8.828457452 [162,] -4.771517843 11.991967645 [163,] -3.081617679 -4.771517843 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -2.329126551 -21.934823483 2 10.761664826 -2.329126551 3 0.303669151 10.761664826 4 1.734581785 0.303669151 5 -24.939949476 1.734581785 6 3.444650293 -24.939949476 7 -11.728073999 3.444650293 8 -11.808230869 -11.728073999 9 3.788562952 -11.808230869 10 -11.626213583 3.788562952 11 18.357457576 -11.626213583 12 -3.581137918 18.357457576 13 -8.337122467 -3.581137918 14 6.965143313 -8.337122467 15 26.621797658 6.965143313 16 -0.414138824 26.621797658 17 2.505015743 -0.414138824 18 2.156945752 2.505015743 19 21.095486051 2.156945752 20 2.611443748 21.095486051 21 -15.963584489 2.611443748 22 3.485330010 -15.963584489 23 16.982622600 3.485330010 24 27.687673290 16.982622600 25 7.492592674 27.687673290 26 8.376262806 7.492592674 27 18.748106083 8.376262806 28 -0.608919478 18.748106083 29 18.377085289 -0.608919478 30 12.467292367 18.377085289 31 -18.794549007 12.467292367 32 5.920750463 -18.794549007 33 9.205666305 5.920750463 34 -17.721654028 9.205666305 35 13.907231693 -17.721654028 36 -10.491594629 13.907231693 37 -3.935973952 -10.491594629 38 -2.182985877 -3.935973952 39 8.552035626 -2.182985877 40 7.411960143 8.552035626 41 -0.224677082 7.411960143 42 3.089331804 -0.224677082 43 -1.872591576 3.089331804 44 -6.265603838 -1.872591576 45 -7.859423880 -6.265603838 46 -5.635499496 -7.859423880 47 6.086977943 -5.635499496 48 -11.246455445 6.086977943 49 9.229120788 -11.246455445 50 7.224233647 9.229120788 51 -3.713146981 7.224233647 52 -30.635682248 -3.713146981 53 -0.040832996 -30.635682248 54 -4.125670619 -0.040832996 55 -6.233933854 -4.125670619 56 10.420131508 -6.233933854 57 -41.409856420 10.420131508 58 9.677223906 -41.409856420 59 9.363336673 9.677223906 60 4.064833947 9.363336673 61 -7.115498272 4.064833947 62 4.639857112 -7.115498272 63 -14.775261759 4.639857112 64 -26.434440251 -14.775261759 65 5.748855671 -26.434440251 66 1.396214304 5.748855671 67 7.108600691 1.396214304 68 7.357631945 7.108600691 69 47.699862853 7.357631945 70 8.877920706 47.699862853 71 -0.582741946 8.877920706 72 0.001844499 -0.582741946 73 -10.378793571 0.001844499 74 28.047257737 -10.378793571 75 20.036514529 28.047257737 76 -6.156984975 20.036514529 77 12.402686613 -6.156984975 78 19.220939189 12.402686613 79 -3.921893716 19.220939189 80 7.522021441 -3.921893716 81 14.474928336 7.522021441 82 -11.352354974 14.474928336 83 4.865192636 -11.352354974 84 3.706495611 4.865192636 85 -8.882447445 3.706495611 86 -10.676075300 -8.882447445 87 -19.671159739 -10.676075300 88 -3.607018040 -19.671159739 89 28.728731916 -3.607018040 90 -6.722074726 28.728731916 91 15.379058920 -6.722074726 92 -14.046529362 15.379058920 93 10.961301797 -14.046529362 94 -23.708238335 10.961301797 95 0.999631261 -23.708238335 96 -18.297600273 0.999631261 97 11.992754114 -18.297600273 98 18.259353718 11.992754114 99 -22.542675115 18.259353718 100 20.962224661 -22.542675115 101 5.785998645 20.962224661 102 -3.708046359 5.785998645 103 -18.462564536 -3.708046359 104 16.535210033 -18.462564536 105 -17.805646830 16.535210033 106 15.212107026 -17.805646830 107 25.175787230 15.212107026 108 -16.987211755 25.175787230 109 24.776055480 -16.987211755 110 -14.891519849 24.776055480 111 16.865028965 -14.891519849 112 -1.200864011 16.865028965 113 20.660701443 -1.200864011 114 -1.897207101 20.660701443 115 7.281244958 -1.897207101 116 -22.308104251 7.281244958 117 -5.736626817 -22.308104251 118 -5.871111907 -5.736626817 119 -9.540540808 -5.871111907 120 13.254327469 -9.540540808 121 -12.377692264 13.254327469 122 -7.006282672 -12.377692264 123 -23.080252141 -7.006282672 124 -2.070214351 -23.080252141 125 -0.012618675 -2.070214351 126 -8.758467057 -0.012618675 127 -47.845624643 -8.758467057 128 13.449203564 -47.845624643 129 27.801697592 13.449203564 130 -10.382471293 27.801697592 131 -4.832631623 -10.382471293 132 -3.021181226 -4.832631623 133 -11.412373927 -3.021181226 134 -22.385014529 -11.412373927 135 -9.515856409 -22.385014529 136 -6.391864411 -9.515856409 137 -3.234415261 -6.391864411 138 7.802709508 -3.234415261 139 -11.506432514 7.802709508 140 -30.427381909 -11.506432514 141 -25.240183849 -30.427381909 142 -10.396994191 -25.240183849 143 3.549213588 -10.396994191 144 19.912978560 3.549213588 145 65.498327445 19.912978560 146 2.456748810 65.498327445 147 8.411198890 2.456748810 148 2.976532971 8.411198890 149 -5.346068566 2.976532971 150 -4.634495600 -5.346068566 151 -4.690657415 -4.634495600 152 -3.775121787 -4.690657415 153 -4.619078632 -3.775121787 154 -18.257537527 -4.619078632 155 9.640437802 -18.257537527 156 -4.619078632 9.640437802 157 -4.651013781 -4.619078632 158 -1.492518083 -4.651013781 159 -5.935775260 -1.492518083 160 -8.828457452 -5.935775260 161 11.991967645 -8.828457452 162 -4.771517843 11.991967645 163 -3.081617679 -4.771517843 > 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/7a1p11321542431.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/89cfs1321542431.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/9d4a21321542431.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/100tdg1321542431.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/117he31321542431.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/125j991321542431.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/13q6z71321542431.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/14m74w1321542431.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/159uxj1321542431.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/166dck1321542432.tab") + } > > try(system("convert tmp/1i1ph1321542431.ps tmp/1i1ph1321542431.png",intern=TRUE)) character(0) > try(system("convert tmp/2ovja1321542431.ps tmp/2ovja1321542431.png",intern=TRUE)) character(0) > try(system("convert tmp/3gd3y1321542431.ps tmp/3gd3y1321542431.png",intern=TRUE)) character(0) > try(system("convert tmp/4wo561321542431.ps tmp/4wo561321542431.png",intern=TRUE)) character(0) > try(system("convert tmp/546xq1321542431.ps tmp/546xq1321542431.png",intern=TRUE)) character(0) > try(system("convert tmp/6hf8w1321542431.ps tmp/6hf8w1321542431.png",intern=TRUE)) character(0) > try(system("convert tmp/7a1p11321542431.ps tmp/7a1p11321542431.png",intern=TRUE)) character(0) > try(system("convert tmp/89cfs1321542431.ps tmp/89cfs1321542431.png",intern=TRUE)) character(0) > try(system("convert tmp/9d4a21321542431.ps tmp/9d4a21321542431.png",intern=TRUE)) character(0) > try(system("convert tmp/100tdg1321542431.ps tmp/100tdg1321542431.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.268 0.547 5.845