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. 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,7 + ,7199 + ,7199 + ,0 + ,0 + ,1644 + ,1644 + ,4245 + ,4245 + ,6 + ,6 + ,1 + ,12 + ,46660 + ,46660 + ,0 + ,0 + ,6179 + ,6179 + ,21509 + ,21509 + ,13 + ,13 + ,0 + ,0 + ,17547 + ,0 + ,0 + ,0 + ,3926 + ,0 + ,7670 + ,0 + ,3 + ,0 + ,1 + ,37 + ,73567 + ,73567 + ,0 + ,0 + ,23238 + ,23238 + ,10641 + ,10641 + ,18 + ,18 + ,0 + ,0 + ,969 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,39 + ,101060 + ,0 + ,2 + ,0 + ,49288 + ,0 + ,41243 + ,0 + ,49 + ,0) + ,dim=c(12 + ,164) + ,dimnames=list(c('Pop' + ,'BloggedComputations' + ,'TotalTime' + ,'TotalTimep' + ,'Shared' + ,'Sharedp' + ,'Characters' + ,'Charactersp' + ,'Writing' + ,'Writingp' + ,'Hyperlinks' + ,'Hyperlinksp') + ,1:164)) > y <- array(NA,dim=c(12,164),dimnames=list(c('Pop','BloggedComputations','TotalTime','TotalTimep','Shared','Sharedp','Characters','Charactersp','Writing','Writingp','Hyperlinks','Hyperlinksp'),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 BloggedComputations Pop TotalTime TotalTimep Shared Sharedp Characters 1 65 0 146455 0 1 0 95556 2 54 0 84944 0 4 0 54565 3 58 0 113337 0 9 0 63016 4 75 0 128655 0 2 0 79774 5 41 0 74398 0 1 0 31258 6 0 0 35523 0 2 0 52491 7 111 1 293403 293403 0 0 91256 8 1 0 32750 0 0 0 22807 9 36 0 106539 0 5 0 77411 10 60 0 130539 0 0 0 48821 11 63 0 154991 0 0 0 52295 12 71 1 126683 126683 7 7 63262 13 38 1 100672 100672 6 6 50466 14 76 1 179562 179562 3 3 62932 15 61 1 125971 125971 4 4 38439 16 125 0 234509 0 0 0 70817 17 84 1 158980 158980 4 4 105965 18 69 0 184217 0 3 0 73795 19 77 1 107342 107342 0 0 82043 20 95 1 141371 141371 5 5 74349 21 78 1 154730 154730 0 0 82204 22 76 1 264020 264020 1 1 55709 23 40 1 90938 90938 3 3 37137 24 81 0 101324 0 5 0 70780 25 102 0 130232 0 0 0 55027 26 70 0 137793 0 0 0 56699 27 75 0 161678 0 4 0 65911 28 93 1 151503 151503 0 0 56316 29 42 1 105324 105324 0 0 26982 30 95 1 175914 175914 0 0 54628 31 87 0 181853 0 3 0 96750 32 44 0 114928 0 4 0 53009 33 84 1 190410 190410 1 1 64664 34 28 1 61499 61499 4 4 36990 35 87 1 223004 223004 1 1 85224 36 71 1 167131 167131 0 0 37048 37 68 1 233482 233482 0 0 59635 38 50 1 121185 121185 2 2 42051 39 30 1 78776 78776 1 1 26998 40 86 1 188967 188967 2 2 63717 41 75 1 199512 199512 8 8 55071 42 46 1 102531 102531 5 5 40001 43 52 1 118958 118958 3 3 54506 44 31 0 68948 0 4 0 35838 45 30 0 93125 0 1 0 50838 46 70 0 277108 0 2 0 86997 47 20 0 78800 0 2 0 33032 48 84 0 157250 0 0 0 61704 49 81 1 210554 210554 6 6 117986 50 79 0 127324 0 3 0 56733 51 70 1 114397 114397 0 0 55064 52 8 1 24188 24188 0 0 5950 53 67 1 246209 246209 6 6 84607 54 21 0 65029 0 5 0 32551 55 30 0 98030 0 3 0 31701 56 70 1 173587 173587 1 1 71170 57 87 0 172684 0 5 0 101773 58 87 0 191381 0 5 0 101653 59 112 1 191276 191276 0 0 81493 60 54 1 134043 134043 9 9 55901 61 96 1 233406 233406 6 6 109104 62 93 1 195304 195304 6 6 114425 63 49 1 127619 127619 5 5 36311 64 49 0 162810 0 6 0 70027 65 38 0 129100 0 2 0 73713 66 64 1 108715 108715 0 0 40671 67 62 0 106469 0 3 0 89041 68 66 1 142069 142069 8 8 57231 69 98 0 143937 0 2 0 78792 70 97 1 84256 84256 5 5 59155 71 56 0 118807 0 11 0 55827 72 22 1 69471 69471 6 6 22618 73 51 0 122433 0 5 0 58425 74 56 1 131122 131122 1 1 65724 75 94 0 94763 0 0 0 56979 76 98 1 188780 188780 3 3 72369 77 76 0 191467 0 3 0 79194 78 57 0 105615 0 6 0 202316 79 75 0 89318 0 1 0 44970 80 48 0 107335 0 0 0 49319 81 48 0 98599 0 1 0 36252 82 109 0 260646 0 0 0 75741 83 27 1 131876 131876 5 5 38417 84 83 1 119291 119291 2 2 64102 85 49 1 80953 80953 0 0 56622 86 24 1 99768 99768 0 0 15430 87 43 1 84572 84572 5 5 72571 88 44 1 202373 202373 1 1 67271 89 49 1 166790 166790 0 0 43460 90 106 0 99946 0 1 0 99501 91 42 1 116900 116900 1 1 28340 92 108 0 142146 0 2 0 76013 93 27 1 99246 99246 4 4 37361 94 79 0 156833 0 1 0 48204 95 49 1 175078 175078 4 4 76168 96 64 0 130533 0 0 0 85168 97 75 1 142339 142339 2 2 125410 98 115 0 176789 0 0 0 123328 99 92 1 181379 181379 7 7 83038 100 106 0 228548 0 7 0 120087 101 73 1 142141 142141 6 6 91939 102 105 1 167845 167845 0 0 103646 103 30 1 103012 103012 0 0 29467 104 13 1 43287 43287 4 4 43750 105 69 1 125366 125366 4 4 34497 106 72 1 118372 118372 0 0 66477 107 80 0 135171 0 0 0 71181 108 106 0 175568 0 0 0 74482 109 28 0 74112 0 0 0 174949 110 70 0 88817 0 0 0 46765 111 51 1 164767 164767 4 4 90257 112 90 0 141933 0 0 0 51370 113 12 0 22938 0 0 0 1168 114 84 0 115199 0 0 0 51360 115 23 0 61857 0 4 0 25162 116 57 1 91185 91185 0 0 21067 117 84 0 213765 0 1 0 58233 118 4 1 21054 21054 0 0 855 119 56 0 167105 0 5 0 85903 120 18 0 31414 0 0 0 14116 121 86 1 178863 178863 1 1 57637 122 39 0 126681 0 7 0 94137 123 16 1 64320 64320 5 5 62147 124 18 1 67746 67746 2 2 62832 125 16 1 38214 38214 0 0 8773 126 42 1 90961 90961 1 1 63785 127 75 1 181510 181510 0 0 65196 128 30 0 116775 0 0 0 73087 129 104 0 223914 0 2 0 72631 130 121 0 185139 0 0 0 86281 131 106 0 242879 0 2 0 162365 132 57 1 139144 139144 0 0 56530 133 28 1 75812 75812 0 0 35606 134 56 1 178218 178218 4 4 70111 135 81 1 246834 246834 4 4 92046 136 2 0 50999 0 8 0 63989 137 88 0 223842 0 0 0 104911 138 41 0 93577 0 4 0 43448 139 83 1 155383 155383 0 0 60029 140 55 1 111664 111664 1 1 38650 141 3 1 75426 75426 0 0 47261 142 54 1 243551 243551 9 9 73586 143 89 1 136548 136548 0 0 83042 144 41 1 173260 173260 3 3 37238 145 94 0 185039 0 7 0 63958 146 101 0 67507 0 5 0 78956 147 70 0 139350 0 2 0 99518 148 111 0 172964 0 1 0 111436 149 0 1 0 0 9 9 0 150 4 1 14688 14688 0 0 6023 151 0 1 98 98 0 0 0 152 0 1 455 455 0 0 0 153 0 0 0 0 1 0 0 154 0 0 0 0 0 0 0 155 42 1 128066 128066 2 2 42564 156 97 0 176460 0 1 0 38885 157 0 1 0 0 0 0 0 158 0 1 203 203 0 0 0 159 7 1 7199 7199 0 0 1644 160 12 1 46660 46660 0 0 6179 161 0 0 17547 0 0 0 3926 162 37 1 73567 73567 0 0 23238 163 0 0 969 0 0 0 0 164 39 0 101060 0 2 0 49288 Charactersp Writing Writingp Hyperlinks Hyperlinksp 1 0 114468 0 127 0 2 0 88594 0 90 0 3 0 74151 0 68 0 4 0 77921 0 111 0 5 0 53212 0 51 0 6 0 34956 0 33 0 7 91256 149703 149703 123 123 8 0 6853 0 5 0 9 0 58907 0 63 0 10 0 67067 0 66 0 11 0 110563 0 99 0 12 63262 58126 58126 72 72 13 50466 57113 57113 55 55 14 62932 77993 77993 116 116 15 38439 68091 68091 71 71 16 0 124676 0 125 0 17 105965 109522 109522 123 123 18 0 75865 0 74 0 19 82043 79746 79746 116 116 20 74349 77844 77844 117 117 21 82204 98681 98681 98 98 22 55709 105531 105531 101 101 23 37137 51428 51428 43 43 24 0 65703 0 103 0 25 0 72562 0 107 0 26 0 81728 0 77 0 27 0 95580 0 87 0 28 56316 98278 98278 99 99 29 26982 46629 46629 46 46 30 54628 115189 115189 96 96 31 0 124865 0 92 0 32 0 59392 0 96 0 33 64664 127818 127818 96 96 34 36990 17821 17821 15 15 35 85224 154076 154076 147 147 36 37048 64881 64881 56 56 37 59635 136506 136506 81 81 38 42051 66524 66524 69 69 39 26998 45988 45988 34 34 40 63717 107445 107445 98 98 41 55071 102772 102772 82 82 42 40001 46657 46657 64 64 43 54506 97563 97563 61 61 44 0 36663 0 45 0 45 0 55369 0 37 0 46 0 77921 0 64 0 47 0 56968 0 21 0 48 0 77519 0 104 0 49 117986 129805 129805 126 126 50 0 72761 0 104 0 51 55064 81278 81278 87 87 52 5950 15049 15049 7 7 53 84607 113935 113935 130 130 54 0 25109 0 21 0 55 0 45824 0 35 0 56 71170 89644 89644 97 97 57 0 109011 0 103 0 58 0 134245 0 210 0 59 81493 136692 136692 151 151 60 55901 50741 50741 57 57 61 109104 149510 149510 117 117 62 114425 147888 147888 152 152 63 36311 54987 54987 52 52 64 0 74467 0 83 0 65 0 100033 0 87 0 66 40671 85505 85505 80 80 67 0 62426 0 88 0 68 57231 82932 82932 83 83 69 0 79169 0 140 0 70 59155 65469 65469 76 76 71 0 63572 0 70 0 72 22618 23824 23824 26 26 73 0 73831 0 66 0 74 65724 63551 63551 89 89 75 0 56756 0 100 0 76 72369 81399 81399 98 98 77 0 117881 0 109 0 78 0 70711 0 51 0 79 0 50495 0 82 0 80 0 53845 0 65 0 81 0 51390 0 46 0 82 0 104953 0 104 0 83 38417 65983 65983 36 36 84 64102 76839 76839 123 123 85 56622 55792 55792 59 59 86 15430 25155 25155 27 27 87 72571 55291 55291 84 84 88 67271 84279 84279 61 61 89 43460 99692 99692 46 46 90 0 59633 0 125 0 91 28340 63249 63249 58 58 92 0 82928 0 152 0 93 37361 50000 50000 52 52 94 0 69455 0 85 0 95 76168 84068 84068 95 95 96 0 76195 0 78 0 97 125410 114634 114634 144 144 98 0 139357 0 149 0 99 83038 110044 110044 101 101 100 0 155118 0 205 0 101 91939 83061 83061 61 61 102 103646 127122 127122 145 145 103 29467 45653 45653 28 28 104 43750 19630 19630 49 49 105 34497 67229 67229 68 68 106 66477 86060 86060 142 142 107 0 88003 0 82 0 108 0 95815 0 105 0 109 0 85499 0 52 0 110 0 27220 0 56 0 111 90257 109882 109882 81 81 112 0 72579 0 100 0 113 0 5841 0 11 0 114 0 68369 0 87 0 115 0 24610 0 31 0 116 21067 30995 30995 67 67 117 0 150662 0 150 0 118 855 6622 6622 4 4 119 0 93694 0 75 0 120 0 13155 0 39 0 121 57637 111908 111908 88 88 122 0 57550 0 67 0 123 62147 16356 16356 24 24 124 62832 40174 40174 58 58 125 8773 13983 13983 16 16 126 63785 52316 52316 49 49 127 65196 99585 99585 109 109 128 0 86271 0 124 0 129 0 131012 0 115 0 130 0 130274 0 128 0 131 0 159051 0 159 0 132 56530 76506 76506 75 75 133 35606 49145 49145 30 30 134 70111 66398 66398 83 83 135 92046 127546 127546 135 135 136 0 6802 0 8 0 137 0 99509 0 115 0 138 0 43106 0 60 0 139 60029 108303 108303 99 99 140 38650 64167 64167 98 98 141 47261 8579 8579 36 36 142 73586 97811 97811 93 93 143 83042 84365 84365 158 158 144 37238 10901 10901 16 16 145 0 91346 0 100 0 146 0 33660 0 49 0 147 0 93634 0 89 0 148 0 109348 0 153 0 149 0 0 0 0 0 150 6023 7953 7953 5 5 151 0 0 0 0 0 152 0 0 0 0 0 153 0 0 0 0 0 154 0 0 0 0 0 155 42564 63538 63538 80 80 156 0 108281 0 122 0 157 0 0 0 0 0 158 0 0 0 0 0 159 1644 4245 4245 6 6 160 6179 21509 21509 13 13 161 0 7670 0 3 0 162 23238 10641 10641 18 18 163 0 0 0 0 0 164 0 41243 0 49 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Pop TotalTime TotalTimep Shared Sharedp 5.231e+00 5.502e-02 2.738e-04 -2.074e-04 -1.541e+00 1.613e+00 Characters Charactersp Writing Writingp Hyperlinks Hyperlinksp 7.417e-05 -1.769e-04 -2.615e-04 4.975e-04 4.972e-01 -8.562e-02 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -51.717 -8.148 -1.558 8.604 63.572 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 5.231e+00 4.412e+00 1.186 0.23766 Pop 5.502e-02 5.759e+00 0.010 0.99239 TotalTime 2.738e-04 5.830e-05 4.697 5.86e-06 *** TotalTimep -2.074e-04 7.845e-05 -2.644 0.00905 ** Shared -1.541e+00 7.089e-01 -2.173 0.03130 * Sharedp 1.613e+00 9.765e-01 1.652 0.10070 Characters 7.417e-05 6.496e-05 1.142 0.25535 Charactersp -1.769e-04 1.384e-04 -1.278 0.20305 Writing -2.615e-04 1.274e-04 -2.053 0.04181 * Writingp 4.975e-04 1.695e-04 2.935 0.00385 ** Hyperlinks 4.972e-01 7.637e-02 6.510 1.03e-09 *** Hyperlinksp -8.562e-02 1.194e-01 -0.717 0.47427 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 15.05 on 152 degrees of freedom Multiple R-squared: 0.7969, Adjusted R-squared: 0.7822 F-statistic: 54.21 on 11 and 152 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,] 0.069486989 1.389740e-01 9.305130e-01 [2,] 0.069204635 1.384093e-01 9.307954e-01 [3,] 0.026470082 5.294016e-02 9.735299e-01 [4,] 0.014910513 2.982103e-02 9.850895e-01 [5,] 0.061895263 1.237905e-01 9.381047e-01 [6,] 0.077435362 1.548707e-01 9.225646e-01 [7,] 0.047613496 9.522699e-02 9.523865e-01 [8,] 0.040281360 8.056272e-02 9.597186e-01 [9,] 0.022722254 4.544451e-02 9.772777e-01 [10,] 0.012373797 2.474759e-02 9.876262e-01 [11,] 0.007863344 1.572669e-02 9.921367e-01 [12,] 0.009491964 1.898393e-02 9.905080e-01 [13,] 0.005133635 1.026727e-02 9.948664e-01 [14,] 0.005806408 1.161282e-02 9.941936e-01 [15,] 0.003421985 6.843969e-03 9.965780e-01 [16,] 0.001953584 3.907168e-03 9.980464e-01 [17,] 0.045748204 9.149641e-02 9.542518e-01 [18,] 0.191325472 3.826509e-01 8.086745e-01 [19,] 0.167004486 3.340090e-01 8.329955e-01 [20,] 0.151231832 3.024637e-01 8.487682e-01 [21,] 0.249816098 4.996322e-01 7.501839e-01 [22,] 0.242444256 4.848885e-01 7.575557e-01 [23,] 0.221013916 4.420278e-01 7.789861e-01 [24,] 0.193193693 3.863874e-01 8.068063e-01 [25,] 0.167092333 3.341847e-01 8.329077e-01 [26,] 0.140398729 2.807975e-01 8.596013e-01 [27,] 0.111385229 2.227705e-01 8.886148e-01 [28,] 0.089810251 1.796205e-01 9.101897e-01 [29,] 0.068468801 1.369376e-01 9.315312e-01 [30,] 0.050767212 1.015344e-01 9.492328e-01 [31,] 0.037542788 7.508558e-02 9.624572e-01 [32,] 0.058115737 1.162315e-01 9.418843e-01 [33,] 0.043011574 8.602315e-02 9.569884e-01 [34,] 0.031515047 6.303009e-02 9.684850e-01 [35,] 0.028157564 5.631513e-02 9.718424e-01 [36,] 0.020520839 4.104168e-02 9.794792e-01 [37,] 0.014978763 2.995753e-02 9.850212e-01 [38,] 0.013244446 2.648889e-02 9.867556e-01 [39,] 0.035446548 7.089310e-02 9.645535e-01 [40,] 0.026510112 5.302022e-02 9.734899e-01 [41,] 0.020178716 4.035743e-02 9.798213e-01 [42,] 0.015030313 3.006063e-02 9.849697e-01 [43,] 0.013486736 2.697347e-02 9.865133e-01 [44,] 0.133867506 2.677350e-01 8.661325e-01 [45,] 0.113757370 2.275147e-01 8.862426e-01 [46,] 0.098940994 1.978820e-01 9.010590e-01 [47,] 0.081173109 1.623462e-01 9.188269e-01 [48,] 0.070846255 1.416925e-01 9.291537e-01 [49,] 0.055686813 1.113736e-01 9.443132e-01 [50,] 0.062425549 1.248511e-01 9.375745e-01 [51,] 0.086485245 1.729705e-01 9.135148e-01 [52,] 0.068608264 1.372165e-01 9.313917e-01 [53,] 0.062827999 1.256560e-01 9.371720e-01 [54,] 0.049660130 9.932026e-02 9.503399e-01 [55,] 0.044274284 8.854857e-02 9.557257e-01 [56,] 0.210790325 4.215806e-01 7.892097e-01 [57,] 0.192911086 3.858222e-01 8.070889e-01 [58,] 0.170430362 3.408607e-01 8.295696e-01 [59,] 0.141934386 2.838688e-01 8.580656e-01 [60,] 0.124143549 2.482871e-01 8.758565e-01 [61,] 0.190953289 3.819066e-01 8.090467e-01 [62,] 0.269276710 5.385534e-01 7.307233e-01 [63,] 0.237951053 4.759021e-01 7.620489e-01 [64,] 0.239759082 4.795182e-01 7.602409e-01 [65,] 0.249425607 4.988512e-01 7.505744e-01 [66,] 0.223880668 4.477613e-01 7.761193e-01 [67,] 0.193966024 3.879320e-01 8.060340e-01 [68,] 0.170720756 3.414415e-01 8.292792e-01 [69,] 0.163702466 3.274049e-01 8.362975e-01 [70,] 0.147227395 2.944548e-01 8.527726e-01 [71,] 0.126122352 2.522447e-01 8.738776e-01 [72,] 0.108396455 2.167929e-01 8.916035e-01 [73,] 0.102050634 2.041013e-01 8.979494e-01 [74,] 0.098431987 1.968640e-01 9.015680e-01 [75,] 0.084498279 1.689966e-01 9.155017e-01 [76,] 0.105423746 2.108475e-01 8.945763e-01 [77,] 0.091406668 1.828133e-01 9.085933e-01 [78,] 0.080734058 1.614681e-01 9.192659e-01 [79,] 0.079814269 1.596285e-01 9.201857e-01 [80,] 0.064704107 1.294082e-01 9.352959e-01 [81,] 0.073434023 1.468680e-01 9.265660e-01 [82,] 0.058679116 1.173582e-01 9.413209e-01 [83,] 0.054712324 1.094246e-01 9.452877e-01 [84,] 0.057508277 1.150166e-01 9.424917e-01 [85,] 0.062095740 1.241915e-01 9.379043e-01 [86,] 0.065894673 1.317893e-01 9.341053e-01 [87,] 0.102638566 2.052771e-01 8.973614e-01 [88,] 0.101480645 2.029613e-01 8.985194e-01 [89,] 0.082444768 1.648895e-01 9.175552e-01 [90,] 0.080289107 1.605782e-01 9.197109e-01 [91,] 0.085207775 1.704156e-01 9.147922e-01 [92,] 0.075779705 1.515594e-01 9.242203e-01 [93,] 0.073473909 1.469478e-01 9.265261e-01 [94,] 0.083605854 1.672117e-01 9.163941e-01 [95,] 0.076599426 1.531989e-01 9.234006e-01 [96,] 0.082699599 1.653992e-01 9.173004e-01 [97,] 0.072868875 1.457378e-01 9.271311e-01 [98,] 0.071817440 1.436349e-01 9.281826e-01 [99,] 0.057070464 1.141409e-01 9.429295e-01 [100,] 0.074457350 1.489147e-01 9.255426e-01 [101,] 0.058675350 1.173507e-01 9.413246e-01 [102,] 0.054544862 1.090897e-01 9.454551e-01 [103,] 0.070231612 1.404632e-01 9.297684e-01 [104,] 0.056559621 1.131192e-01 9.434404e-01 [105,] 0.057765306 1.155306e-01 9.422347e-01 [106,] 0.048559870 9.711974e-02 9.514401e-01 [107,] 0.045174232 9.034846e-02 9.548258e-01 [108,] 0.058825461 1.176509e-01 9.411745e-01 [109,] 0.046608036 9.321607e-02 9.533920e-01 [110,] 0.043108994 8.621799e-02 9.568910e-01 [111,] 0.031493849 6.298770e-02 9.685062e-01 [112,] 0.025340528 5.068106e-02 9.746595e-01 [113,] 0.017956061 3.591212e-02 9.820439e-01 [114,] 0.298791053 5.975821e-01 7.012089e-01 [115,] 0.282945443 5.658909e-01 7.170546e-01 [116,] 0.411977245 8.239545e-01 5.880228e-01 [117,] 0.416902828 8.338057e-01 5.830972e-01 [118,] 0.355600100 7.112002e-01 6.443999e-01 [119,] 0.297776448 5.955529e-01 7.022236e-01 [120,] 0.244146597 4.882932e-01 7.558534e-01 [121,] 0.207351645 4.147033e-01 7.926484e-01 [122,] 0.467095274 9.341905e-01 5.329047e-01 [123,] 0.470061477 9.401230e-01 5.299385e-01 [124,] 0.603598760 7.928025e-01 3.964012e-01 [125,] 0.782272392 4.354552e-01 2.177276e-01 [126,] 0.777341985 4.453160e-01 2.226580e-01 [127,] 0.855923990 2.881520e-01 1.440760e-01 [128,] 0.813114723 3.737706e-01 1.868853e-01 [129,] 0.735326171 5.293477e-01 2.646738e-01 [130,] 0.725566038 5.488679e-01 2.744340e-01 [131,] 0.985578513 2.884297e-02 1.442149e-02 [132,] 0.999976645 4.670920e-05 2.335460e-05 [133,] 0.999919811 1.603776e-04 8.018879e-05 [134,] 0.999311409 1.377182e-03 6.885912e-04 [135,] 0.999999946 1.076323e-07 5.381616e-08 > postscript(file="/var/wessaorg/rcomp/tmp/1txdt1321896929.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/2d54p1321896929.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/36ma41321896929.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4erwf1321896929.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5d60x1321896929.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 -19.0874702 6.0477986 16.5099663 -3.1052312 3.1787681 -23.0353580 7 8 9 10 11 12 9.6543997 -15.5844897 -12.3599115 0.1276986 -8.8571184 19.9463152 13 14 15 16 17 18 -5.3333362 -1.1077573 5.7199045 20.7583900 2.2848325 -4.4781482 19 20 21 22 23 24 6.4523842 21.0801542 7.2620396 -7.6387463 2.4403295 16.4502551 25 26 27 28 29 30 22.8042703 5.9223089 8.5121602 19.5009710 2.5560399 16.9505352 31 32 33 34 35 36 16.3324320 -22.6678740 2.9664141 11.7627520 -21.2724544 20.0631545 37 38 39 40 41 42 -12.2148240 -3.2544369 -2.6616564 8.8778557 3.5455550 0.3039651 43 44 45 46 47 48 -3.9320485 -2.3914751 -6.8770955 -25.9256546 -1.7198997 -0.3015848 49 50 51 52 53 54 -9.0702178 6.6397179 7.7867420 -4.7133628 -26.7667137 -0.6232750 55 56 57 58 59 60 -5.2213854 -0.6509440 11.9367761 -39.7727962 7.9792848 9.4738984 61 62 63 64 65 66 2.5539643 -11.3915746 4.2321125 -18.5547524 -22.0622472 2.5690941 67 68 69 70 71 72 -1.7938916 2.8519883 1.6910520 45.1062384 12.8655944 -2.3305007 73 74 75 76 77 78 2.1075500 -2.9404161 23.7186971 27.8537310 -6.2766172 10.2224071 79 80 81 82 83 84 15.9529402 -8.5159133 5.1905758 2.5183953 -13.8443110 7.4775119 85 86 87 88 89 90 6.7056347 -3.3740467 -8.4273126 -12.8803347 -5.3554106 21.0090586 91 92 93 94 95 96 -7.0060938 7.4045893 -14.5273020 4.6920303 -19.3135353 -2.1455175 97 98 99 100 101 102 -13.3183670 14.5766538 15.1580542 -21.2915921 22.5798219 9.5381209 103 104 105 106 107 108 -1.3968073 -15.7537399 14.7933157 -13.0693527 14.7213539 20.0222834 109 110 111 112 113 114 -13.9947865 16.2557540 -15.5121377 11.3560401 -3.5402876 18.0398207 115 116 117 118 119 120 -3.8495726 12.9341303 -17.7202891 -5.8051264 -6.4432565 -12.8298711 121 122 123 124 125 126 12.0585848 -15.3783986 -1.2710510 -18.8262682 -0.8071452 4.6407868 127 128 129 130 131 132 -4.0033605 -51.7169499 12.2353909 29.1032601 -12.1565667 -0.6408536 133 134 135 136 137 138 -2.6075777 -4.0352817 -17.1701763 -11.8156767 -17.4596686 -5.4729016 139 140 141 142 143 144 7.2588177 -9.2786044 -19.2807244 -21.9051609 -1.7597570 18.6614035 145 146 147 148 149 150 18.3113466 63.5715057 2.5489619 4.2098154 -5.9339633 -5.5773429 151 152 153 154 155 156 -5.2925407 -5.3162437 -3.6903205 -5.2310169 -15.4810825 9.7673164 157 158 159 160 161 162 -5.2860340 -5.2995122 -2.0663396 -6.1757865 -9.8128334 19.2968358 163 164 -5.4963657 -8.0548741 > postscript(file="/var/wessaorg/rcomp/tmp/6289v1321896929.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 -19.0874702 NA 1 6.0477986 -19.0874702 2 16.5099663 6.0477986 3 -3.1052312 16.5099663 4 3.1787681 -3.1052312 5 -23.0353580 3.1787681 6 9.6543997 -23.0353580 7 -15.5844897 9.6543997 8 -12.3599115 -15.5844897 9 0.1276986 -12.3599115 10 -8.8571184 0.1276986 11 19.9463152 -8.8571184 12 -5.3333362 19.9463152 13 -1.1077573 -5.3333362 14 5.7199045 -1.1077573 15 20.7583900 5.7199045 16 2.2848325 20.7583900 17 -4.4781482 2.2848325 18 6.4523842 -4.4781482 19 21.0801542 6.4523842 20 7.2620396 21.0801542 21 -7.6387463 7.2620396 22 2.4403295 -7.6387463 23 16.4502551 2.4403295 24 22.8042703 16.4502551 25 5.9223089 22.8042703 26 8.5121602 5.9223089 27 19.5009710 8.5121602 28 2.5560399 19.5009710 29 16.9505352 2.5560399 30 16.3324320 16.9505352 31 -22.6678740 16.3324320 32 2.9664141 -22.6678740 33 11.7627520 2.9664141 34 -21.2724544 11.7627520 35 20.0631545 -21.2724544 36 -12.2148240 20.0631545 37 -3.2544369 -12.2148240 38 -2.6616564 -3.2544369 39 8.8778557 -2.6616564 40 3.5455550 8.8778557 41 0.3039651 3.5455550 42 -3.9320485 0.3039651 43 -2.3914751 -3.9320485 44 -6.8770955 -2.3914751 45 -25.9256546 -6.8770955 46 -1.7198997 -25.9256546 47 -0.3015848 -1.7198997 48 -9.0702178 -0.3015848 49 6.6397179 -9.0702178 50 7.7867420 6.6397179 51 -4.7133628 7.7867420 52 -26.7667137 -4.7133628 53 -0.6232750 -26.7667137 54 -5.2213854 -0.6232750 55 -0.6509440 -5.2213854 56 11.9367761 -0.6509440 57 -39.7727962 11.9367761 58 7.9792848 -39.7727962 59 9.4738984 7.9792848 60 2.5539643 9.4738984 61 -11.3915746 2.5539643 62 4.2321125 -11.3915746 63 -18.5547524 4.2321125 64 -22.0622472 -18.5547524 65 2.5690941 -22.0622472 66 -1.7938916 2.5690941 67 2.8519883 -1.7938916 68 1.6910520 2.8519883 69 45.1062384 1.6910520 70 12.8655944 45.1062384 71 -2.3305007 12.8655944 72 2.1075500 -2.3305007 73 -2.9404161 2.1075500 74 23.7186971 -2.9404161 75 27.8537310 23.7186971 76 -6.2766172 27.8537310 77 10.2224071 -6.2766172 78 15.9529402 10.2224071 79 -8.5159133 15.9529402 80 5.1905758 -8.5159133 81 2.5183953 5.1905758 82 -13.8443110 2.5183953 83 7.4775119 -13.8443110 84 6.7056347 7.4775119 85 -3.3740467 6.7056347 86 -8.4273126 -3.3740467 87 -12.8803347 -8.4273126 88 -5.3554106 -12.8803347 89 21.0090586 -5.3554106 90 -7.0060938 21.0090586 91 7.4045893 -7.0060938 92 -14.5273020 7.4045893 93 4.6920303 -14.5273020 94 -19.3135353 4.6920303 95 -2.1455175 -19.3135353 96 -13.3183670 -2.1455175 97 14.5766538 -13.3183670 98 15.1580542 14.5766538 99 -21.2915921 15.1580542 100 22.5798219 -21.2915921 101 9.5381209 22.5798219 102 -1.3968073 9.5381209 103 -15.7537399 -1.3968073 104 14.7933157 -15.7537399 105 -13.0693527 14.7933157 106 14.7213539 -13.0693527 107 20.0222834 14.7213539 108 -13.9947865 20.0222834 109 16.2557540 -13.9947865 110 -15.5121377 16.2557540 111 11.3560401 -15.5121377 112 -3.5402876 11.3560401 113 18.0398207 -3.5402876 114 -3.8495726 18.0398207 115 12.9341303 -3.8495726 116 -17.7202891 12.9341303 117 -5.8051264 -17.7202891 118 -6.4432565 -5.8051264 119 -12.8298711 -6.4432565 120 12.0585848 -12.8298711 121 -15.3783986 12.0585848 122 -1.2710510 -15.3783986 123 -18.8262682 -1.2710510 124 -0.8071452 -18.8262682 125 4.6407868 -0.8071452 126 -4.0033605 4.6407868 127 -51.7169499 -4.0033605 128 12.2353909 -51.7169499 129 29.1032601 12.2353909 130 -12.1565667 29.1032601 131 -0.6408536 -12.1565667 132 -2.6075777 -0.6408536 133 -4.0352817 -2.6075777 134 -17.1701763 -4.0352817 135 -11.8156767 -17.1701763 136 -17.4596686 -11.8156767 137 -5.4729016 -17.4596686 138 7.2588177 -5.4729016 139 -9.2786044 7.2588177 140 -19.2807244 -9.2786044 141 -21.9051609 -19.2807244 142 -1.7597570 -21.9051609 143 18.6614035 -1.7597570 144 18.3113466 18.6614035 145 63.5715057 18.3113466 146 2.5489619 63.5715057 147 4.2098154 2.5489619 148 -5.9339633 4.2098154 149 -5.5773429 -5.9339633 150 -5.2925407 -5.5773429 151 -5.3162437 -5.2925407 152 -3.6903205 -5.3162437 153 -5.2310169 -3.6903205 154 -15.4810825 -5.2310169 155 9.7673164 -15.4810825 156 -5.2860340 9.7673164 157 -5.2995122 -5.2860340 158 -2.0663396 -5.2995122 159 -6.1757865 -2.0663396 160 -9.8128334 -6.1757865 161 19.2968358 -9.8128334 162 -5.4963657 19.2968358 163 -8.0548741 -5.4963657 164 NA -8.0548741 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 6.0477986 -19.0874702 [2,] 16.5099663 6.0477986 [3,] -3.1052312 16.5099663 [4,] 3.1787681 -3.1052312 [5,] -23.0353580 3.1787681 [6,] 9.6543997 -23.0353580 [7,] -15.5844897 9.6543997 [8,] -12.3599115 -15.5844897 [9,] 0.1276986 -12.3599115 [10,] -8.8571184 0.1276986 [11,] 19.9463152 -8.8571184 [12,] -5.3333362 19.9463152 [13,] -1.1077573 -5.3333362 [14,] 5.7199045 -1.1077573 [15,] 20.7583900 5.7199045 [16,] 2.2848325 20.7583900 [17,] -4.4781482 2.2848325 [18,] 6.4523842 -4.4781482 [19,] 21.0801542 6.4523842 [20,] 7.2620396 21.0801542 [21,] -7.6387463 7.2620396 [22,] 2.4403295 -7.6387463 [23,] 16.4502551 2.4403295 [24,] 22.8042703 16.4502551 [25,] 5.9223089 22.8042703 [26,] 8.5121602 5.9223089 [27,] 19.5009710 8.5121602 [28,] 2.5560399 19.5009710 [29,] 16.9505352 2.5560399 [30,] 16.3324320 16.9505352 [31,] -22.6678740 16.3324320 [32,] 2.9664141 -22.6678740 [33,] 11.7627520 2.9664141 [34,] -21.2724544 11.7627520 [35,] 20.0631545 -21.2724544 [36,] -12.2148240 20.0631545 [37,] -3.2544369 -12.2148240 [38,] -2.6616564 -3.2544369 [39,] 8.8778557 -2.6616564 [40,] 3.5455550 8.8778557 [41,] 0.3039651 3.5455550 [42,] -3.9320485 0.3039651 [43,] -2.3914751 -3.9320485 [44,] -6.8770955 -2.3914751 [45,] -25.9256546 -6.8770955 [46,] -1.7198997 -25.9256546 [47,] -0.3015848 -1.7198997 [48,] -9.0702178 -0.3015848 [49,] 6.6397179 -9.0702178 [50,] 7.7867420 6.6397179 [51,] -4.7133628 7.7867420 [52,] -26.7667137 -4.7133628 [53,] -0.6232750 -26.7667137 [54,] -5.2213854 -0.6232750 [55,] -0.6509440 -5.2213854 [56,] 11.9367761 -0.6509440 [57,] -39.7727962 11.9367761 [58,] 7.9792848 -39.7727962 [59,] 9.4738984 7.9792848 [60,] 2.5539643 9.4738984 [61,] -11.3915746 2.5539643 [62,] 4.2321125 -11.3915746 [63,] -18.5547524 4.2321125 [64,] -22.0622472 -18.5547524 [65,] 2.5690941 -22.0622472 [66,] -1.7938916 2.5690941 [67,] 2.8519883 -1.7938916 [68,] 1.6910520 2.8519883 [69,] 45.1062384 1.6910520 [70,] 12.8655944 45.1062384 [71,] -2.3305007 12.8655944 [72,] 2.1075500 -2.3305007 [73,] -2.9404161 2.1075500 [74,] 23.7186971 -2.9404161 [75,] 27.8537310 23.7186971 [76,] -6.2766172 27.8537310 [77,] 10.2224071 -6.2766172 [78,] 15.9529402 10.2224071 [79,] -8.5159133 15.9529402 [80,] 5.1905758 -8.5159133 [81,] 2.5183953 5.1905758 [82,] -13.8443110 2.5183953 [83,] 7.4775119 -13.8443110 [84,] 6.7056347 7.4775119 [85,] -3.3740467 6.7056347 [86,] -8.4273126 -3.3740467 [87,] -12.8803347 -8.4273126 [88,] -5.3554106 -12.8803347 [89,] 21.0090586 -5.3554106 [90,] -7.0060938 21.0090586 [91,] 7.4045893 -7.0060938 [92,] -14.5273020 7.4045893 [93,] 4.6920303 -14.5273020 [94,] -19.3135353 4.6920303 [95,] -2.1455175 -19.3135353 [96,] -13.3183670 -2.1455175 [97,] 14.5766538 -13.3183670 [98,] 15.1580542 14.5766538 [99,] -21.2915921 15.1580542 [100,] 22.5798219 -21.2915921 [101,] 9.5381209 22.5798219 [102,] -1.3968073 9.5381209 [103,] -15.7537399 -1.3968073 [104,] 14.7933157 -15.7537399 [105,] -13.0693527 14.7933157 [106,] 14.7213539 -13.0693527 [107,] 20.0222834 14.7213539 [108,] -13.9947865 20.0222834 [109,] 16.2557540 -13.9947865 [110,] -15.5121377 16.2557540 [111,] 11.3560401 -15.5121377 [112,] -3.5402876 11.3560401 [113,] 18.0398207 -3.5402876 [114,] -3.8495726 18.0398207 [115,] 12.9341303 -3.8495726 [116,] -17.7202891 12.9341303 [117,] -5.8051264 -17.7202891 [118,] -6.4432565 -5.8051264 [119,] -12.8298711 -6.4432565 [120,] 12.0585848 -12.8298711 [121,] -15.3783986 12.0585848 [122,] -1.2710510 -15.3783986 [123,] -18.8262682 -1.2710510 [124,] -0.8071452 -18.8262682 [125,] 4.6407868 -0.8071452 [126,] -4.0033605 4.6407868 [127,] -51.7169499 -4.0033605 [128,] 12.2353909 -51.7169499 [129,] 29.1032601 12.2353909 [130,] -12.1565667 29.1032601 [131,] -0.6408536 -12.1565667 [132,] -2.6075777 -0.6408536 [133,] -4.0352817 -2.6075777 [134,] -17.1701763 -4.0352817 [135,] -11.8156767 -17.1701763 [136,] -17.4596686 -11.8156767 [137,] -5.4729016 -17.4596686 [138,] 7.2588177 -5.4729016 [139,] -9.2786044 7.2588177 [140,] -19.2807244 -9.2786044 [141,] -21.9051609 -19.2807244 [142,] -1.7597570 -21.9051609 [143,] 18.6614035 -1.7597570 [144,] 18.3113466 18.6614035 [145,] 63.5715057 18.3113466 [146,] 2.5489619 63.5715057 [147,] 4.2098154 2.5489619 [148,] -5.9339633 4.2098154 [149,] -5.5773429 -5.9339633 [150,] -5.2925407 -5.5773429 [151,] -5.3162437 -5.2925407 [152,] -3.6903205 -5.3162437 [153,] -5.2310169 -3.6903205 [154,] -15.4810825 -5.2310169 [155,] 9.7673164 -15.4810825 [156,] -5.2860340 9.7673164 [157,] -5.2995122 -5.2860340 [158,] -2.0663396 -5.2995122 [159,] -6.1757865 -2.0663396 [160,] -9.8128334 -6.1757865 [161,] 19.2968358 -9.8128334 [162,] -5.4963657 19.2968358 [163,] -8.0548741 -5.4963657 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 6.0477986 -19.0874702 2 16.5099663 6.0477986 3 -3.1052312 16.5099663 4 3.1787681 -3.1052312 5 -23.0353580 3.1787681 6 9.6543997 -23.0353580 7 -15.5844897 9.6543997 8 -12.3599115 -15.5844897 9 0.1276986 -12.3599115 10 -8.8571184 0.1276986 11 19.9463152 -8.8571184 12 -5.3333362 19.9463152 13 -1.1077573 -5.3333362 14 5.7199045 -1.1077573 15 20.7583900 5.7199045 16 2.2848325 20.7583900 17 -4.4781482 2.2848325 18 6.4523842 -4.4781482 19 21.0801542 6.4523842 20 7.2620396 21.0801542 21 -7.6387463 7.2620396 22 2.4403295 -7.6387463 23 16.4502551 2.4403295 24 22.8042703 16.4502551 25 5.9223089 22.8042703 26 8.5121602 5.9223089 27 19.5009710 8.5121602 28 2.5560399 19.5009710 29 16.9505352 2.5560399 30 16.3324320 16.9505352 31 -22.6678740 16.3324320 32 2.9664141 -22.6678740 33 11.7627520 2.9664141 34 -21.2724544 11.7627520 35 20.0631545 -21.2724544 36 -12.2148240 20.0631545 37 -3.2544369 -12.2148240 38 -2.6616564 -3.2544369 39 8.8778557 -2.6616564 40 3.5455550 8.8778557 41 0.3039651 3.5455550 42 -3.9320485 0.3039651 43 -2.3914751 -3.9320485 44 -6.8770955 -2.3914751 45 -25.9256546 -6.8770955 46 -1.7198997 -25.9256546 47 -0.3015848 -1.7198997 48 -9.0702178 -0.3015848 49 6.6397179 -9.0702178 50 7.7867420 6.6397179 51 -4.7133628 7.7867420 52 -26.7667137 -4.7133628 53 -0.6232750 -26.7667137 54 -5.2213854 -0.6232750 55 -0.6509440 -5.2213854 56 11.9367761 -0.6509440 57 -39.7727962 11.9367761 58 7.9792848 -39.7727962 59 9.4738984 7.9792848 60 2.5539643 9.4738984 61 -11.3915746 2.5539643 62 4.2321125 -11.3915746 63 -18.5547524 4.2321125 64 -22.0622472 -18.5547524 65 2.5690941 -22.0622472 66 -1.7938916 2.5690941 67 2.8519883 -1.7938916 68 1.6910520 2.8519883 69 45.1062384 1.6910520 70 12.8655944 45.1062384 71 -2.3305007 12.8655944 72 2.1075500 -2.3305007 73 -2.9404161 2.1075500 74 23.7186971 -2.9404161 75 27.8537310 23.7186971 76 -6.2766172 27.8537310 77 10.2224071 -6.2766172 78 15.9529402 10.2224071 79 -8.5159133 15.9529402 80 5.1905758 -8.5159133 81 2.5183953 5.1905758 82 -13.8443110 2.5183953 83 7.4775119 -13.8443110 84 6.7056347 7.4775119 85 -3.3740467 6.7056347 86 -8.4273126 -3.3740467 87 -12.8803347 -8.4273126 88 -5.3554106 -12.8803347 89 21.0090586 -5.3554106 90 -7.0060938 21.0090586 91 7.4045893 -7.0060938 92 -14.5273020 7.4045893 93 4.6920303 -14.5273020 94 -19.3135353 4.6920303 95 -2.1455175 -19.3135353 96 -13.3183670 -2.1455175 97 14.5766538 -13.3183670 98 15.1580542 14.5766538 99 -21.2915921 15.1580542 100 22.5798219 -21.2915921 101 9.5381209 22.5798219 102 -1.3968073 9.5381209 103 -15.7537399 -1.3968073 104 14.7933157 -15.7537399 105 -13.0693527 14.7933157 106 14.7213539 -13.0693527 107 20.0222834 14.7213539 108 -13.9947865 20.0222834 109 16.2557540 -13.9947865 110 -15.5121377 16.2557540 111 11.3560401 -15.5121377 112 -3.5402876 11.3560401 113 18.0398207 -3.5402876 114 -3.8495726 18.0398207 115 12.9341303 -3.8495726 116 -17.7202891 12.9341303 117 -5.8051264 -17.7202891 118 -6.4432565 -5.8051264 119 -12.8298711 -6.4432565 120 12.0585848 -12.8298711 121 -15.3783986 12.0585848 122 -1.2710510 -15.3783986 123 -18.8262682 -1.2710510 124 -0.8071452 -18.8262682 125 4.6407868 -0.8071452 126 -4.0033605 4.6407868 127 -51.7169499 -4.0033605 128 12.2353909 -51.7169499 129 29.1032601 12.2353909 130 -12.1565667 29.1032601 131 -0.6408536 -12.1565667 132 -2.6075777 -0.6408536 133 -4.0352817 -2.6075777 134 -17.1701763 -4.0352817 135 -11.8156767 -17.1701763 136 -17.4596686 -11.8156767 137 -5.4729016 -17.4596686 138 7.2588177 -5.4729016 139 -9.2786044 7.2588177 140 -19.2807244 -9.2786044 141 -21.9051609 -19.2807244 142 -1.7597570 -21.9051609 143 18.6614035 -1.7597570 144 18.3113466 18.6614035 145 63.5715057 18.3113466 146 2.5489619 63.5715057 147 4.2098154 2.5489619 148 -5.9339633 4.2098154 149 -5.5773429 -5.9339633 150 -5.2925407 -5.5773429 151 -5.3162437 -5.2925407 152 -3.6903205 -5.3162437 153 -5.2310169 -3.6903205 154 -15.4810825 -5.2310169 155 9.7673164 -15.4810825 156 -5.2860340 9.7673164 157 -5.2995122 -5.2860340 158 -2.0663396 -5.2995122 159 -6.1757865 -2.0663396 160 -9.8128334 -6.1757865 161 19.2968358 -9.8128334 162 -5.4963657 19.2968358 163 -8.0548741 -5.4963657 > 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/7dr681321896929.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/8zaxt1321896929.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/98bnl1321896929.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/10grbm1321896929.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/11elrs1321896929.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12dn7q1321896929.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/13u8nw1321896929.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$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/14gxsu1321896929.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/15oy3o1321896929.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/1690hi1321896929.tab") + } > > try(system("convert tmp/1txdt1321896929.ps tmp/1txdt1321896929.png",intern=TRUE)) character(0) > try(system("convert tmp/2d54p1321896929.ps tmp/2d54p1321896929.png",intern=TRUE)) character(0) > try(system("convert tmp/36ma41321896929.ps tmp/36ma41321896929.png",intern=TRUE)) character(0) > try(system("convert tmp/4erwf1321896929.ps tmp/4erwf1321896929.png",intern=TRUE)) character(0) > try(system("convert tmp/5d60x1321896929.ps tmp/5d60x1321896929.png",intern=TRUE)) character(0) > try(system("convert tmp/6289v1321896929.ps tmp/6289v1321896929.png",intern=TRUE)) character(0) > try(system("convert tmp/7dr681321896929.ps tmp/7dr681321896929.png",intern=TRUE)) character(0) > try(system("convert tmp/8zaxt1321896929.ps tmp/8zaxt1321896929.png",intern=TRUE)) character(0) > try(system("convert tmp/98bnl1321896929.ps tmp/98bnl1321896929.png",intern=TRUE)) character(0) > try(system("convert tmp/10grbm1321896929.ps tmp/10grbm1321896929.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.865 0.580 6.460