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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+ ,0 + ,5 + ,5 + ,13 + ,13 + ,13 + ,13 + ,6179 + ,6179 + ,1 + ,17547 + ,0 + ,0 + ,1 + ,1 + ,4 + ,4 + ,3 + ,3 + ,3926 + ,3926 + ,1 + ,73567 + ,0 + ,0 + ,23 + ,23 + ,31 + ,31 + ,18 + ,18 + ,23238 + ,23238 + ,1 + ,969 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,101060 + ,2 + ,2 + ,16 + ,16 + ,29 + ,29 + ,48 + ,48 + ,49288 + ,49288) + ,dim=c(12 + ,164) + ,dimnames=list(c('Gender' + ,'Time_in_RFC' + ,'Shared_compendiums' + ,'Shared_compendiums_g' + ,'Reviewed_compendiums' + ,'Reviewed_compendiums_g' + ,'Long_feedback' + ,'Long_feedback_g' + ,'Blogs' + ,'Blogs_g' + ,'Characters' + ,'Characters_g') + ,1:164)) > y <- array(NA,dim=c(12,164),dimnames=list(c('Gender','Time_in_RFC','Shared_compendiums','Shared_compendiums_g','Reviewed_compendiums','Reviewed_compendiums_g','Long_feedback','Long_feedback_g','Blogs','Blogs_g','Characters','Characters_g'),1:164)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par20 = '' > par19 = '' > par18 = '' > par17 = '' > par16 = '' > par15 = '' > par14 = '' > par13 = '' > par12 = '' > par11 = '' > par10 = '' > par9 = '' > par8 = '' > par7 = '' > par6 = '' > par5 = '' > par4 = '' > 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 Time_in_RFC Gender Shared_compendiums Shared_compendiums_g 1 146455 0 1 0 2 84944 0 4 0 3 113337 0 9 0 4 128655 0 2 0 5 74398 0 1 0 6 35523 0 2 0 7 293403 0 0 0 8 32750 0 0 0 9 106539 0 5 0 10 130539 0 0 0 11 154991 0 0 0 12 126683 0 7 0 13 100672 0 6 0 14 179562 0 3 0 15 125971 0 4 0 16 234509 0 0 0 17 158980 0 4 0 18 184217 0 3 0 19 107342 0 0 0 20 141371 0 5 0 21 154730 0 0 0 22 264020 0 1 0 23 90938 0 3 0 24 101324 0 5 0 25 130232 0 0 0 26 137793 0 0 0 27 161678 0 4 0 28 151503 0 0 0 29 105324 0 0 0 30 175914 0 0 0 31 181853 0 3 0 32 114928 0 4 0 33 190410 0 1 0 34 61499 0 4 0 35 223004 0 1 0 36 167131 0 0 0 37 233482 0 0 0 38 121185 0 2 0 39 78776 0 1 0 40 188967 0 2 0 41 199512 0 8 0 42 102531 0 5 0 43 118958 0 3 0 44 68948 0 4 0 45 93125 0 1 0 46 277108 0 2 0 47 78800 0 2 0 48 157250 0 0 0 49 210554 0 6 0 50 127324 0 3 0 51 114397 0 0 0 52 24188 0 0 0 53 246209 0 6 0 54 65029 0 5 0 55 98030 0 3 0 56 173587 0 1 0 57 172684 0 5 0 58 191381 0 5 0 59 191276 0 0 0 60 134043 0 9 0 61 233406 0 6 0 62 195304 0 6 0 63 127619 0 5 0 64 162810 0 6 0 65 129100 0 2 0 66 108715 0 0 0 67 106469 0 3 0 68 142069 0 8 0 69 143937 0 2 0 70 84256 0 5 0 71 118807 0 11 0 72 69471 0 6 0 73 122433 1 5 5 74 131122 1 1 1 75 94763 1 0 0 76 188780 1 3 3 77 191467 1 3 3 78 105615 1 6 6 79 89318 1 1 1 80 107335 1 0 0 81 98599 1 1 1 82 260646 1 0 0 83 131876 1 5 5 84 119291 1 2 2 85 80953 1 0 0 86 99768 1 0 0 87 84572 1 5 5 88 202373 1 1 1 89 166790 1 0 0 90 99946 1 1 1 91 116900 1 1 1 92 142146 1 2 2 93 99246 1 4 4 94 156833 1 1 1 95 175078 1 4 4 96 130533 1 0 0 97 142339 1 2 2 98 176789 1 0 0 99 181379 1 7 7 100 228548 1 7 7 101 142141 1 6 6 102 167845 1 0 0 103 103012 1 0 0 104 43287 1 4 4 105 125366 1 4 4 106 118372 1 0 0 107 135171 1 0 0 108 175568 1 0 0 109 74112 1 0 0 110 88817 1 0 0 111 164767 1 4 4 112 141933 1 0 0 113 22938 1 0 0 114 115199 1 0 0 115 61857 1 4 4 116 91185 1 0 0 117 213765 1 1 1 118 21054 1 0 0 119 167105 1 5 5 120 31414 1 0 0 121 178863 1 1 1 122 126681 1 7 7 123 64320 1 5 5 124 67746 1 2 2 125 38214 1 0 0 126 90961 1 1 1 127 181510 1 0 0 128 116775 1 0 0 129 223914 1 2 2 130 185139 1 0 0 131 242879 1 2 2 132 139144 1 0 0 133 75812 1 0 0 134 178218 1 4 4 135 246834 1 4 4 136 50999 1 8 8 137 223842 1 0 0 138 93577 1 4 4 139 155383 1 0 0 140 111664 1 1 1 141 75426 1 0 0 142 243551 1 9 9 143 136548 1 0 0 144 173260 1 3 3 145 185039 1 7 7 146 67507 1 5 5 147 139350 1 2 2 148 172964 1 1 1 149 0 1 9 9 150 14688 1 0 0 151 98 1 0 0 152 455 1 0 0 153 0 1 1 1 154 0 1 0 0 155 128066 1 2 2 156 176460 1 1 1 157 0 1 0 0 158 203 1 0 0 159 7199 1 0 0 160 46660 1 0 0 161 17547 1 0 0 162 73567 1 0 0 163 969 1 0 0 164 101060 1 2 2 Reviewed_compendiums Reviewed_compendiums_g Long_feedback Long_feedback_g 1 22 0 68 0 2 20 0 72 0 3 24 0 37 0 4 21 0 70 0 5 15 0 30 0 6 16 0 53 0 7 20 0 74 0 8 18 0 22 0 9 19 0 68 0 10 20 0 47 0 11 25 0 87 0 12 37 0 123 0 13 23 0 69 0 14 28 0 89 0 15 25 0 45 0 16 35 0 122 0 17 20 0 75 0 18 22 0 45 0 19 19 0 53 0 20 26 0 96 0 21 27 0 82 0 22 22 0 76 0 23 15 0 51 0 24 26 0 104 0 25 24 0 83 0 26 22 0 78 0 27 21 0 59 0 28 23 0 83 0 29 21 0 71 0 30 25 0 81 0 31 25 0 93 0 32 28 0 72 0 33 30 0 107 0 34 20 0 75 0 35 23 0 84 0 36 25 0 69 0 37 26 0 90 0 38 20 0 51 0 39 8 0 18 0 40 20 0 75 0 41 21 0 59 0 42 25 0 63 0 43 20 0 68 0 44 18 0 47 0 45 21 0 29 0 46 22 0 69 0 47 26 0 66 0 48 30 0 106 0 49 24 0 73 0 50 26 0 87 0 51 18 0 65 0 52 4 0 7 0 53 31 0 111 0 54 18 0 61 0 55 14 0 41 0 56 20 0 70 0 57 30 0 112 0 58 20 0 71 0 59 26 0 90 0 60 20 0 69 0 61 27 0 85 0 62 18 0 47 0 63 27 0 50 0 64 22 0 76 0 65 19 0 60 0 66 15 0 35 0 67 19 0 72 0 68 28 0 88 0 69 20 0 66 0 70 17 0 58 0 71 25 0 81 0 72 20 0 63 0 73 25 25 91 91 74 20 20 50 50 75 22 22 75 75 76 25 25 85 85 77 20 20 75 75 78 23 23 70 70 79 22 22 78 78 80 21 21 61 61 81 18 18 55 55 82 25 25 60 60 83 22 22 83 83 84 25 25 38 38 85 8 8 27 27 86 21 21 62 62 87 22 22 82 82 88 21 21 79 79 89 30 30 59 59 90 23 23 80 80 91 20 20 36 36 92 24 24 88 88 93 21 21 63 63 94 20 20 73 73 95 20 20 71 71 96 20 20 76 76 97 20 20 67 67 98 23 23 66 66 99 33 33 123 123 100 19 19 65 65 101 27 27 87 87 102 25 25 77 77 103 20 20 37 37 104 19 19 64 64 105 15 15 22 22 106 21 21 35 35 107 22 22 61 61 108 24 24 80 80 109 19 19 54 54 110 20 20 60 60 111 23 23 87 87 112 27 27 75 75 113 1 1 0 0 114 20 20 54 54 115 11 11 30 30 116 27 27 66 66 117 22 22 56 56 118 0 0 0 0 119 17 17 32 32 120 8 8 9 9 121 23 23 78 78 122 26 26 90 90 123 20 20 56 56 124 16 16 35 35 125 8 8 21 21 126 22 22 78 78 127 33 33 118 118 128 28 28 83 83 129 26 26 89 89 130 27 27 83 83 131 35 35 124 124 132 21 21 76 76 133 20 20 57 57 134 24 24 91 91 135 26 26 89 89 136 20 20 66 66 137 22 22 82 82 138 24 24 63 63 139 23 23 75 75 140 22 22 59 59 141 12 12 19 19 142 21 21 57 57 143 21 21 62 62 144 21 21 78 78 145 25 25 73 73 146 32 32 112 112 147 24 24 79 79 148 28 28 96 96 149 0 0 0 0 150 0 0 0 0 151 0 0 0 0 152 0 0 0 0 153 0 0 0 0 154 0 0 0 0 155 20 20 48 48 156 27 27 55 55 157 0 0 0 0 158 0 0 0 0 159 0 0 0 0 160 5 5 13 13 161 1 1 4 4 162 23 23 31 31 163 0 0 0 0 164 16 16 29 29 Blogs Blogs_g Characters Characters_g 1 128 0 95556 0 2 89 0 54565 0 3 68 0 63016 0 4 108 0 79774 0 5 51 0 31258 0 6 33 0 52491 0 7 119 0 91256 0 8 5 0 22807 0 9 63 0 77411 0 10 66 0 48821 0 11 98 0 52295 0 12 71 0 63262 0 13 55 0 50466 0 14 116 0 62932 0 15 71 0 38439 0 16 120 0 70817 0 17 122 0 105965 0 18 74 0 73795 0 19 111 0 82043 0 20 103 0 74349 0 21 98 0 82204 0 22 100 0 55709 0 23 42 0 37137 0 24 100 0 70780 0 25 105 0 55027 0 26 77 0 56699 0 27 83 0 65911 0 28 98 0 56316 0 29 46 0 26982 0 30 95 0 54628 0 31 91 0 96750 0 32 91 0 53009 0 33 94 0 64664 0 34 15 0 36990 0 35 137 0 85224 0 36 56 0 37048 0 37 78 0 59635 0 38 68 0 42051 0 39 34 0 26998 0 40 94 0 63717 0 41 82 0 55071 0 42 63 0 40001 0 43 58 0 54506 0 44 43 0 35838 0 45 36 0 50838 0 46 64 0 86997 0 47 21 0 33032 0 48 104 0 61704 0 49 124 0 117986 0 50 101 0 56733 0 51 85 0 55064 0 52 7 0 5950 0 53 124 0 84607 0 54 21 0 32551 0 55 35 0 31701 0 56 95 0 71170 0 57 102 0 101773 0 58 212 0 101653 0 59 141 0 81493 0 60 54 0 55901 0 61 117 0 109104 0 62 145 0 114425 0 63 50 0 36311 0 64 80 0 70027 0 65 87 0 73713 0 66 78 0 40671 0 67 86 0 89041 0 68 82 0 57231 0 69 139 0 78792 0 70 75 0 59155 0 71 70 0 55827 0 72 25 0 22618 0 73 66 66 58425 58425 74 89 89 65724 65724 75 99 99 56979 56979 76 98 98 72369 72369 77 104 104 79194 79194 78 48 48 202316 202316 79 81 81 44970 44970 80 64 64 49319 49319 81 44 44 36252 36252 82 104 104 75741 75741 83 36 36 38417 38417 84 120 120 64102 64102 85 58 58 56622 56622 86 27 27 15430 15430 87 84 84 72571 72571 88 56 56 67271 67271 89 46 46 43460 43460 90 119 119 99501 99501 91 57 57 28340 28340 92 139 139 76013 76013 93 51 51 37361 37361 94 85 85 48204 48204 95 91 91 76168 76168 96 79 79 85168 85168 97 142 142 125410 125410 98 149 149 123328 123328 99 96 96 83038 83038 100 198 198 120087 120087 101 61 61 91939 91939 102 145 145 103646 103646 103 26 26 29467 29467 104 49 49 43750 43750 105 68 68 34497 34497 106 145 145 66477 66477 107 82 82 71181 71181 108 102 102 74482 74482 109 52 52 174949 174949 110 56 56 46765 46765 111 80 80 90257 90257 112 99 99 51370 51370 113 11 11 1168 1168 114 87 87 51360 51360 115 28 28 25162 25162 116 67 67 21067 21067 117 150 150 58233 58233 118 4 4 855 855 119 71 71 85903 85903 120 39 39 14116 14116 121 87 87 57637 57637 122 66 66 94137 94137 123 23 23 62147 62147 124 56 56 62832 62832 125 16 16 8773 8773 126 49 49 63785 63785 127 108 108 65196 65196 128 112 112 73087 73087 129 110 110 72631 72631 130 126 126 86281 86281 131 155 155 162365 162365 132 75 75 56530 56530 133 30 30 35606 35606 134 78 78 70111 70111 135 135 135 92046 92046 136 8 8 63989 63989 137 114 114 104911 104911 138 60 60 43448 43448 139 99 99 60029 60029 140 98 98 38650 38650 141 33 33 47261 47261 142 93 93 73586 73586 143 157 157 83042 83042 144 15 15 37238 37238 145 98 98 63958 63958 146 49 49 78956 78956 147 88 88 99518 99518 148 151 151 111436 111436 149 0 0 0 0 150 5 5 6023 6023 151 0 0 0 0 152 0 0 0 0 153 0 0 0 0 154 0 0 0 0 155 80 80 42564 42564 156 122 122 38885 38885 157 0 0 0 0 158 0 0 0 0 159 6 6 1644 1644 160 13 13 6179 6179 161 3 3 3926 3926 162 18 18 23238 23238 163 0 0 0 0 164 48 48 49288 49288 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Gender Shared_compendiums -1486.6839 8004.3197 -3317.2469 Shared_compendiums_g Reviewed_compendiums Reviewed_compendiums_g 5615.6677 2738.5429 -511.9359 Long_feedback Long_feedback_g Blogs 57.8991 178.5758 405.5396 Blogs_g Characters Characters_g 329.3881 0.8864 -0.9069 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -82632 -25122 -3373 16705 117914 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1486.6839 20795.2130 -0.071 0.94310 Gender 8004.3197 22767.1398 0.352 0.72565 Shared_compendiums -3317.2469 1764.8637 -1.880 0.06208 . Shared_compendiums_g 5615.6677 2491.5730 2.254 0.02563 * Reviewed_compendiums 2738.5429 1501.8420 1.823 0.07020 . Reviewed_compendiums_g -511.9359 1874.4856 -0.273 0.78514 Long_feedback 57.8991 364.6050 0.159 0.87404 Long_feedback_g 178.5758 482.9468 0.370 0.71207 Blogs 405.5396 214.8118 1.888 0.06095 . Blogs_g 329.3881 251.5993 1.309 0.19245 Characters 0.8864 0.3269 2.711 0.00747 ** Characters_g -0.9069 0.3613 -2.510 0.01312 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 37570 on 152 degrees of freedom Multiple R-squared: 0.6781, Adjusted R-squared: 0.6548 F-statistic: 29.1 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,] 0.9946699 0.010660199 0.005330100 [2,] 0.9875269 0.024946272 0.012473136 [3,] 0.9775911 0.044817838 0.022408919 [4,] 0.9712588 0.057482457 0.028741228 [5,] 0.9875530 0.024894026 0.012447013 [6,] 0.9780992 0.043801589 0.021900794 [7,] 0.9730764 0.053847141 0.026923570 [8,] 0.9986518 0.002696450 0.001348225 [9,] 0.9977947 0.004410661 0.002205330 [10,] 0.9981265 0.003746964 0.001873482 [11,] 0.9978350 0.004329992 0.002164996 [12,] 0.9962570 0.007485979 0.003742990 [13,] 0.9952023 0.009595454 0.004797727 [14,] 0.9922275 0.015544997 0.007772499 [15,] 0.9880019 0.023996293 0.011998146 [16,] 0.9824099 0.035180106 0.017590053 [17,] 0.9776691 0.044661807 0.022330903 [18,] 0.9745780 0.050844099 0.025422049 [19,] 0.9654157 0.069168511 0.034584256 [20,] 0.9553413 0.089317445 0.044658722 [21,] 0.9448261 0.110347726 0.055173863 [22,] 0.9446980 0.110603903 0.055301952 [23,] 0.9735575 0.052884901 0.026442451 [24,] 0.9636099 0.072780277 0.036390138 [25,] 0.9554790 0.089041955 0.044520977 [26,] 0.9586969 0.082606210 0.041303105 [27,] 0.9882407 0.023518637 0.011759319 [28,] 0.9836599 0.032680299 0.016340149 [29,] 0.9772557 0.045488514 0.022744257 [30,] 0.9702734 0.059453223 0.029726611 [31,] 0.9633162 0.073367575 0.036683787 [32,] 0.9982348 0.003530498 0.001765249 [33,] 0.9976591 0.004681770 0.002340885 [34,] 0.9969758 0.006048394 0.003024197 [35,] 0.9955584 0.008883159 0.004441580 [36,] 0.9945660 0.010868062 0.005434031 [37,] 0.9929841 0.014031754 0.007015877 [38,] 0.9903496 0.019300756 0.009650378 [39,] 0.9925172 0.014965637 0.007482819 [40,] 0.9895824 0.020835140 0.010417570 [41,] 0.9875614 0.024877115 0.012438558 [42,] 0.9852828 0.029434481 0.014717240 [43,] 0.9820797 0.035840583 0.017920291 [44,] 0.9796322 0.040735552 0.020367776 [45,] 0.9741416 0.051716839 0.025858420 [46,] 0.9726866 0.054626867 0.027313434 [47,] 0.9697163 0.060567366 0.030283683 [48,] 0.9609983 0.078003473 0.039001737 [49,] 0.9527545 0.094491053 0.047245526 [50,] 0.9498423 0.100315326 0.050157663 [51,] 0.9387459 0.122508252 0.061254126 [52,] 0.9226530 0.154694092 0.077347046 [53,] 0.9234443 0.153111417 0.076555708 [54,] 0.9044946 0.191010778 0.095505389 [55,] 0.8916576 0.216684705 0.108342353 [56,] 0.8753607 0.249278686 0.124639343 [57,] 0.8491036 0.301792748 0.150896374 [58,] 0.8190520 0.361896063 0.180948032 [59,] 0.7900970 0.419805962 0.209902981 [60,] 0.7536821 0.492635870 0.246317935 [61,] 0.7470234 0.505953190 0.252976595 [62,] 0.7171878 0.565624333 0.282812166 [63,] 0.6982299 0.603540133 0.301770066 [64,] 0.6579729 0.684054216 0.342027108 [65,] 0.6515571 0.696885805 0.348442902 [66,] 0.6508783 0.698243426 0.349121713 [67,] 0.6110208 0.777958425 0.388979212 [68,] 0.8074522 0.385095576 0.192547788 [69,] 0.7907031 0.418593851 0.209296926 [70,] 0.8782096 0.243580714 0.121790357 [71,] 0.8547566 0.290486747 0.145243373 [72,] 0.8282859 0.343428243 0.171714122 [73,] 0.8637032 0.272593611 0.136296806 [74,] 0.9368320 0.126336024 0.063168012 [75,] 0.9419975 0.116004985 0.058002492 [76,] 0.9667740 0.066451938 0.033225969 [77,] 0.9589767 0.082046538 0.041023269 [78,] 0.9621457 0.075708545 0.037854273 [79,] 0.9521354 0.095729296 0.047864648 [80,] 0.9441717 0.111656673 0.055828337 [81,] 0.9450436 0.109912894 0.054956447 [82,] 0.9303857 0.139228617 0.069614309 [83,] 0.9262513 0.147497380 0.073748690 [84,] 0.9083578 0.183284323 0.091642161 [85,] 0.8898701 0.220259715 0.110129857 [86,] 0.8921792 0.215641535 0.107820768 [87,] 0.8668913 0.266217458 0.133108729 [88,] 0.8480142 0.303971675 0.151985838 [89,] 0.8509478 0.298104484 0.149052242 [90,] 0.9074027 0.185194624 0.092597312 [91,] 0.8927322 0.214535628 0.107267814 [92,] 0.9140505 0.171899020 0.085949510 [93,] 0.8928942 0.214211562 0.107105781 [94,] 0.8752854 0.249429103 0.124714551 [95,] 0.8588206 0.282358750 0.141179375 [96,] 0.8319841 0.336031888 0.168015944 [97,] 0.8027484 0.394503145 0.197251572 [98,] 0.7666034 0.466793282 0.233396641 [99,] 0.7238747 0.552250698 0.276125349 [100,] 0.6814284 0.637143267 0.318571633 [101,] 0.6327253 0.734549433 0.367274716 [102,] 0.6138832 0.772233547 0.386116773 [103,] 0.5857935 0.828413013 0.414206507 [104,] 0.5343646 0.931270749 0.465635375 [105,] 0.5750436 0.849912726 0.424956363 [106,] 0.5380902 0.923819528 0.461909764 [107,] 0.5283129 0.943374128 0.471687064 [108,] 0.4928332 0.985666441 0.507166779 [109,] 0.4565156 0.913031262 0.543484369 [110,] 0.4266965 0.853393079 0.573303460 [111,] 0.3690943 0.738188699 0.630905650 [112,] 0.3256734 0.651346731 0.674326634 [113,] 0.2724160 0.544831926 0.727584037 [114,] 0.3179510 0.635901919 0.682049041 [115,] 0.3473187 0.694637360 0.652681320 [116,] 0.2903477 0.580695450 0.709652275 [117,] 0.2401644 0.480328820 0.759835590 [118,] 0.1963716 0.392743123 0.803628438 [119,] 0.1537922 0.307584447 0.846207776 [120,] 0.1351545 0.270308927 0.864845536 [121,] 0.1528493 0.305698560 0.847150720 [122,] 0.1672643 0.334528651 0.832735675 [123,] 0.2694824 0.538964718 0.730517641 [124,] 0.2607428 0.521485668 0.739257166 [125,] 0.2224135 0.444827091 0.777586454 [126,] 0.1830367 0.366073428 0.816963286 [127,] 0.1372178 0.274435618 0.862782191 [128,] 0.4038118 0.807623571 0.596188214 [129,] 0.3626666 0.725333167 0.637333417 [130,] 0.9768595 0.046281073 0.023140536 [131,] 0.9961152 0.007769581 0.003884791 [132,] 0.9907697 0.018460617 0.009230308 [133,] 0.9823110 0.035377993 0.017688996 [134,] 0.9904008 0.019198315 0.009599157 [135,] 0.9740128 0.051974390 0.025987195 > postscript(file="/var/wessaorg/rcomp/tmp/17yaf1321986445.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/2a7sf1321986445.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3thq91321986445.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4w44k1321986445.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5wr4k1321986445.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 -49539.5158 -43701.3362 -6624.7442 -39298.9965 -12003.9271 -63153.8885 7 8 9 10 11 12 106682.4603 -38575.5155 -25526.4405 4491.2543 -3122.1713 -41928.3008 13 14 15 16 17 18 -11958.9249 6340.4082 6790.6081 21643.4893 -28784.6594 37377.5340 19 20 21 22 23 24 -64013.0622 -24992.7327 -35083.1805 114239.2222 8393.1837 -61122.6160 25 26 27 28 29 30 -30171.5528 -6970.9836 23422.5424 -4465.8489 2617.8044 17296.7867 31 32 33 34 35 36 -3223.5673 -35057.4636 11421.2088 -21730.9866 28853.3575 40608.1687 37 38 39 40 41 42 74060.9394 6730.2453 22909.0537 43373.1108 84540.0377 -12514.6254 43 44 45 46 47 48 -148.9589 -17517.6605 -20923.6109 117914.3744 -25899.3689 -26429.6841 49 50 51 52 53 54 7118.5329 -28726.5959 -20455.1176 6202.1463 50991.9193 -7094.4140 55 56 57 58 59 60 26460.1620 17953.2112 -29464.3755 -25511.0686 -13069.7778 35167.2035 61 62 63 64 65 66 31772.2183 4445.4342 16391.9418 25034.2561 -18908.8982 -587.2627 67 68 69 70 71 72 -52099.1945 4333.4614 -32748.1049 -30437.0605 5755.1562 2254.7609 73 74 75 76 77 78 -20068.2334 1889.2970 -50064.9785 29062.7202 40977.8975 -13585.9963 79 80 81 82 83 84 -45535.3936 -6390.3372 5104.5013 109395.4374 19583.9137 -43351.4815 85 86 87 88 89 90 8773.0274 12303.5516 -62059.7455 88340.2579 46606.6983 -64415.9361 91 92 93 94 95 96 13728.9982 -43812.9854 -14837.1324 24741.8034 32728.3823 5198.3866 97 98 99 100 101 102 -30939.3843 -3523.0783 -12642.2187 5211.9579 -1804.0584 -16985.4237 103 104 105 106 107 108 24708.8146 -64978.5187 21785.4755 -48382.2763 6438.6811 23258.5842 109 110 111 112 113 114 -22109.3721 -16618.2086 20327.0916 -14143.0370 6133.5046 -11505.8888 115 116 117 118 119 120 -5503.2200 -39866.5001 33676.0255 11614.1868 53257.4920 -23417.4691 121 122 123 124 125 126 37633.1953 -21674.8609 -27093.3597 -27138.2647 -2661.3990 -19988.8676 127 128 129 130 131 132 -4424.9175 -52528.1526 54508.8748 8044.0357 13926.2202 13935.2110 133 134 135 136 137 138 -10034.5011 31662.3066 54856.9839 -38612.6810 67317.7233 -33675.4765 139 140 141 142 143 144 8390.9609 -31319.7466 14412.6253 89270.5152 -45070.5317 84383.0407 145 146 147 148 149 150 18793.2511 -82631.6538 -6517.3812 -29587.5044 -27203.4227 4619.2393 151 152 153 154 155 156 -6419.6358 -6062.6358 -8816.0566 -6517.6358 3147.2333 5655.6722 157 158 159 160 161 162 -6517.6358 -6314.6358 -3694.4886 16507.8087 5732.5851 -4245.4735 163 164 -5548.6358 13196.2594 > postscript(file="/var/wessaorg/rcomp/tmp/6e3ix1321986445.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 -49539.5158 NA 1 -43701.3362 -49539.5158 2 -6624.7442 -43701.3362 3 -39298.9965 -6624.7442 4 -12003.9271 -39298.9965 5 -63153.8885 -12003.9271 6 106682.4603 -63153.8885 7 -38575.5155 106682.4603 8 -25526.4405 -38575.5155 9 4491.2543 -25526.4405 10 -3122.1713 4491.2543 11 -41928.3008 -3122.1713 12 -11958.9249 -41928.3008 13 6340.4082 -11958.9249 14 6790.6081 6340.4082 15 21643.4893 6790.6081 16 -28784.6594 21643.4893 17 37377.5340 -28784.6594 18 -64013.0622 37377.5340 19 -24992.7327 -64013.0622 20 -35083.1805 -24992.7327 21 114239.2222 -35083.1805 22 8393.1837 114239.2222 23 -61122.6160 8393.1837 24 -30171.5528 -61122.6160 25 -6970.9836 -30171.5528 26 23422.5424 -6970.9836 27 -4465.8489 23422.5424 28 2617.8044 -4465.8489 29 17296.7867 2617.8044 30 -3223.5673 17296.7867 31 -35057.4636 -3223.5673 32 11421.2088 -35057.4636 33 -21730.9866 11421.2088 34 28853.3575 -21730.9866 35 40608.1687 28853.3575 36 74060.9394 40608.1687 37 6730.2453 74060.9394 38 22909.0537 6730.2453 39 43373.1108 22909.0537 40 84540.0377 43373.1108 41 -12514.6254 84540.0377 42 -148.9589 -12514.6254 43 -17517.6605 -148.9589 44 -20923.6109 -17517.6605 45 117914.3744 -20923.6109 46 -25899.3689 117914.3744 47 -26429.6841 -25899.3689 48 7118.5329 -26429.6841 49 -28726.5959 7118.5329 50 -20455.1176 -28726.5959 51 6202.1463 -20455.1176 52 50991.9193 6202.1463 53 -7094.4140 50991.9193 54 26460.1620 -7094.4140 55 17953.2112 26460.1620 56 -29464.3755 17953.2112 57 -25511.0686 -29464.3755 58 -13069.7778 -25511.0686 59 35167.2035 -13069.7778 60 31772.2183 35167.2035 61 4445.4342 31772.2183 62 16391.9418 4445.4342 63 25034.2561 16391.9418 64 -18908.8982 25034.2561 65 -587.2627 -18908.8982 66 -52099.1945 -587.2627 67 4333.4614 -52099.1945 68 -32748.1049 4333.4614 69 -30437.0605 -32748.1049 70 5755.1562 -30437.0605 71 2254.7609 5755.1562 72 -20068.2334 2254.7609 73 1889.2970 -20068.2334 74 -50064.9785 1889.2970 75 29062.7202 -50064.9785 76 40977.8975 29062.7202 77 -13585.9963 40977.8975 78 -45535.3936 -13585.9963 79 -6390.3372 -45535.3936 80 5104.5013 -6390.3372 81 109395.4374 5104.5013 82 19583.9137 109395.4374 83 -43351.4815 19583.9137 84 8773.0274 -43351.4815 85 12303.5516 8773.0274 86 -62059.7455 12303.5516 87 88340.2579 -62059.7455 88 46606.6983 88340.2579 89 -64415.9361 46606.6983 90 13728.9982 -64415.9361 91 -43812.9854 13728.9982 92 -14837.1324 -43812.9854 93 24741.8034 -14837.1324 94 32728.3823 24741.8034 95 5198.3866 32728.3823 96 -30939.3843 5198.3866 97 -3523.0783 -30939.3843 98 -12642.2187 -3523.0783 99 5211.9579 -12642.2187 100 -1804.0584 5211.9579 101 -16985.4237 -1804.0584 102 24708.8146 -16985.4237 103 -64978.5187 24708.8146 104 21785.4755 -64978.5187 105 -48382.2763 21785.4755 106 6438.6811 -48382.2763 107 23258.5842 6438.6811 108 -22109.3721 23258.5842 109 -16618.2086 -22109.3721 110 20327.0916 -16618.2086 111 -14143.0370 20327.0916 112 6133.5046 -14143.0370 113 -11505.8888 6133.5046 114 -5503.2200 -11505.8888 115 -39866.5001 -5503.2200 116 33676.0255 -39866.5001 117 11614.1868 33676.0255 118 53257.4920 11614.1868 119 -23417.4691 53257.4920 120 37633.1953 -23417.4691 121 -21674.8609 37633.1953 122 -27093.3597 -21674.8609 123 -27138.2647 -27093.3597 124 -2661.3990 -27138.2647 125 -19988.8676 -2661.3990 126 -4424.9175 -19988.8676 127 -52528.1526 -4424.9175 128 54508.8748 -52528.1526 129 8044.0357 54508.8748 130 13926.2202 8044.0357 131 13935.2110 13926.2202 132 -10034.5011 13935.2110 133 31662.3066 -10034.5011 134 54856.9839 31662.3066 135 -38612.6810 54856.9839 136 67317.7233 -38612.6810 137 -33675.4765 67317.7233 138 8390.9609 -33675.4765 139 -31319.7466 8390.9609 140 14412.6253 -31319.7466 141 89270.5152 14412.6253 142 -45070.5317 89270.5152 143 84383.0407 -45070.5317 144 18793.2511 84383.0407 145 -82631.6538 18793.2511 146 -6517.3812 -82631.6538 147 -29587.5044 -6517.3812 148 -27203.4227 -29587.5044 149 4619.2393 -27203.4227 150 -6419.6358 4619.2393 151 -6062.6358 -6419.6358 152 -8816.0566 -6062.6358 153 -6517.6358 -8816.0566 154 3147.2333 -6517.6358 155 5655.6722 3147.2333 156 -6517.6358 5655.6722 157 -6314.6358 -6517.6358 158 -3694.4886 -6314.6358 159 16507.8087 -3694.4886 160 5732.5851 16507.8087 161 -4245.4735 5732.5851 162 -5548.6358 -4245.4735 163 13196.2594 -5548.6358 164 NA 13196.2594 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -43701.3362 -49539.5158 [2,] -6624.7442 -43701.3362 [3,] -39298.9965 -6624.7442 [4,] -12003.9271 -39298.9965 [5,] -63153.8885 -12003.9271 [6,] 106682.4603 -63153.8885 [7,] -38575.5155 106682.4603 [8,] -25526.4405 -38575.5155 [9,] 4491.2543 -25526.4405 [10,] -3122.1713 4491.2543 [11,] -41928.3008 -3122.1713 [12,] -11958.9249 -41928.3008 [13,] 6340.4082 -11958.9249 [14,] 6790.6081 6340.4082 [15,] 21643.4893 6790.6081 [16,] -28784.6594 21643.4893 [17,] 37377.5340 -28784.6594 [18,] -64013.0622 37377.5340 [19,] -24992.7327 -64013.0622 [20,] -35083.1805 -24992.7327 [21,] 114239.2222 -35083.1805 [22,] 8393.1837 114239.2222 [23,] -61122.6160 8393.1837 [24,] -30171.5528 -61122.6160 [25,] -6970.9836 -30171.5528 [26,] 23422.5424 -6970.9836 [27,] -4465.8489 23422.5424 [28,] 2617.8044 -4465.8489 [29,] 17296.7867 2617.8044 [30,] -3223.5673 17296.7867 [31,] -35057.4636 -3223.5673 [32,] 11421.2088 -35057.4636 [33,] -21730.9866 11421.2088 [34,] 28853.3575 -21730.9866 [35,] 40608.1687 28853.3575 [36,] 74060.9394 40608.1687 [37,] 6730.2453 74060.9394 [38,] 22909.0537 6730.2453 [39,] 43373.1108 22909.0537 [40,] 84540.0377 43373.1108 [41,] -12514.6254 84540.0377 [42,] -148.9589 -12514.6254 [43,] -17517.6605 -148.9589 [44,] -20923.6109 -17517.6605 [45,] 117914.3744 -20923.6109 [46,] -25899.3689 117914.3744 [47,] -26429.6841 -25899.3689 [48,] 7118.5329 -26429.6841 [49,] -28726.5959 7118.5329 [50,] -20455.1176 -28726.5959 [51,] 6202.1463 -20455.1176 [52,] 50991.9193 6202.1463 [53,] -7094.4140 50991.9193 [54,] 26460.1620 -7094.4140 [55,] 17953.2112 26460.1620 [56,] -29464.3755 17953.2112 [57,] -25511.0686 -29464.3755 [58,] -13069.7778 -25511.0686 [59,] 35167.2035 -13069.7778 [60,] 31772.2183 35167.2035 [61,] 4445.4342 31772.2183 [62,] 16391.9418 4445.4342 [63,] 25034.2561 16391.9418 [64,] -18908.8982 25034.2561 [65,] -587.2627 -18908.8982 [66,] -52099.1945 -587.2627 [67,] 4333.4614 -52099.1945 [68,] -32748.1049 4333.4614 [69,] -30437.0605 -32748.1049 [70,] 5755.1562 -30437.0605 [71,] 2254.7609 5755.1562 [72,] -20068.2334 2254.7609 [73,] 1889.2970 -20068.2334 [74,] -50064.9785 1889.2970 [75,] 29062.7202 -50064.9785 [76,] 40977.8975 29062.7202 [77,] -13585.9963 40977.8975 [78,] -45535.3936 -13585.9963 [79,] -6390.3372 -45535.3936 [80,] 5104.5013 -6390.3372 [81,] 109395.4374 5104.5013 [82,] 19583.9137 109395.4374 [83,] -43351.4815 19583.9137 [84,] 8773.0274 -43351.4815 [85,] 12303.5516 8773.0274 [86,] -62059.7455 12303.5516 [87,] 88340.2579 -62059.7455 [88,] 46606.6983 88340.2579 [89,] -64415.9361 46606.6983 [90,] 13728.9982 -64415.9361 [91,] -43812.9854 13728.9982 [92,] -14837.1324 -43812.9854 [93,] 24741.8034 -14837.1324 [94,] 32728.3823 24741.8034 [95,] 5198.3866 32728.3823 [96,] -30939.3843 5198.3866 [97,] -3523.0783 -30939.3843 [98,] -12642.2187 -3523.0783 [99,] 5211.9579 -12642.2187 [100,] -1804.0584 5211.9579 [101,] -16985.4237 -1804.0584 [102,] 24708.8146 -16985.4237 [103,] -64978.5187 24708.8146 [104,] 21785.4755 -64978.5187 [105,] -48382.2763 21785.4755 [106,] 6438.6811 -48382.2763 [107,] 23258.5842 6438.6811 [108,] -22109.3721 23258.5842 [109,] -16618.2086 -22109.3721 [110,] 20327.0916 -16618.2086 [111,] -14143.0370 20327.0916 [112,] 6133.5046 -14143.0370 [113,] -11505.8888 6133.5046 [114,] -5503.2200 -11505.8888 [115,] -39866.5001 -5503.2200 [116,] 33676.0255 -39866.5001 [117,] 11614.1868 33676.0255 [118,] 53257.4920 11614.1868 [119,] -23417.4691 53257.4920 [120,] 37633.1953 -23417.4691 [121,] -21674.8609 37633.1953 [122,] -27093.3597 -21674.8609 [123,] -27138.2647 -27093.3597 [124,] -2661.3990 -27138.2647 [125,] -19988.8676 -2661.3990 [126,] -4424.9175 -19988.8676 [127,] -52528.1526 -4424.9175 [128,] 54508.8748 -52528.1526 [129,] 8044.0357 54508.8748 [130,] 13926.2202 8044.0357 [131,] 13935.2110 13926.2202 [132,] -10034.5011 13935.2110 [133,] 31662.3066 -10034.5011 [134,] 54856.9839 31662.3066 [135,] -38612.6810 54856.9839 [136,] 67317.7233 -38612.6810 [137,] -33675.4765 67317.7233 [138,] 8390.9609 -33675.4765 [139,] -31319.7466 8390.9609 [140,] 14412.6253 -31319.7466 [141,] 89270.5152 14412.6253 [142,] -45070.5317 89270.5152 [143,] 84383.0407 -45070.5317 [144,] 18793.2511 84383.0407 [145,] -82631.6538 18793.2511 [146,] -6517.3812 -82631.6538 [147,] -29587.5044 -6517.3812 [148,] -27203.4227 -29587.5044 [149,] 4619.2393 -27203.4227 [150,] -6419.6358 4619.2393 [151,] -6062.6358 -6419.6358 [152,] -8816.0566 -6062.6358 [153,] -6517.6358 -8816.0566 [154,] 3147.2333 -6517.6358 [155,] 5655.6722 3147.2333 [156,] -6517.6358 5655.6722 [157,] -6314.6358 -6517.6358 [158,] -3694.4886 -6314.6358 [159,] 16507.8087 -3694.4886 [160,] 5732.5851 16507.8087 [161,] -4245.4735 5732.5851 [162,] -5548.6358 -4245.4735 [163,] 13196.2594 -5548.6358 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -43701.3362 -49539.5158 2 -6624.7442 -43701.3362 3 -39298.9965 -6624.7442 4 -12003.9271 -39298.9965 5 -63153.8885 -12003.9271 6 106682.4603 -63153.8885 7 -38575.5155 106682.4603 8 -25526.4405 -38575.5155 9 4491.2543 -25526.4405 10 -3122.1713 4491.2543 11 -41928.3008 -3122.1713 12 -11958.9249 -41928.3008 13 6340.4082 -11958.9249 14 6790.6081 6340.4082 15 21643.4893 6790.6081 16 -28784.6594 21643.4893 17 37377.5340 -28784.6594 18 -64013.0622 37377.5340 19 -24992.7327 -64013.0622 20 -35083.1805 -24992.7327 21 114239.2222 -35083.1805 22 8393.1837 114239.2222 23 -61122.6160 8393.1837 24 -30171.5528 -61122.6160 25 -6970.9836 -30171.5528 26 23422.5424 -6970.9836 27 -4465.8489 23422.5424 28 2617.8044 -4465.8489 29 17296.7867 2617.8044 30 -3223.5673 17296.7867 31 -35057.4636 -3223.5673 32 11421.2088 -35057.4636 33 -21730.9866 11421.2088 34 28853.3575 -21730.9866 35 40608.1687 28853.3575 36 74060.9394 40608.1687 37 6730.2453 74060.9394 38 22909.0537 6730.2453 39 43373.1108 22909.0537 40 84540.0377 43373.1108 41 -12514.6254 84540.0377 42 -148.9589 -12514.6254 43 -17517.6605 -148.9589 44 -20923.6109 -17517.6605 45 117914.3744 -20923.6109 46 -25899.3689 117914.3744 47 -26429.6841 -25899.3689 48 7118.5329 -26429.6841 49 -28726.5959 7118.5329 50 -20455.1176 -28726.5959 51 6202.1463 -20455.1176 52 50991.9193 6202.1463 53 -7094.4140 50991.9193 54 26460.1620 -7094.4140 55 17953.2112 26460.1620 56 -29464.3755 17953.2112 57 -25511.0686 -29464.3755 58 -13069.7778 -25511.0686 59 35167.2035 -13069.7778 60 31772.2183 35167.2035 61 4445.4342 31772.2183 62 16391.9418 4445.4342 63 25034.2561 16391.9418 64 -18908.8982 25034.2561 65 -587.2627 -18908.8982 66 -52099.1945 -587.2627 67 4333.4614 -52099.1945 68 -32748.1049 4333.4614 69 -30437.0605 -32748.1049 70 5755.1562 -30437.0605 71 2254.7609 5755.1562 72 -20068.2334 2254.7609 73 1889.2970 -20068.2334 74 -50064.9785 1889.2970 75 29062.7202 -50064.9785 76 40977.8975 29062.7202 77 -13585.9963 40977.8975 78 -45535.3936 -13585.9963 79 -6390.3372 -45535.3936 80 5104.5013 -6390.3372 81 109395.4374 5104.5013 82 19583.9137 109395.4374 83 -43351.4815 19583.9137 84 8773.0274 -43351.4815 85 12303.5516 8773.0274 86 -62059.7455 12303.5516 87 88340.2579 -62059.7455 88 46606.6983 88340.2579 89 -64415.9361 46606.6983 90 13728.9982 -64415.9361 91 -43812.9854 13728.9982 92 -14837.1324 -43812.9854 93 24741.8034 -14837.1324 94 32728.3823 24741.8034 95 5198.3866 32728.3823 96 -30939.3843 5198.3866 97 -3523.0783 -30939.3843 98 -12642.2187 -3523.0783 99 5211.9579 -12642.2187 100 -1804.0584 5211.9579 101 -16985.4237 -1804.0584 102 24708.8146 -16985.4237 103 -64978.5187 24708.8146 104 21785.4755 -64978.5187 105 -48382.2763 21785.4755 106 6438.6811 -48382.2763 107 23258.5842 6438.6811 108 -22109.3721 23258.5842 109 -16618.2086 -22109.3721 110 20327.0916 -16618.2086 111 -14143.0370 20327.0916 112 6133.5046 -14143.0370 113 -11505.8888 6133.5046 114 -5503.2200 -11505.8888 115 -39866.5001 -5503.2200 116 33676.0255 -39866.5001 117 11614.1868 33676.0255 118 53257.4920 11614.1868 119 -23417.4691 53257.4920 120 37633.1953 -23417.4691 121 -21674.8609 37633.1953 122 -27093.3597 -21674.8609 123 -27138.2647 -27093.3597 124 -2661.3990 -27138.2647 125 -19988.8676 -2661.3990 126 -4424.9175 -19988.8676 127 -52528.1526 -4424.9175 128 54508.8748 -52528.1526 129 8044.0357 54508.8748 130 13926.2202 8044.0357 131 13935.2110 13926.2202 132 -10034.5011 13935.2110 133 31662.3066 -10034.5011 134 54856.9839 31662.3066 135 -38612.6810 54856.9839 136 67317.7233 -38612.6810 137 -33675.4765 67317.7233 138 8390.9609 -33675.4765 139 -31319.7466 8390.9609 140 14412.6253 -31319.7466 141 89270.5152 14412.6253 142 -45070.5317 89270.5152 143 84383.0407 -45070.5317 144 18793.2511 84383.0407 145 -82631.6538 18793.2511 146 -6517.3812 -82631.6538 147 -29587.5044 -6517.3812 148 -27203.4227 -29587.5044 149 4619.2393 -27203.4227 150 -6419.6358 4619.2393 151 -6062.6358 -6419.6358 152 -8816.0566 -6062.6358 153 -6517.6358 -8816.0566 154 3147.2333 -6517.6358 155 5655.6722 3147.2333 156 -6517.6358 5655.6722 157 -6314.6358 -6517.6358 158 -3694.4886 -6314.6358 159 16507.8087 -3694.4886 160 5732.5851 16507.8087 161 -4245.4735 5732.5851 162 -5548.6358 -4245.4735 163 13196.2594 -5548.6358 > 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/7cp131321986445.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/8pkox1321986445.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/9izdt1321986445.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/10659z1321986445.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/11mz8i1321986445.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/123c201321986445.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/1347kj1321986445.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$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/14a3gn1321986445.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/15siqi1321986446.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/162fgl1321986446.tab") + } > > try(system("convert tmp/17yaf1321986445.ps tmp/17yaf1321986445.png",intern=TRUE)) character(0) > try(system("convert tmp/2a7sf1321986445.ps tmp/2a7sf1321986445.png",intern=TRUE)) character(0) > try(system("convert tmp/3thq91321986445.ps tmp/3thq91321986445.png",intern=TRUE)) character(0) > try(system("convert tmp/4w44k1321986445.ps tmp/4w44k1321986445.png",intern=TRUE)) character(0) > try(system("convert tmp/5wr4k1321986445.ps tmp/5wr4k1321986445.png",intern=TRUE)) character(0) > try(system("convert tmp/6e3ix1321986445.ps tmp/6e3ix1321986445.png",intern=TRUE)) character(0) > try(system("convert tmp/7cp131321986445.ps tmp/7cp131321986445.png",intern=TRUE)) character(0) > try(system("convert tmp/8pkox1321986445.ps tmp/8pkox1321986445.png",intern=TRUE)) character(0) > try(system("convert tmp/9izdt1321986445.ps tmp/9izdt1321986445.png",intern=TRUE)) character(0) > try(system("convert tmp/10659z1321986445.ps tmp/10659z1321986445.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.661 0.536 6.295